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path planning</title> <meta name="description" content="Search results for: automatic path planning"> <meta name="keywords" content="automatic path planning"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="automatic path planning"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5329</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: automatic path planning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5329</span> Path Planning for Orchard Robot Using Occupancy Grid Map in 2D Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Satyam%20Raikwar">Satyam Raikwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Herlitzius"> Thomas Herlitzius</a>, <a href="https://publications.waset.org/abstracts/search?q=Jens%20Fehrmann"> Jens Fehrmann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the autonomous navigation of orchard and field robots is an emerging technology of the mobile robotics in agriculture. One of the core aspects of autonomous navigation builds upon path planning, which is still a crucial issue. Generally, for simple representation, the path planning for a mobile robot is performed in a two-dimensional space, which creates a path between the start and goal point. This paper presents the automatic path planning approach for robots used in orchards and vineyards using occupancy grid maps with field consideration. The orchards and vineyards are usually structured environment and their topology is assumed to be constant over time; therefore, in this approach, an RGB image of a field is used as a working environment. These images undergone different image processing operations and then discretized into two-dimensional grid matrices. The individual grid or cell of these grid matrices represents the occupancy of the space, whether it is free or occupied. The grid matrix represents the robot workspace for motion and path planning. After the grid matrix is described, a probabilistic roadmap (PRM) path algorithm is used to create the obstacle-free path over these occupancy grids. The path created by this method was successfully verified in the test area. Furthermore, this approach is used in the navigation of the orchard robot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=orchard%20robots" title="orchard robots">orchard robots</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20path%20planning" title=" automatic path planning"> automatic path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=occupancy%20grid" title=" occupancy grid"> occupancy grid</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20roadmap" title=" probabilistic roadmap"> probabilistic roadmap</a> </p> <a href="https://publications.waset.org/abstracts/110023/path-planning-for-orchard-robot-using-occupancy-grid-map-in-2d-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110023.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5328</span> A Review on Comparative Analysis of Path Planning and Collision Avoidance Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divya%20Agarwal">Divya Agarwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Pushpendra%20S.%20Bharti"> Pushpendra S. Bharti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20mobile%20robots" title=" autonomous mobile robots"> autonomous mobile robots</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a> </p> <a href="https://publications.waset.org/abstracts/93693/a-review-on-comparative-analysis-of-path-planning-and-collision-avoidance-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93693.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5327</span> Joint Path and Push Planning among Moveable Obstacles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victor%20Emeli">Victor Emeli</a>, <a href="https://publications.waset.org/abstracts/search?q=Akansel%20Cosgun"> Akansel Cosgun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the navigation among movable obstacles (NAMO) problem and proposes joint path and push planning: which path to take and in what direction the obstacles should be pushed at, given a start and goal position. We present a planning algorithm for selecting a path and the obstacles to be pushed, where a rapidly-exploring random tree (RRT)-based heuristic is employed to calculate a minimal collision path. When it is necessary to apply a pushing force to slide an obstacle out of the way, the planners leverage means-end analysis through a dynamic physics simulation to determine the sequence of linear pushes to clear the necessary space. Simulation experiments show that our approach finds solutions in higher clutter percentages (up to 49%) compared to the straight-line push planner (37%) and RRT without pushing (18%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20planning" title="motion planning">motion planning</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=push%20planning" title=" push planning"> push planning</a>, <a href="https://publications.waset.org/abstracts/search?q=robot%20navigation" title=" robot navigation"> robot navigation</a> </p> <a href="https://publications.waset.org/abstracts/128403/joint-path-and-push-planning-among-moveable-obstacles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5326</span> Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lana%20Dalawr%20Jalal">Lana Dalawr Jalal </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of ofﬂine path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title="obstacle avoidance">obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=three-dimensional%20path%20planning%20unmanned%20aerial%20vehicles" title=" three-dimensional path planning unmanned aerial vehicles"> three-dimensional path planning unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/26160/three-dimensional-off-line-path-planning-for-unmanned-aerial-vehicle-using-modified-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26160.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5325</span> Construction Project Planning Using Fuzzy Critical Path Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20M.%20Aldenali">Omar M. Aldenali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Planning is one of the most important phases of the management science and network planning, which represents the project activities relationship. Critical path is one of the project management techniques used to plan and control the execution of a project activities. The objective of this paper is to implement a fuzzy logic approach to arrange network planning on construction projects. This method is used to finding out critical path in the fuzzy construction project network. The trapezoidal fuzzy numbers are used to represent the activity construction project times. A numerical example that represents a house construction project is introduced. The critical path method is implemented on the fuzzy construction network activities, and the results showed that this method significantly affects the completion time of the construction projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20project" title="construction project">construction project</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20path" title=" critical path"> critical path</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20network%20project" title=" fuzzy network project"> fuzzy network project</a>, <a href="https://publications.waset.org/abstracts/search?q=planning" title=" planning"> planning</a> </p> <a href="https://publications.waset.org/abstracts/111693/construction-project-planning-using-fuzzy-critical-path-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111693.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5324</span> Optimization of Robot Motion Planning Using Biogeography Based Optimization (Bbo)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaber%20Nikpouri">Jaber Nikpouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Arsalan%20Amralizadeh"> Arsalan Amralizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In robotics manipulators, the trajectory should be optimum, thus the torque of the robot can be minimized in order to save power. This paper includes an optimal path planning scheme for a robotic manipulator. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications. In this paper, the improved BBO algorithm is used to minimize the objective function in the presence of different obstacles. The simulation represents that the proposed optimal path planning method has satisfactory performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biogeography-based%20optimization" title="biogeography-based optimization">biogeography-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20detection" title=" obstacle detection"> obstacle detection</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20manipulator" title=" robotic manipulator"> robotic manipulator</a> </p> <a href="https://publications.waset.org/abstracts/55588/optimization-of-robot-motion-planning-using-biogeography-based-optimization-bbo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5323</span> Application of Rapidly Exploring Random Tree Star-Smart and G2 Quintic Pythagorean Hodograph Curves to the UAV Path Planning Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luiz%20G.%20V%C3%A9ras">Luiz G. Véras</a>, <a href="https://publications.waset.org/abstracts/search?q=Felipe%20L.%20Medeiros"> Felipe L. Medeiros</a>, <a href="https://publications.waset.org/abstracts/search?q=Lamartine%20F.%20Guimar%C3%A3es"> Lamartine F. Guimarães</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work approaches the automatic planning of paths for Unmanned Aerial Vehicles (UAVs) through the application of the Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm. RRT*-Smart is a sampling process of positions of a navigation environment through a tree-type graph. The algorithm consists of randomly expanding a tree from an initial position (root node) until one of its branches reaches the final position of the path to be planned. The algorithm ensures the planning of the shortest path, considering the number of iterations tending to infinity. When a new node is inserted into the tree, each neighbor node of the new node is connected to it, if and only if the extension of the path between the root node and that neighbor node, with this new connection, is less than the current extension of the path between those two nodes. RRT*-smart uses an intelligent sampling strategy to plan less extensive routes by spending a smaller number of iterations. This strategy is based on the creation of samples/nodes near to the convex vertices of the navigation environment obstacles. The planned paths are smoothed through the application of the method called quintic pythagorean hodograph curves. The smoothing process converts a route into a dynamically-viable one based on the kinematic constraints of the vehicle. This smoothing method models the hodograph components of a curve with polynomials that obey the Pythagorean Theorem. Its advantage is that the obtained structure allows computation of the curve length in an exact way, without the need for quadratural techniques for the resolution of integrals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20smoothing" title=" path smoothing"> path smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=Pythagorean%20hodograph%20curve" title=" Pythagorean hodograph curve"> Pythagorean hodograph curve</a>, <a href="https://publications.waset.org/abstracts/search?q=RRT%2A-Smart" title=" RRT*-Smart"> RRT*-Smart</a> </p> <a href="https://publications.waset.org/abstracts/92814/application-of-rapidly-exploring-random-tree-star-smart-and-g2-quintic-pythagorean-hodograph-curves-to-the-uav-path-planning-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5322</span> Application of Heuristic Integration Ant Colony Optimization in Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeyu%20Zhang">Zeyu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guisheng%20Yin"> Guisheng Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziying%20Zhang"> Ziying Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Liguo%20Zhang"> Liguo Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20integration" title=" heuristic integration"> heuristic integration</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20formula" title=" probability formula"> probability formula</a> </p> <a href="https://publications.waset.org/abstracts/115269/application-of-heuristic-integration-ant-colony-optimization-in-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5321</span> Three-Dimensional Optimal Path Planning of a Flying Robot for Terrain Following/Terrain Avoidance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amirreza%20Kosari">Amirreza Kosari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Maghsoudi"> Hossein Maghsoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Malahat%20Givar"> Malahat Givar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the three-dimensional optimal path planning of a flying robot for Terrain Following / Terrain Avoidance (TF/TA) purposes using Direct Collocation has been investigated. To this purpose, firstly, the appropriate equations of motion representing the flying robot translational movement have been described. The three-dimensional optimal path planning of the flying vehicle in terrain following/terrain avoidance maneuver is formulated as an optimal control problem. The terrain profile, as the main allowable height constraint has been modeled using Fractal Generation Method. The resulting optimal control problem is discretized by applying Direct Collocation numerical technique, and then transformed into a Nonlinear Programming Problem (NLP). The efficacy of the proposed method is demonstrated by extensive simulations, and in particular, it is verified that this approach could produce a solution satisfying almost all performance and environmental constraints encountering a low-level flying maneuver <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=terrain%20following" title=" terrain following"> terrain following</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20programming" title=" nonlinear programming"> nonlinear programming</a> </p> <a href="https://publications.waset.org/abstracts/98941/three-dimensional-optimal-path-planning-of-a-flying-robot-for-terrain-followingterrain-avoidance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98941.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5320</span> Intelligent Algorithm-Based Tool-Path Planning and Optimization for Additive Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Efrain%20Rodriguez">Efrain Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Pertuz"> Sergio Pertuz</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristhian%20Riano"> Cristhian Riano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tool-path generation is an essential step in the FFF (Fused Filament Fabrication)-based Additive Manufacturing (AM) process planning. In the manufacture of a mechanical part by using additive processes, high resource consumption and prolonged production times are inherent drawbacks of these processes mainly due to non-optimized tool-path generation. In this work, we propose a heuristic-search intelligent algorithm-based approach for optimized tool-path generation for FFF-based AM. The main benefit of this approach is a significant reduction of travels without material deposition when the AM machine performs moves without any extrusion. The optimization method used reduces the number of travels without extrusion in comparison with commercial software as Slic3r or Cura Engine, which means a reduction of production time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20manufacturing" title="additive manufacturing">additive manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=tool-path%20optimization" title=" tool-path optimization"> tool-path optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=fused%20filament%20fabrication" title=" fused filament fabrication"> fused filament fabrication</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title=" process planning"> process planning</a> </p> <a href="https://publications.waset.org/abstracts/83494/intelligent-algorithm-based-tool-path-planning-and-optimization-for-additive-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83494.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5319</span> Path Planning for Collision Detection between two Polyhedra</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khouil">M. Khouil</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Saber"> N. Saber</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mestari"> M. Mestari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20detection" title=" collision detection"> collision detection</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20polyhedron" title=" convex polyhedron"> convex polyhedron</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/26616/path-planning-for-collision-detection-between-two-polyhedra" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">438</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5318</span> A Refinement Strategy Coupling Event-B and Planning Domain Definition Language (PDDL) for Planning Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabrine%20Ammar">Sabrine Ammar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Tahar%20Bhiri"> Mohamed Tahar Bhiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic planning has a de facto standard language called Planning Domain Definition Language (PDDL) for describing planning problems. It aims to formalize the planning problems described by the concept of state space. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Indeed, these tools made it possible to detect errors a posteriori by means of test activity. In this paper, we recommend a formal approach coupling the two languages Event-B and PDDL, for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. Thus, this paper proposes a refinement strategy allowing to obtain reliable PDDL descriptions from an ultimate Event-B model correct by construction. The ultimate Event-B model, correct by construction which is supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=code%20generation" title="code generation">code generation</a>, <a href="https://publications.waset.org/abstracts/search?q=event-b" title=" event-b"> event-b</a>, <a href="https://publications.waset.org/abstracts/search?q=PDDL" title=" PDDL"> PDDL</a>, <a href="https://publications.waset.org/abstracts/search?q=refinement%20strategy" title=" refinement strategy"> refinement strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=translation%20rules" title=" translation rules"> translation rules</a> </p> <a href="https://publications.waset.org/abstracts/135577/a-refinement-strategy-coupling-event-b-and-planning-domain-definition-language-pddl-for-planning-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5317</span> Obtaining High-Dimensional Configuration Space for Robotic Systems Operating in a Common Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Yerlikaya">U. Yerlikaya</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20T.%20Balkan"> R. T. Balkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, a method is developed to obtain high-dimensional configuration space for path planning problems. In typical cases, the path planning problems are solved directly in the 3-dimensional (D) workspace. However, this method is inefficient in handling the robots with various geometrical and mechanical restrictions. To overcome these difficulties, path planning may be formalized and solved in a new space which is called configuration space. The number of dimensions of the configuration space comes from the degree of freedoms of the system of interest. The method can be applied in two ways. In the first way, the point clouds of all the bodies of the system and interaction of them are used. The second way is performed via using the clearance function of simulation software where the minimum distances between surfaces of bodies are simultaneously measured. A double-turret system is held in the scope of this study. The 4-D configuration space of a double-turret system is obtained in these two ways. As a result, the difference between these two methods is around 1%, depending on the density of the point cloud. The disparity between the two forms steadily decreases as the point cloud density increases. At the end of the study, in order to verify 4-D configuration space obtained, 4-D path planning problem was realized as 2-D + 2-D and a sample path planning is carried out with using A* algorithm. Then, the accuracy of the configuration space is proved using the obtained paths on the simulation model of the double-turret system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=A%2A%20algorithm" title="A* algorithm">A* algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20turrets" title=" autonomous turrets"> autonomous turrets</a>, <a href="https://publications.waset.org/abstracts/search?q=high-dimensional%20C-space" title=" high-dimensional C-space"> high-dimensional C-space</a>, <a href="https://publications.waset.org/abstracts/search?q=manifold%20C-space" title=" manifold C-space"> manifold C-space</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20clouds" title=" point clouds"> point clouds</a> </p> <a href="https://publications.waset.org/abstracts/134869/obtaining-high-dimensional-configuration-space-for-robotic-systems-operating-in-a-common-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5316</span> Path Planning for Multiple Unmanned Aerial Vehicles Based on Adaptive Probabilistic Sampling Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Long%20Cheng">Long Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20He"> Tong He</a>, <a href="https://publications.waset.org/abstracts/search?q=Iraj%20Mantegh"> Iraj Mantegh</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen-Fang%20Xie"> Wen-Fang Xie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path planning is essential for UAVs (Unmanned Aerial Vehicle) with autonomous navigation in unknown environments. In this paper, an adaptive probabilistic sampling algorithm is proposed for the GPS-denied environment, which can be utilized for autonomous navigation system of multiple UAVs in a dynamically-changing structured environment. This method can be used for Unmanned Aircraft Systems Traffic Management (UTM) solutions and in autonomous urban aerial mobility, where a number of platforms are expected to share the airspace. A path network is initially built off line based on available environment map, and on-board sensors systems on the flying UAVs are used for continuous situational awareness and to inform the changes in the path network. Simulation results based on MATLAB and Gazebo in different scenarios and algorithms performance measurement show the high efficiency and accuracy of the proposed technique in unknown environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20probabilistic%20sampling" title=" adaptive probabilistic sampling"> adaptive probabilistic sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20unmanned%20aerial%20vehicles" title=" multiple unmanned aerial vehicles"> multiple unmanned aerial vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=unknown%20environments" title=" unknown environments"> unknown environments</a> </p> <a href="https://publications.waset.org/abstracts/110260/path-planning-for-multiple-unmanned-aerial-vehicles-based-on-adaptive-probabilistic-sampling-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110260.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5315</span> An Exponential Field Path Planning Method for Mobile Robots Integrated with Visual Perception</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdy%20Roman">Magdy Roman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Shoeib"> Mostafa Shoeib</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Rostom"> Mostafa Rostom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title="path planning">path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20avoidance" title=" collision avoidance"> collision avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20robots" title=" mobile robots"> mobile robots</a> </p> <a href="https://publications.waset.org/abstracts/84189/an-exponential-field-path-planning-method-for-mobile-robots-integrated-with-visual-perception" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5314</span> GIS-Based Automatic Flight Planning of Camera-Equipped UAVs for Fire Emergency Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Sulaiman">Mohammed Sulaiman</a>, <a href="https://publications.waset.org/abstracts/search?q=Hexu%20Liu"> Hexu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Binalhaj"> Mohamed Binalhaj</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20W.%20Liou"> William W. Liou</a>, <a href="https://publications.waset.org/abstracts/search?q=Osama%20Abudayyeh"> Osama Abudayyeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Emerging technologies such as camera-equipped unmanned aerial vehicles (UAVs) are increasingly being applied in building fire rescue to provide real-time visualization and 3D reconstruction of the entire fireground. However, flight planning of camera-equipped UAVs is usually a manual process, which is not sufficient to fulfill the needs of emergency management. This research proposes a Geographic Information System (GIS)-based approach to automatic flight planning of camera-equipped UAVs for building fire emergency response. In this research, Haversine formula and lawn mowing patterns are employed to automate flight planning based on geometrical and spatial information from GIS. The resulting flight mission satisfies the requirements of 3D reconstruction purposes of the fireground, in consideration of flight execution safety and visibility of camera frames. The proposed approach is implemented within a GIS environment through an application programming interface. A case study is used to demonstrate the effectiveness of the proposed approach. The result shows that flight mission can be generated in a timely manner for application to fire emergency response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=camera-equipped%20UAVs" title=" camera-equipped UAVs"> camera-equipped UAVs</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20flight%20planning" title=" automatic flight planning"> automatic flight planning</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20emergency%20response" title=" fire emergency response"> fire emergency response</a> </p> <a href="https://publications.waset.org/abstracts/125166/gis-based-automatic-flight-planning-of-camera-equipped-uavs-for-fire-emergency-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125166.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5313</span> LanE-change Path Planning of Autonomous Driving Using Model-Based Optimization, Deep Reinforcement Learning and 5G Vehicle-to-Vehicle Communications </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=William%20Li">William Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lane-change path planning is a crucial and yet complex task in autonomous driving. The traditional path planning approach based on a system of carefully-crafted rules to cover various driving scenarios becomes unwieldy as more and more rules are added to deal with exceptions and corner cases. This paper proposes to divide the entire path planning to two stages. In the first stage the ego vehicle travels longitudinally in the source lane to reach a safe state. In the second stage the ego vehicle makes lateral lane-change maneuver to the target lane. The paper derives the safe state conditions based on lateral lane-change maneuver calculation to ensure collision free in the second stage. To determine the acceleration sequence that minimizes the time to reach a safe state in the first stage, the paper proposes three schemes, namely, kinetic model based optimization, deep reinforcement learning, and 5G vehicle-to-vehicle (V2V) communications. The paper investigates these schemes via simulation. The model-based optimization is sensitive to the model assumptions. The deep reinforcement learning is more flexible in handling scenarios beyond the model assumed by the optimization. The 5G V2V eliminates uncertainty in predicting future behaviors of surrounding vehicles by sharing driving intents and enabling cooperative driving. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lane%20change" title="lane change">lane change</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20driving" title=" autonomous driving"> autonomous driving</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=5G" title=" 5G"> 5G</a>, <a href="https://publications.waset.org/abstracts/search?q=V2V%20communications" title=" V2V communications"> V2V communications</a>, <a href="https://publications.waset.org/abstracts/search?q=connected%20vehicles" title=" connected vehicles"> connected vehicles</a> </p> <a href="https://publications.waset.org/abstracts/118114/lane-change-path-planning-of-autonomous-driving-using-model-based-optimization-deep-reinforcement-learning-and-5g-vehicle-to-vehicle-communications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118114.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">252</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5312</span> Genetic Algorithm for In-Theatre Military Logistics Search-and-Delivery Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean%20Berger">Jean Berger</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Barkaoui"> Mohamed Barkaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20path%20planning" title="search path planning">search path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm" title=" false alarm"> false alarm</a>, <a href="https://publications.waset.org/abstracts/search?q=search-and-delivery" title=" search-and-delivery"> search-and-delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/2263/genetic-algorithm-for-in-theatre-military-logistics-search-and-delivery-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2263.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5311</span> Performance Evaluation of Arrival Time Prediction Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Li">Bin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei%20Liu"> Mei Liu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bus%20transit" title="bus transit">bus transit</a>, <a href="https://publications.waset.org/abstracts/search?q=arrival%20time%20prediction" title=" arrival time prediction"> arrival time prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=link-based" title=" link-based"> link-based</a>, <a href="https://publications.waset.org/abstracts/search?q=path-based" title=" path-based"> path-based</a> </p> <a href="https://publications.waset.org/abstracts/2389/performance-evaluation-of-arrival-time-prediction-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2389.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5310</span> Development and Implementation of Curvature Dependent Force Correction Algorithm for the Planning of Forced Controlled Robotic Grinding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aiman%20Alshare">Aiman Alshare</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Qaadan"> Sahar Qaadan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A curvature dependent force correction algorithm for planning force controlled grinding process with off-line programming flexibility is designed for ABB industrial robot, in order to avoid the manual interface during the process. The machining path utilizes a spline curve fit that is constructed from the CAD data of the workpiece. The fitted spline has a continuity of the second order to assure path smoothness. The implemented algorithm computes uniform forces normal to the grinding surface of the workpiece, by constructing a curvature path in the spatial coordinates using the spline method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABB%20industrial%20robot" title="ABB industrial robot">ABB industrial robot</a>, <a href="https://publications.waset.org/abstracts/search?q=grinding%20process" title=" grinding process"> grinding process</a>, <a href="https://publications.waset.org/abstracts/search?q=offline%20programming" title=" offline programming"> offline programming</a>, <a href="https://publications.waset.org/abstracts/search?q=CAD%20data%20extraction" title=" CAD data extraction"> CAD data extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20correction%20algorithm" title=" force correction algorithm"> force correction algorithm</a> </p> <a href="https://publications.waset.org/abstracts/49221/development-and-implementation-of-curvature-dependent-force-correction-algorithm-for-the-planning-of-forced-controlled-robotic-grinding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">362</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5309</span> A New Multi-Target, Multi-Agent Search and Rescue Path Planning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean%20Berger">Jean Berger</a>, <a href="https://publications.waset.org/abstracts/search?q=Nassirou%20Lo"> Nassirou Lo</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Noel"> Martin Noel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20path%20planning" title="search path planning">search path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20and%20rescue" title=" search and rescue"> search and rescue</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/5918/a-new-multi-target-multi-agent-search-and-rescue-path-planning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5918.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">371</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5308</span> Design and Implementation of Bluetooth Controlled Autonomous Vehicle </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amanuel%20Berhanu%20Kesamo">Amanuel Berhanu Kesamo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents both circuit simulation and hardware implementation of a robot vehicle that can be either controlled manually via Bluetooth with video streaming or navigate autonomously to a target point by avoiding obstacles. In manual mode, the user controls the mobile robot using C# windows form interfaced via Bluetooth. The camera mounted on the robot is used to capture and send the real time video to the user. In autonomous mode, the robot plans the shortest path to the target point while avoiding obstacles along the way. Ultrasonic sensor is used for sensing the obstacle in its environment. An efficient path planning algorithm is implemented to navigate the robot along optimal route. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arduino%20Uno" title="Arduino Uno">Arduino Uno</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous" title=" autonomous"> autonomous</a>, <a href="https://publications.waset.org/abstracts/search?q=Bluetooth%20module" title=" Bluetooth module"> Bluetooth module</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20controlled%20robot" title=" remote controlled robot"> remote controlled robot</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra%20sonic%20sensor" title=" ultra sonic sensor"> ultra sonic sensor</a> </p> <a href="https://publications.waset.org/abstracts/119807/design-and-implementation-of-bluetooth-controlled-autonomous-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119807.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5307</span> Path Planning for Unmanned Aerial Vehicles in Constrained Environments for Locust Elimination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aadiv%20Shah">Aadiv Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hari%20Nair"> Hari Nair</a>, <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Mittal"> Vedant Mittal</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Cheeran"> Alice Cheeran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Present-day agricultural practices such as blanket spraying not only lead to excessive usage of pesticides but also harm the overall crop yield. This paper introduces an algorithm to optimize the traversal of an unmanned aerial vehicle (UAV) in constrained environments. The proposed system focuses on the agricultural application of targeted spraying for locust elimination. Given a satellite image of a farm, target zones that are prone to locust swarm formation are detected through the calculation of the normalized difference vegetation index (NDVI). This is followed by determining the optimal path for traversal of a UAV through these target zones using the proposed algorithm in order to perform pesticide spraying in the most efficient manner possible. Unlike the classic travelling salesman problem involving point-to-point optimization, the proposed algorithm determines an optimal path for multiple regions, independent of its geometry. Finally, the paper explores the idea of implementing reinforcement learning to model complex environmental behaviour and make the path planning mechanism for UAVs agnostic to external environment changes. This system not only presents a solution to the enormous losses incurred due to locust attacks but also an efficient way to automate agricultural practices across the globe in order to improve farmer ergonomics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=locust" title="locust">locust</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/138548/path-planning-for-unmanned-aerial-vehicles-in-constrained-environments-for-locust-elimination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138548.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">248</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5306</span> Critical Path Segments Method for Scheduling Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sherif%20M.%20Hafez">Sherif M. Hafez</a>, <a href="https://publications.waset.org/abstracts/search?q=Remon%20F.%20Aziz"> Remon F. Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=May%20S.%20A.%20Elalim"> May S. A. Elalim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Project managers today rely on scheduling tools based on the Critical Path Method (CPM) to determine the overall project duration and the activities’ float times which lead to greater efficiency in planning and control of projects. CPM was useful for scheduling construction projects, but researchers had highlighted a number of serious drawbacks that limit its use as a decision support tool and lacks the ability to clearly record and represent detailed information. This paper discusses the drawbacks of CPM as a scheduling technique and presents a modified critical path method (CPM) model which is called critical path segments (CPS). The CPS scheduling mechanism addresses the problems of CPM in three ways: decomposing the activity duration of separated but connected time segments; all relationships among activities are converted into finish–to–start relationship; and analysis and calculations are made with forward path. Sample cases are included to illustrate the shortages in CPM, CPS full analysis and calculations are explained in details, and how schedules can be handled better with the CPS technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20management" title="construction management">construction management</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20path%20method" title=" critical path method"> critical path method</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20path%20segments" title=" critical path segments"> critical path segments</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20pass" title=" forward pass"> forward pass</a>, <a href="https://publications.waset.org/abstracts/search?q=float" title=" float"> float</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20control" title=" project control"> project control</a> </p> <a href="https://publications.waset.org/abstracts/17760/critical-path-segments-method-for-scheduling-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17760.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5305</span> Autonomous Kuka Youbot Navigation Based on Machine Learning and Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gordon">Carlos Gordon</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricio%20Encalada"> Patricio Encalada</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20Lema"> Henry Lema</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Leon"> Diego Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=Dennis%20Chicaiza"> Dennis Chicaiza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20navigation" title="autonomous navigation">autonomous navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20operative%20system" title=" robotic operative system"> robotic operative system</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source%20computer%20vision%20library" title=" open source computer vision library"> open source computer vision library</a> </p> <a href="https://publications.waset.org/abstracts/101726/autonomous-kuka-youbot-navigation-based-on-machine-learning-and-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5304</span> Dynamic Model of Automatic Loom on SimulationX</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Jomartov">A. Jomartov</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tuleshov"> A. Tuleshov</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Tultaev"> B. Tultaev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the main tasks in the development of textile machinery is to increase the rapidity of automatic looms, and consequently, their productivity. With increasing automatic loom speeds, the dynamic loads on their separate mechanisms and moving joints sharply increase. Dynamic research allows us to determine the weakest mechanisms of the automatic loom. The modern automatic loom consists of a large number of structurally different mechanisms. These are cam, lever, gear, friction and combined cyclic mechanisms. The modern automatic loom contains various mechatronic devices: A device for the automatic removal of faulty weft, electromechanical drive warp yarns, electronic controllers, servos, etc. In the paper, we consider the multibody dynamic model of the automatic loom on the software complex SimulationX. SimulationX is multidisciplinary software for modeling complex physical and technical facilities and systems. The multibody dynamic model of the automatic loom allows consideration of: The transition processes, backlash at the joints and nodes, the force of resistance and electric motor performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20loom" title="automatic loom">automatic loom</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamics" title=" dynamics"> dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=multibody" title=" multibody"> multibody</a>, <a href="https://publications.waset.org/abstracts/search?q=SimulationX" title=" SimulationX"> SimulationX</a> </p> <a href="https://publications.waset.org/abstracts/59167/dynamic-model-of-automatic-loom-on-simulationx" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59167.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5303</span> Robotic Arm-Automated Spray Painting with One-Shot Object Detection and Region-Based Path Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iqraq%20Kamal">Iqraq Kamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Akmal%20Razif"> Akmal Razif</a>, <a href="https://publications.waset.org/abstracts/search?q=Sivadas%20Chandra%20Sekaran"> Sivadas Chandra Sekaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Syazwan%20Hisaburi"> Ahmad Syazwan Hisaburi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerospace%20manufacturing" title="aerospace manufacturing">aerospace manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=one-shot%20object%20detection" title=" one-shot object detection"> one-shot object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20spray%20painting" title=" automated spray painting"> automated spray painting</a>, <a href="https://publications.waset.org/abstracts/search?q=vision-based%20path%20optimization" title=" vision-based path optimization"> vision-based path optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=automation" title=" automation"> automation</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20arm" title=" robotic arm"> robotic arm</a> </p> <a href="https://publications.waset.org/abstracts/176471/robotic-arm-automated-spray-painting-with-one-shot-object-detection-and-region-based-path-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5302</span> Investigated Optimization of Davidson Path Loss Model for Digital Terrestrial Television (DTTV) Propagation in Urban Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pitak%20Keawbunsong">Pitak Keawbunsong</a>, <a href="https://publications.waset.org/abstracts/search?q=Sathaporn%20Promwong"> Sathaporn Promwong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an investigation on the efficiency of the optimized Davison path loss model in order to look for a suitable path loss model to design and planning DTTV propagation for small and medium urban areas in southern Thailand. Hadyai City in Songkla Province is chosen as the case study to collect the analytical data on the electric field strength. The optimization is conducted through the least square method while the efficiency index is through the statistical value of relative error (RE). The result of the least square method is the offset and slop of the frequency to be used in the optimized process. The statistical result shows that RE of the old Davidson model is at the least when being compared with the optimized Davison and the Hata models. Thus, the old Davison path loss model is the most accurate that further becomes the most optimized for the plan on the propagation network design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DTTV%20propagation" title="DTTV propagation">DTTV propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20loss%20model" title=" path loss model"> path loss model</a>, <a href="https://publications.waset.org/abstracts/search?q=Davidson%20model" title=" Davidson model"> Davidson model</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20method" title=" least square method"> least square method</a> </p> <a href="https://publications.waset.org/abstracts/43812/investigated-optimization-of-davidson-path-loss-model-for-digital-terrestrial-television-dttv-propagation-in-urban-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43812.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5301</span> Designing Floor Planning in 2D and 3D with an Efficient Topological Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Nagammai">V. Nagammai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=application%20specific%20noc" title="application specific noc">application specific noc</a>, <a href="https://publications.waset.org/abstracts/search?q=b%2A%20tree%20representation" title=" b* tree representation"> b* tree representation</a>, <a href="https://publications.waset.org/abstracts/search?q=floor%20planning" title=" floor planning"> floor planning</a>, <a href="https://publications.waset.org/abstracts/search?q=t%20tree%20representation" title=" t tree representation"> t tree representation</a> </p> <a href="https://publications.waset.org/abstracts/45379/designing-floor-planning-in-2d-and-3d-with-an-efficient-topological-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45379.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5300</span> Comparison of Computer Software for Swept Path Analysis on Example of Special Paved Areas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ivana%20Cestar">Ivana Cestar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivica%20Stan%C4%8Deri%C4%87"> Ivica Stančerić</a>, <a href="https://publications.waset.org/abstracts/search?q=Sa%C5%A1a%20Ahac"> Saša Ahac</a>, <a href="https://publications.waset.org/abstracts/search?q=Vesna%20Drag%C4%8Devi%C4%87"> Vesna Dragčević</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamara%20D%C5%BEambas"> Tamara Džambas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On special paved areas, such as road intersections, vehicles are usually moving through horizontal curves with smaller radii and occupy considerably greater area compared to open road segments. Planning procedure of these areas is mainly an iterative process that consists of designing project elements, assembling those elements to a design project, and analyzing swept paths for the design vehicle. If applied elements do not fulfill the swept path requirements for the design vehicle, the process must be carried out again. Application of specialized computer software for swept path analysis significantly facilitates planning procedure of special paved areas. There are various software of this kind available on the global market, and each of them has different specifications. In this paper, comparison of two software commonly used in Croatia (Auto TURN and Vehicle Tracking) is presented, their advantages and disadvantages are described, and their applicability on a particular paved area is discussed. In order to reveal which one of the analyszed software is more favorable in terms of swept paths widths, which one includes input parameters that are more relevant for this kind of analysis, and which one is more suitable for the application on a certain special paved area, the analysis shown in this paper was conducted on a number of different intersection types. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20comparison" title="software comparison">software comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=special%20paved%20areas" title=" special paved areas"> special paved areas</a>, <a href="https://publications.waset.org/abstracts/search?q=swept%20path%20analysis" title=" swept path analysis"> swept path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=swept%20path%20input%20parameters" title=" swept path input parameters"> swept path input parameters</a> </p> <a href="https://publications.waset.org/abstracts/34359/comparison-of-computer-software-for-swept-path-analysis-on-example-of-special-paved-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=automatic%20path%20planning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=automatic%20path%20planning&page=3">3</a></li> <li class="page-item"><a class="page-link" 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