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path</title> <meta name="description" content="Search results for: optimal path"> <meta name="keywords" content="optimal path"> <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 d-lg-none navbar-toggler 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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="optimal path"> <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> 4224</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: optimal path</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4224</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">4223</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">4222</span> Optimal Path Motion of Positional Electric Drive</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Grigoryev">M. A. Grigoryev</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20N.%20Shishkov"> A. N. Shishkov</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20V.%20Savosteenko"> N. V. Savosteenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article identifies optimal path motion of positional electric drive, for example, the feed of cold pilgering mill. It is shown that triangle is the optimum shape of the speed curve, and the ratio of its sides depends on the type of load diagram, in particular from the influence of the main drive of pilgering mill, and is not dependent on the presence of backlash and elasticity in the system. This thesis is proved analytically, and confirmed the results are obtained by a mathematical model that take into account the influence of the main drive-to-drive feed. By statistical analysis of oscillograph traces obtained on the real object allowed to give recommendations on the optimal control of the electric drive feed cold pilgering mill 450. Based on the data that the load torque depends on by hit the pipe in rolls of pilgering mill, occurs in the interval (0,6…0,75) tc, the recommended ratio of start time to the braking time is 2:1. Optimized path motion allowed get up to 25% more RMS torque for the cycle that allowed increased the productivity of the mill. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20curve%20speed" title="optimal curve speed">optimal curve speed</a>, <a href="https://publications.waset.org/abstracts/search?q=positional%20electric%20drive" title=" positional electric drive"> positional electric drive</a>, <a href="https://publications.waset.org/abstracts/search?q=cold%20pilgering%20mill%20450" title=" cold pilgering mill 450"> cold pilgering mill 450</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20path%20motion" title=" optimal path motion"> optimal path motion</a> </p> <a href="https://publications.waset.org/abstracts/46141/optimal-path-motion-of-positional-electric-drive" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46141.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">318</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">4221</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">302</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">4220</span> Iterative Linear Quadratic Regulator (iLQR) vs LQR Controllers for Quadrotor Path Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wesam%20Jasim">Wesam Jasim</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongbing%20Gu"> Dongbing Gu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an iterative linear quadratic regulator optimal control technique to solve the problem of quadrotors path tracking. The dynamic motion equations are represented based on unit quaternion representation and include some modelled aerodynamical effects as a nonlinear part. Simulation results prove the ability and effectiveness of iLQR to stabilize the quadrotor and successfully track different paths. It also shows that iLQR controller outperforms LQR controller in terms of fast convergence and tracking errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iLQR%20controller" title="iLQR controller">iLQR controller</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=path%20tracking" title=" path tracking"> path tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrotor%20UAVs" title=" quadrotor UAVs"> quadrotor UAVs</a> </p> <a href="https://publications.waset.org/abstracts/51436/iterative-linear-quadratic-regulator-ilqr-vs-lqr-controllers-for-quadrotor-path-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51436.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">447</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">4219</span> Determine the Optimal Path of Content Adaptation Services with Max Heap Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shilan%20Rahmani%20Azr">Shilan Rahmani Azr</a>, <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Emtiyaz"> Siavash Emtiyaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent development in computing and communicative technologies leads to much easier mobile accessibility to the information. Users can access to the information in different places using various deceives in which the care variety of abilities. Meanwhile, the format and details of electronic documents are changing each day. In these cases, a mismatch is created between content and client’s abilities. Recently the service-oriented content adaption has been developed which the adapting tasks are dedicated to some extended services. In this method, the main problem is to choose the best appropriate service among accessible and distributed services. In this paper, a method for determining the optimal path to the best services, based on the quality control parameters and user preferences, is proposed using max heap tree. The efficiency of this method in contrast to the other previous methods of the content adaptation is related to the determining the optimal path of the best services which are measured. The results show the advantages and progresses of this method in compare of the others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service-oriented%20content%20adaption" title="service-oriented content adaption">service-oriented content adaption</a>, <a href="https://publications.waset.org/abstracts/search?q=QoS" title=" QoS"> QoS</a>, <a href="https://publications.waset.org/abstracts/search?q=max%20heap%20tree" title=" max heap tree"> max heap tree</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20services" title=" web services"> web services</a> </p> <a href="https://publications.waset.org/abstracts/47488/determine-the-optimal-path-of-content-adaptation-services-with-max-heap-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47488.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">259</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">4218</span> Services-Oriented Model for the Regulation of Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Bendahmane">Mohamed Bendahmane</a>, <a href="https://publications.waset.org/abstracts/search?q=Brahim%20Elfalaki"> Brahim Elfalaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Benattou"> Mohammed Benattou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the major sources of learners' professional difficulties is their heterogeneity. Whether on cognitive, social, cultural or emotional level, learners being part of the same group have many differences. These differences do not allow to apply the same learning process at all learners. Thus, an optimal learning path for one, is not necessarily the same for the other. We present in this paper a model-oriented service to offer to each learner a personalized learning path to acquire the targeted skills. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20path" title="learning path">learning path</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a>, <a href="https://publications.waset.org/abstracts/search?q=trace%20analysis" title=" trace analysis"> trace analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=personalization" title=" personalization"> personalization</a> </p> <a href="https://publications.waset.org/abstracts/50834/services-oriented-model-for-the-regulation-of-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50834.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">356</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">4217</span> Generalized Central Paths for Convex Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Zhi%20Liao">Li-Zhi Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The central path has played the key role in the interior point method. However, the convergence of the central path may not be true even in some convex programming problems with linear constraints. In this paper, the generalized central paths are introduced for convex programming. One advantage of the generalized central paths is that the paths will always converge to some optimal solutions of the convex programming problem for any initial interior point. Some additional theoretical properties for the generalized central paths will be also reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20path" title="central path">central path</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20programming" title=" convex programming"> convex programming</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20central%20path" title=" generalized central path"> generalized central path</a>, <a href="https://publications.waset.org/abstracts/search?q=interior%20point%20method" title=" interior point method"> interior point method</a> </p> <a href="https://publications.waset.org/abstracts/58039/generalized-central-paths-for-convex-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58039.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">327</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">4216</span> Optimization Based Obstacle Avoidance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Dariani">R. Dariani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Schmidt"> S. Schmidt</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Kasper"> R. Kasper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on a non-linear single track model which describes the dynamics of vehicle, an optimal path planning strategy is developed. Real time optimization is used to generate reference control values to allow leading the vehicle alongside a calculated lane which is optimal for different objectives such as energy consumption, run time, safety or comfort characteristics. Strict mathematic formulation of the autonomous driving allows taking decision on undefined situation such as lane change or obstacle avoidance. Based on position of the vehicle, lane situation and obstacle position, the optimization problem is reformulated in real-time to avoid the obstacle and any car crash. <p class="card-text"><strong>Keywords:</strong> <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=obstacle%20avoidance" title=" obstacle avoidance"> obstacle avoidance</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=path%20planning" title=" path planning"> path planning</a> </p> <a href="https://publications.waset.org/abstracts/13122/optimization-based-obstacle-avoidance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13122.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">370</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">4215</span> Optimizing Network Latency with Fast Path Assignment for Incoming Flows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qing%20Lyu">Qing Lyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hang%20Zhu"> Hang Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various flows in the network require to go through different types of middlebox. The improper placement of network middlebox and path assignment for flows could greatly increase the network latency and also decrease the performance of network. Minimizing the total end to end latency of all the ows requires to assign path for the incoming flows. In this paper, the flow path assignment problem in regard to the placement of various kinds of middlebox is studied. The flow path assignment problem is formulated to a linear programming problem, which is very time consuming. On the other hand, a naive greedy algorithm is studied. Which is very fast but causes much more latency than the linear programming algorithm. At last, the paper presents a heuristic algorithm named FPA, which takes bottleneck link information and estimated bandwidth occupancy into consideration, and achieves near optimal latency in much less time. Evaluation results validate the effectiveness of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flow%20path" title="flow path">flow path</a>, <a href="https://publications.waset.org/abstracts/search?q=latency" title=" latency"> latency</a>, <a href="https://publications.waset.org/abstracts/search?q=middlebox" title=" middlebox"> middlebox</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a> </p> <a href="https://publications.waset.org/abstracts/103177/optimizing-network-latency-with-fast-path-assignment-for-incoming-flows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103177.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">207</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">4214</span> An Efficient Robot Navigation Model in a Multi-Target Domain amidst Static and Dynamic Obstacles </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Ayomoh">Michael Ayomoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriaan%20Roux"> Adriaan Roux</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyindamola%20Omotuyi"> Oyindamola Omotuyi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient robot navigation model in a multi-target domain amidst static and dynamic workspace obstacles. The problem is that of developing an optimal algorithm to minimize the total travel time of a robot as it visits all target points within its task domain amidst unknown workspace obstacles and finally return to its initial position. In solving this problem, a classical algorithm was first developed to compute the optimal number of paths to be travelled by the robot amidst the network of paths. The principle of shortest distance between robot and targets was used to compute the target point visitation order amidst workspace obstacles. Algorithm premised on the standard polar coordinate system was developed to determine the length of obstacles encountered by the robot hence giving room for a geometrical estimation of the total surface area occupied by the obstacle especially when classified as a relevant obstacle i.e. obstacle that lies in between a robot and its potential visitation point. A stochastic model was developed and used to estimate the likelihood of a dynamic obstacle bumping into the robot’s navigation path and finally, the navigation/obstacle avoidance algorithm was hinged on the hybrid virtual force field (HVFF) method. Significant modelling constraints herein include the choice of navigation path to selected target points, the possible presence of static obstacles along a desired navigation path and the likelihood of encountering a dynamic obstacle along the robot’s path and the chances of it remaining at this position as a static obstacle hence resulting in a case of re-routing after routing. The proposed algorithm demonstrated a high potential for optimal solution in terms of efficiency and effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-target" title="multi-target">multi-target</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20robot" title=" mobile robot"> mobile robot</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20path" title=" optimal path"> optimal path</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20obstacles" title=" static obstacles"> static obstacles</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20obstacles" title=" dynamic obstacles"> dynamic obstacles</a> </p> <a href="https://publications.waset.org/abstracts/82853/an-efficient-robot-navigation-model-in-a-multi-target-domain-amidst-static-and-dynamic-obstacles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82853.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">281</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">4213</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">251</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">4212</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">251</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">4211</span> Resource Constrained Time-Cost Trade-Off Analysis in Construction Project Planning and Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sangwon%20Han">Sangwon Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Chengquan%20Jin"> Chengquan Jin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time-cost trade-off (TCTO) is one of the most significant part of construction project management. Despite the significance, current TCTO analysis, based on the Critical Path Method, does not consider resource constraint, and accordingly sometimes generates an impractical and/or infeasible schedule planning in terms of resource availability. Therefore, resource constraint needs to be considered when doing TCTO analysis. In this research, genetic algorithms (GA) based optimization model is created in order to find the optimal schedule. This model is utilized to compare four distinct scenarios (i.e., 1) initial CPM, 2) TCTO without considering resource constraint, 3) resource allocation after TCTO, and 4) TCTO with considering resource constraint) in terms of duration, cost, and resource utilization. The comparison results identify that ‘TCTO with considering resource constraint’ generates the optimal schedule with the respect of duration, cost, and resource. This verifies the need for consideration of resource constraint when doing TCTO analysis. It is expected that the proposed model will produce more feasible and optimal schedule. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time-cost%20trade-off" title="time-cost trade-off">time-cost trade-off</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</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=resource%20availability" title=" resource availability"> resource availability</a> </p> <a href="https://publications.waset.org/abstracts/91630/resource-constrained-time-cost-trade-off-analysis-in-construction-project-planning-and-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91630.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">187</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">4210</span> Top-K Shortest Distance as a Similarity Measure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Lebedev">Andrey Lebedev</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilya%20Dmitrenok"> Ilya Dmitrenok</a>, <a href="https://publications.waset.org/abstracts/search?q=JooYoung%20Lee"> JooYoung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Leonard%20Johard"> Leonard Johard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20matching" title="graph matching">graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=link%20prediction" title=" link prediction"> link prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=shortest%20path" title=" shortest path"> shortest path</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a> </p> <a href="https://publications.waset.org/abstracts/63488/top-k-shortest-distance-as-a-similarity-measure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63488.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">358</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">4209</span> Optimised Path Recommendation for a Real Time Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Likewin%20Thomas">Likewin Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Manoj%20Kumar"> M. V. Manoj Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Annappa"> B. Annappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-organization%20process%20mining" title="cross-organization process mining">cross-organization process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20behaviour" title=" process behaviour"> process behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20of%20execution" title=" path of execution"> path of execution</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20regression%20model" title=" polynomial regression model"> polynomial regression model</a> </p> <a href="https://publications.waset.org/abstracts/45002/optimised-path-recommendation-for-a-real-time-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45002.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">334</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">4208</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">4207</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">4206</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">165</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">4205</span> Optimal 3D Deployment and Path Planning of Multiple Uavs for Maximum Coverage and Autonomy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indu%20Chandran">Indu Chandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Shubham%20Sharma"> Shubham Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Rohan%20Mehta"> Rohan Mehta</a>, <a href="https://publications.waset.org/abstracts/search?q=Vipin%20Kizheppatt"> Vipin Kizheppatt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned aerial vehicles are increasingly being explored as the most promising solution to disaster monitoring, assessment, and recovery. Current relief operations heavily rely on intelligent robot swarms to capture the damage caused, provide timely rescue, and create road maps for the victims. To perform these time-critical missions, efficient path planning that ensures quick coverage of the area is vital. This study aims to develop a technically balanced approach to provide maximum coverage of the affected area in a minimum time using the optimal number of UAVs. A coverage trajectory is designed through area decomposition and task assignment. To perform efficient and autonomous coverage mission, solution to a TSP-based optimization problem using meta-heuristic approaches is designed to allocate waypoints to the UAVs of different flight capacities. The study exploits multi-agent simulations like PX4-SITL and QGroundcontrol through the ROS framework and visualizes the dynamics of UAV deployment to different search paths in a 3D Gazebo environment. Through detailed theoretical analysis and simulation tests, we illustrate the optimality and efficiency of the proposed methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=area%20coverage" title="area coverage">area coverage</a>, <a href="https://publications.waset.org/abstracts/search?q=coverage%20path%20planning" title=" coverage path planning"> coverage path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=mission%20monitoring" title=" mission monitoring"> mission monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=task%20assignment" title=" task assignment"> task assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicles" title=" unmanned aerial vehicles"> unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/161102/optimal-3d-deployment-and-path-planning-of-multiple-uavs-for-maximum-coverage-and-autonomy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161102.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">215</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">4204</span> Optimal and Critical Path Analysis of State Transportation Network Using Neo4J</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pallavi%20Bhogaram">Pallavi Bhogaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaolong%20Wu"> Xiaolong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20He"> Min He</a>, <a href="https://publications.waset.org/abstracts/search?q=Onyedikachi%20Okenwa"> Onyedikachi Okenwa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's <em>k</em>-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area. <p class="card-text"><strong>Keywords:</strong> <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=transportation%20network" title=" transportation network"> transportation network</a>, <a href="https://publications.waset.org/abstracts/search?q=connectivity%20reliability" title=" connectivity reliability"> connectivity reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20model" title=" network model"> network model</a>, <a href="https://publications.waset.org/abstracts/search?q=Neo4j%20application" title=" Neo4j application"> Neo4j application</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20betweenness%20centrality%20index" title=" edge betweenness centrality index"> edge betweenness centrality index</a> </p> <a href="https://publications.waset.org/abstracts/127021/optimal-and-critical-path-analysis-of-state-transportation-network-using-neo4j" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127021.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">134</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">4203</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">4202</span> Quality of Service Based Routing Algorithm for Real Time Applications in MANETs Using Ant Colony and Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farahnaz%20Karami">Farahnaz Karami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20ad%20hoc%20networks" title="mobile ad hoc networks">mobile ad hoc networks</a>, <a href="https://publications.waset.org/abstracts/search?q=routing" title=" routing"> routing</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20service" title=" quality of service"> quality of service</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title=" ant colony"> ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a> </p> <a href="https://publications.waset.org/abstracts/177059/quality-of-service-based-routing-algorithm-for-real-time-applications-in-manets-using-ant-colony-and-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177059.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">64</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">4201</span> Independence and Path Independence on Cayley Digraphs of Left Groups and Right Groups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nuttawoot%20Nupo">Nuttawoot Nupo</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayan%20Panma"> Sayan Panma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A semigroup S is said to be a left (right) zero semigroup if S satisfies the equation xy=x (xy=y) for all x,y in S. In addition, the semigroup S is called a left (right) group if S is isomorphic to the direct product of a group and a left (right) zero semigroup. The Cayley digraph Cay(S,A) of a semigroup S with a connection set A is defined to be a digraph with the vertex set S and the arc set E(Cay(S,A))={(x,xa) | x∈S, a∈A} where A is any subset of S. All sets in this research are assumed to be finite. Let D be a digraph together with a vertex set V and an arc set E. Let u and v be two different vertices in V and I a nonempty subset of V. The vertices u and v are said to be independent if (u,v)∉E and (v,u)∉E. The set I is called an independent set of D if any two different vertices in I are independent. The independence number of D is the maximum cardinality of an independent set of D. Moreover, the vertices u and v are said to be path independent if there is no dipath from u to v and there is no dipath from v to u. The set I is called a path independent set of D if any two different vertices in I are path independent. The path independence number of D is the maximum cardinality of a path independent set of D. In this research, we describe a lower bound and an upper bound of the independence number of Cayley digraphs of left groups and right groups. Some examples corresponding to those bounds are illustrated here. Furthermore, the exact value of the path independence number of Cayley digraphs of left groups and right groups are also presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cayley%20digraphs" title="Cayley digraphs">Cayley digraphs</a>, <a href="https://publications.waset.org/abstracts/search?q=independence%20number" title=" independence number"> independence number</a>, <a href="https://publications.waset.org/abstracts/search?q=left%20groups" title=" left groups"> left groups</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20independence%20number" title=" path independence number"> path independence number</a>, <a href="https://publications.waset.org/abstracts/search?q=right%20groups" title=" right groups"> right groups</a> </p> <a href="https://publications.waset.org/abstracts/59306/independence-and-path-independence-on-cayley-digraphs-of-left-groups-and-right-groups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59306.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">233</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">4200</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">4199</span> Copula-Based Estimation of Direct and Indirect Effects in Path Analysis Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alam%20Ali">Alam Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Pathak"> Ashok Kumar Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path analysis is a statistical technique used to evaluate the direct and indirect effects of variables in path models. One or more structural regression equations are used to estimate a series of parameters in path models to find the better fit of data. However, sometimes the assumptions of classical regression models, such as ordinary least squares (OLS), are violated by the nature of the data, resulting in insignificant direct and indirect effects of exogenous variables. This article aims to explore the effectiveness of a copula-based regression approach as an alternative to classical regression, specifically when variables are linked through an elliptical copula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20analysis" title="path analysis">path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=copula-based%20regression%20models" title=" copula-based regression models"> copula-based regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20and%20indirect%20effects" title=" direct and indirect effects"> direct and indirect effects</a>, <a href="https://publications.waset.org/abstracts/search?q=k-fold%20cross%20validation%20technique" title=" k-fold cross validation technique"> k-fold cross validation technique</a> </p> <a href="https://publications.waset.org/abstracts/186900/copula-based-estimation-of-direct-and-indirect-effects-in-path-analysis-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186900.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">41</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">4198</span> Eliminating Cutter-Path Deviation For Five-Axis Nc Machining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alan%20C.%20Lin">Alan C. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsong%20Der%20Lin"> Tsong Der Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes a deviation control method to add interpolation points to numerical control (NC) codes of five-axis machining in order to achieve the required machining accuracy. Specific research issues include: (1) converting machining data between the CL (cutter location) domain and the NC domain, (2) calculating the deviation between the deviated path and the linear path, (3) finding interpolation points, and (4) determining tool orientations for the interpolation points. System implementation with practical examples will also be included to highlight the applicability of the proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD%2FCAM" title="CAD/CAM">CAD/CAM</a>, <a href="https://publications.waset.org/abstracts/search?q=cutter%20path" title=" cutter path"> cutter path</a>, <a href="https://publications.waset.org/abstracts/search?q=five-axis%20machining" title=" five-axis machining"> five-axis machining</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20control" title=" numerical control"> numerical control</a> </p> <a href="https://publications.waset.org/abstracts/30394/eliminating-cutter-path-deviation-for-five-axis-nc-machining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30394.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">424</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">4197</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">4196</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">4195</span> Nonparametric Path Analysis with Truncated Spline Approach in Modeling Rural Poverty in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usriatur%20Rohma">Usriatur Rohma</a>, <a href="https://publications.waset.org/abstracts/search?q=Adji%20Achmad%20Rinaldo%20Fernandes"> Adji Achmad Rinaldo Fernandes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nonparametric path analysis is a statistical method that does not rely on the assumption that the curve is known. The purpose of this study is to determine the best nonparametric truncated spline path function between linear and quadratic polynomial degrees with 1, 2, and 3-knot points and to determine the significance of estimating the best nonparametric truncated spline path function in the model of the effect of population migration and agricultural economic growth on rural poverty through the variable unemployment rate using the t-test statistic at the jackknife resampling stage. The data used in this study are secondary data obtained from statistical publications. The results showed that the best model of nonparametric truncated spline path analysis is quadratic polynomial degree with 3-knot points. In addition, the significance of the best-truncated spline nonparametric path function estimation using jackknife resampling shows that all exogenous variables have a significant influence on the endogenous variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20path%20analysis" title="nonparametric path analysis">nonparametric path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=truncated%20spline" title=" truncated spline"> truncated spline</a>, <a href="https://publications.waset.org/abstracts/search?q=linear" title=" linear"> linear</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic" title=" quadratic"> quadratic</a>, <a href="https://publications.waset.org/abstracts/search?q=rural%20poverty" title=" rural poverty"> rural poverty</a>, <a href="https://publications.waset.org/abstracts/search?q=jackknife%20resampling" title=" jackknife resampling"> jackknife resampling</a> </p> <a href="https://publications.waset.org/abstracts/186676/nonparametric-path-analysis-with-truncated-spline-approach-in-modeling-rural-poverty-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186676.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">46</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=optimal%20path&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimal%20path&page=3">3</a></li> <li class="page-item"><a class="page-link" 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