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obstacle avoidance</title> <meta name="description" content="Search results for: local obstacle avoidance"> <meta name="keywords" content="local obstacle avoidance"> <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 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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="local obstacle avoidance"> <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> 5917</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: local obstacle avoidance</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5917</span> Obstacle Avoidance Using Image-Based Visual Servoing Based on Deep Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tong%20He">Tong He</a>, <a href="https://publications.waset.org/abstracts/search?q=Long%20Chen"> Long Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Irag%20Mantegh"> Irag 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> This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image-based%20visual%20servoing" title="image-based visual servoing">image-based visual servoing</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=tracking%20target%20identification" title=" tracking target identification"> tracking target identification</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=artificial%20potential%20approach" title=" artificial potential approach"> artificial potential approach</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/110259/obstacle-avoidance-using-image-based-visual-servoing-based-on-deep-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110259.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">5916</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">5915</span> Drone On-Time Obstacle Avoidance for 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=Herath%20M.%20P.%20C.%20Jayaweera">Herath M. P. C. Jayaweera</a>, <a href="https://publications.waset.org/abstracts/search?q=Samer%20Hanoun"> Samer Hanoun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GME′s velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use, and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks, including their tendency to generate longer routes when the obstacles are sideways of the drone′s route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilized on most types of drones that have basic distance measurement sensors and autopilot-supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS-supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drones" title="drones">drones</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20field%20methods" title=" force field methods"> force field methods</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=path%20planning" title=" path planning"> path planning</a> </p> <a href="https://publications.waset.org/abstracts/172301/drone-on-time-obstacle-avoidance-for-static-and-dynamic-obstacles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172301.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">93</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">5914</span> An Improved Dynamic Window Approach with Environment Awareness for Local Obstacle Avoidance of Mobile Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baoshan%20Wei">Baoshan Wei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuai%20Han"> Shuai Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Xing%20Zhang"> Xing Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Local obstacle avoidance is critical for mobile robot navigation. It is a challenging task to ensure path optimality and safety in cluttered environments. We proposed an Environment Aware Dynamic Window Approach in this paper to cope with the issue. The method integrates environment characterization into Dynamic Window Approach (DWA). Two strategies are proposed in order to achieve the integration. The local goal strategy guides the robot to move through openings before approaching the final goal, which solves the local minima problem in DWA. The adaptive control strategy endows the robot to adjust its state according to the environment, which addresses path safety compared with DWA. Besides, the evaluation shows that the path generated from the proposed algorithm is safer and smoother compared with state-of-the-art algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20control" title="adaptive control">adaptive control</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20window%20approach" title=" dynamic window approach"> dynamic window approach</a>, <a href="https://publications.waset.org/abstracts/search?q=environment%20aware" title=" environment aware"> environment aware</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20obstacle%20avoidance" title=" local obstacle avoidance"> local obstacle avoidance</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/102425/an-improved-dynamic-window-approach-with-environment-awareness-for-local-obstacle-avoidance-of-mobile-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102425.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">159</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">5913</span> Model of Obstacle Avoidance on Hard Disk Drive Manufacturing with Distance Constraint </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rawinun%20Praserttaweelap">Rawinun Praserttaweelap</a>, <a href="https://publications.waset.org/abstracts/search?q=Somyot%20Kiatwanidvilai"> Somyot Kiatwanidvilai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Obstacle avoidance is the one key for the robot system in unknown environment. The robots should be able to know their position and safety region. This research starts on the path planning which are SLAM and AMCL in ROS system. In addition, the best parameters of the obstacle avoidance function are required. In situation on Hard Disk Drive Manufacturing, the distance between robots and obstacles are very serious due to the manufacturing constraint. The simulations are accomplished by the SLAM and AMCL with adaptive velocity and safety region calculation. <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=OA" title=" OA"> OA</a>, <a href="https://publications.waset.org/abstracts/search?q=Simultaneous%20Localization%20and%20Mapping" title=" Simultaneous Localization and Mapping"> Simultaneous Localization and Mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=SLAM" title=" SLAM"> SLAM</a>, <a href="https://publications.waset.org/abstracts/search?q=Adaptive%20Monte%20Carlo%20Localization" title=" Adaptive Monte Carlo Localization"> Adaptive Monte Carlo Localization</a>, <a href="https://publications.waset.org/abstracts/search?q=AMCL" title=" AMCL"> AMCL</a>, <a href="https://publications.waset.org/abstracts/search?q=KLD%20sampling" title=" KLD sampling"> KLD sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=KLD" title=" KLD"> KLD</a> </p> <a href="https://publications.waset.org/abstracts/87279/model-of-obstacle-avoidance-on-hard-disk-drive-manufacturing-with-distance-constraint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87279.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">198</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">5912</span> Real Time Adaptive Obstacle Avoidance in Dynamic Environments with Different D-S</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi">Mohammad Javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi"> Farhad Asadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a real-time obstacle avoidance approach for both autonomous and non-autonomous dynamical systems (DS) is presented. In this approach the original dynamics of the controller which allow us to determine safety margin can be modulated. Different common types of DS increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle especially when robot moves very fast in changeable complex environments. The method is validated by simulation and influence of different autonomous and non-autonomous DS such as important characteristics of limit cycles and unstable DS. Furthermore, the position of different obstacles in complex environment is explained. Finally, the verification of avoidance trajectories is described through different parameters such as safety factor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=limit%20cycles" title="limit cycles">limit cycles</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20dynamical%20system" title=" nonlinear dynamical system"> nonlinear dynamical system</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20obstacle%20avoidance" title=" real time obstacle avoidance"> real time obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=safety%20margin" title=" safety margin"> safety margin</a> </p> <a href="https://publications.waset.org/abstracts/18168/real-time-adaptive-obstacle-avoidance-in-dynamic-environments-with-different-d-s" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18168.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">5911</span> Collision Avoidance Based on Model Predictive Control for Nonlinear Octocopter Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Do%C4%9Fan%20Y%C4%B1ld%C4%B1z">Doğan Yıldız</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydan%20M%C3%BC%C5%9Ferref%20Erkmen"> Aydan Müşerref Erkmen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The controller of the octocopter is mostly based on the PID controller. For complex maneuvers, PID controllers have limited performance capability like in collision avoidance. When an octocopter needs avoidance from an obstacle, it must instantly show an agile maneuver. Also, this kind of maneuver is affected severely by the nonlinear characteristic of octocopter. When these kinds of limitations are considered, the situation is highly challenging for the PID controller. In the proposed study, these challenges are tried to minimize by using the model predictive controller (MPC) for collision avoidance with a nonlinear octocopter model. The aim is to show that MPC-based collision avoidance has the capability to deal with fast varying conditions in case of obstacle detection and diminish the nonlinear effects of octocopter with varying disturbances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20octocopter%20model" title=" nonlinear octocopter model"> nonlinear octocopter model</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=obstacle%20detection" title=" obstacle detection"> obstacle detection</a> </p> <a href="https://publications.waset.org/abstracts/150063/collision-avoidance-based-on-model-predictive-control-for-nonlinear-octocopter-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150063.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">191</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">5910</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">5909</span> Simulation of Obstacle Avoidance for Multiple Autonomous Vehicles in a Dynamic Environment Using Q-Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andreas%20D.%20Jansson">Andreas D. Jansson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title="autonomous vehicles">autonomous vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%204.0" title=" industry 4.0"> industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</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=Q-learning" title=" Q-learning"> Q-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/132508/simulation-of-obstacle-avoidance-for-multiple-autonomous-vehicles-in-a-dynamic-environment-using-q-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132508.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">138</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">5908</span> Real-Time Adaptive Obstacle Avoidance with DS Method and the Influence of Dynamic Environments Change on Different DS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Mahjoub%20Moghadas">Saeed Mahjoub Moghadas</a>, <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi"> Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahed%20Torkamandi"> Shahed Torkamandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Moradi"> Hassan Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmood%20Purgamshidian"> Mahmood Purgamshidian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present real-time obstacle avoidance approach for both autonomous and non-autonomous DS-based controllers and also based on dynamical systems (DS) method. In this approach, we can modulate the original dynamics of the controller and it allows us to determine safety margin and different types of DS to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle and especially when robot moves very fast in changeable complex environments. The method is validated in simulation and influence of different autonomous and non-autonomous DS such as limit cycles, and unstable DS on this algorithm and also the position of different obstacles in complex environment is explained. Finally, we describe how the avoidance trajectories can be verified through different parameters such as safety factor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=limit%20cycles" title="limit cycles">limit cycles</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20dynamical%20system" title=" nonlinear dynamical system"> nonlinear dynamical system</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20obstacle%20avoidance" title=" real time obstacle avoidance"> real time obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=DS-based%20controllers" title=" DS-based controllers"> DS-based controllers</a> </p> <a href="https://publications.waset.org/abstracts/13882/real-time-adaptive-obstacle-avoidance-with-ds-method-and-the-influence-of-dynamic-environments-change-on-different-ds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13882.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">389</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">5907</span> Q-Learning of Bee-Like Robots Through Obstacle Avoidance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jawairia%20Rasheed">Jawairia Rasheed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modern robots are often used for search and rescue purpose. One of the key areas of interest in such cases is learning complex environments. One of the key methodologies for robots in such cases is reinforcement learning. In reinforcement learning robots learn to move the path to reach the goal while avoiding obstacles. Q-learning, one of the most advancement of reinforcement learning is used for making the robots to learn the path. Robots learn by interacting with the environment to reach the goal. In this paper simulation model of bee-like robots is implemented in NETLOGO. In the start the learning rate was less and it increased with the passage of time. The bees successfully learned to reach the goal while avoiding obstacles through Q-learning technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforlearning%20of%20bee%20like%20robots%20for%20reaching%20the%20goalcement%20learning%20for%20randomly%20placed%20obstacles" title="reinforlearning of bee like robots for reaching the goalcement learning for randomly placed obstacles">reinforlearning of bee like robots for reaching the goalcement learning for randomly placed obstacles</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance%20through%20q-learning" title=" obstacle avoidance through q-learning"> obstacle avoidance through q-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=q-learning%20for%20obstacle%20avoidance" title=" q-learning for obstacle avoidance"> q-learning for obstacle avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=" title=""></a> </p> <a href="https://publications.waset.org/abstracts/155154/q-learning-of-bee-like-robots-through-obstacle-avoidance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155154.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">103</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">5906</span> An Online Priority-Configuration Algorithm for Obstacle Avoidance of the Unmanned Air Vehicles Swarm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lihua%20Zhu">Lihua Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianfeng%20Du"> Jianfeng Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Wang"> Yu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiqiang%20Wu"> Zhiqiang Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collision avoidance problems of a swarm of unmanned air vehicles (UAVs) flying in an obstacle-laden environment are investigated in this paper. Given that the UAV swarm needs to adapt to the obstacle distribution in dynamic operation, a priority configuration is designed to guide the UAVs to pass through the obstacles in turn. Based on the collision cone approach and the prediction of the collision time, a collision evaluation model is established to judge the urgency of the imminent collision of each UAV, and the evaluation result is used to assign the priority of each UAV to further instruct them going through the obstacles in descending order. At last, the simulation results provide the promising validation in terms of the efficiency and scalability of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UAV%20swarm" title="UAV swarm">UAV swarm</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=complex%20environment" title=" complex environment"> complex environment</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20priority%20design" title=" online priority design"> online priority design</a> </p> <a href="https://publications.waset.org/abstracts/93689/an-online-priority-configuration-algorithm-for-obstacle-avoidance-of-the-unmanned-air-vehicles-swarm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93689.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">214</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">5905</span> Performance Analysis of Vision-Based Transparent Obstacle Avoidance for Construction Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siwei%20Chang">Siwei Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Heng%20Li"> Heng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Haitao%20Wu"> Haitao Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Fang"> Xin Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Construction robots are receiving more and more attention as a promising solution to the manpower shortage issue in the construction industry. The development of intelligent control techniques that assist in controlling the robots to avoid transparency and reflected building obstacles is crucial for guaranteeing the adaptability and flexibility of mobile construction robots in complex construction environments. With the boom of computer vision techniques, a number of studies have proposed vision-based methods for transparent obstacle avoidance to improve operation accuracy. However, vision-based methods are also associated with disadvantages such as high computational costs. To provide better perception and value evaluation, this study aims to analyze the performance of vision-based techniques for avoiding transparent building obstacles. To achieve this, commonly used sensors, including a lidar, an ultrasonic sensor, and a USB camera, are equipped on the robotic platform to detect obstacles. A Raspberry Pi 3 computer board is employed to compute data collecting and control algorithms. The turtlebot3 burger is employed to test the programs. On-site experiments are carried out to observe the performance in terms of success rate and detection distance. Control variables include obstacle shapes and environmental conditions. The findings contribute to demonstrating how effectively vision-based obstacle avoidance strategies for transparent building obstacle avoidance and provide insights and informed knowledge when introducing computer vision techniques in the aforementioned domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20robot" title="construction robot">construction robot</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=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=transparent%20obstacle" title=" transparent obstacle"> transparent obstacle</a> </p> <a href="https://publications.waset.org/abstracts/165433/performance-analysis-of-vision-based-transparent-obstacle-avoidance-for-construction-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165433.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">80</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">5904</span> Beyond the Beep: Optimizing Flight Controller Performance for Reliable Ultrasonic Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raunak%20Munjal">Raunak Munjal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Akif%20Ali"> Mohammad Akif Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Prithiv%20Raj"> Prithiv Raj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ultrasonic%20distance%20measurement" title="ultrasonic distance measurement">ultrasonic distance measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy%20and%20consistency" title=" accuracy and consistency"> accuracy and consistency</a>, <a href="https://publications.waset.org/abstracts/search?q=flight%20controller%20comparisons" title=" flight controller comparisons"> flight controller comparisons</a>, <a href="https://publications.waset.org/abstracts/search?q=ESP32%20vs%20arduino%20nano" title=" ESP32 vs arduino nano"> ESP32 vs arduino nano</a> </p> <a href="https://publications.waset.org/abstracts/183773/beyond-the-beep-optimizing-flight-controller-performance-for-reliable-ultrasonic-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183773.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">58</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">5903</span> Collision Avoidance Maneuvers for Vessels Navigating through Traffic Separation Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aswin%20V.%20J.">Aswin V. J.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sreeja%20%20S."> Sreeja S.</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Harikumar"> R. Harikumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ship collision is one of the major concerns while navigating in the ocean. In congested sea routes where there are hectic offshore operations, ships are often forced to take close encounter maneuvers. Maritime rules for preventing collision at sea are defined in the International Regulations for Preventing Collision at Sea. Traffic Separation Schemes (TSS) are traffic management route systems ruled by International Maritime Organization (IMO), where the traffic lanes indicate the general direction of traffic flow. The Rule 10 of International Regulations for Preventing Collision at Sea prescribes the conduct of vessels while navigating through TSS. But no quantitative criteria regarding the procedures to detect and evaluate collision risk is specified in International Regulations for Preventing Collision at Sea. Most of the accidents that occur are due to operational errors affected by human factors such as lack of experience and loss of situational awareness. In open waters, the traffic density is less when compared to that in TSS, and hence the vessels can be operated in autopilot mode. A collision avoidance method that uses the possible obstacle trajectories in advance to predict “collision occurrence” and can generate suitable maneuvers for collision avoidance is presented in this paper. The suitable course and propulsion changes that can be used in a TSS considering International Regulations for Preventing Collision at Sea are found out for various obstacle scenarios. <p class="card-text"><strong>Keywords:</strong> <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=maneuvers" title=" maneuvers"> maneuvers</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20trajectories" title=" obstacle trajectories"> obstacle trajectories</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20separation%20scheme" title=" traffic separation scheme"> traffic separation scheme</a> </p> <a href="https://publications.waset.org/abstracts/145379/collision-avoidance-maneuvers-for-vessels-navigating-through-traffic-separation-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145379.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">77</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">5902</span> Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hirofumi%20Miyajima">Hirofumi Miyajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuya%20Kishida"> Kazuya Kishida</a>, <a href="https://publications.waset.org/abstracts/search?q=Noritaka%20Shigei"> Noritaka Shigei</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiromi%20Miyajima"> Hiromi Miyajima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Box-Jenkins%27s%20problem" title="Box-Jenkins's problem">Box-Jenkins's problem</a>, <a href="https://publications.waset.org/abstracts/search?q=double-input%20rule%20module" title=" double-input rule module"> double-input rule module</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20model" title=" fuzzy inference model"> fuzzy inference model</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=single-input%20rule%20module" title=" single-input rule module"> single-input rule module</a> </p> <a href="https://publications.waset.org/abstracts/46971/learning-algorithms-for-fuzzy-inference-systems-composed-of-double-and-single-input-rule-modules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46971.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">5901</span> A Diagnostic Comparative Analysis of on Simultaneous Localization and Mapping (SLAM) Models for Indoor and Outdoor Route Planning and Obstacle Avoidance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Esmail%20Seyedi%20Bariran">Seyed Esmail Seyedi Bariran</a>, <a href="https://publications.waset.org/abstracts/search?q=Khairul%20Salleh%20Mohamed%20Sahari"> Khairul Salleh Mohamed Sahari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=route%20planning" title="route planning">route planning</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle" title=" obstacle"> obstacle</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation%20performance" title=" estimation performance"> estimation performance</a>, <a href="https://publications.waset.org/abstracts/search?q=FastSLAM" title=" FastSLAM"> FastSLAM</a>, <a href="https://publications.waset.org/abstracts/search?q=L-SLAM" title=" L-SLAM"> L-SLAM</a>, <a href="https://publications.waset.org/abstracts/search?q=GraphSLAM" title=" GraphSLAM"> GraphSLAM</a>, <a href="https://publications.waset.org/abstracts/search?q=Grid%20SLAM" title=" Grid SLAM"> Grid SLAM</a>, <a href="https://publications.waset.org/abstracts/search?q=DP-SLAM" title=" DP-SLAM"> DP-SLAM</a> </p> <a href="https://publications.waset.org/abstracts/13160/a-diagnostic-comparative-analysis-of-on-simultaneous-localization-and-mapping-slam-models-for-indoor-and-outdoor-route-planning-and-obstacle-avoidance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13160.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">444</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">5900</span> Limit-Cycles Method for the Navigation and Avoidance of Any Form of Obstacles for Mobile Robots in Cluttered Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Boufera">F. Boufera</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Debbat"> F. Debbat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with an approach based on limit-cycles method for the problem of obstacle avoidance of mobile robots in unknown environments for any form of obstacles. The purpose of this approach is the improvement of limit-cycles method in order to obtain safe and ﬂexible navigation. The proposed algorithm has been successfully tested in diﬀerent conﬁguration on simulation. <p class="card-text"><strong>Keywords:</strong> <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=navigation" title=" navigation"> navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=avoidance%20of%20obstacles" title=" avoidance of obstacles"> avoidance of obstacles</a>, <a href="https://publications.waset.org/abstracts/search?q=limit-cycles%20method" title=" limit-cycles method"> limit-cycles method</a> </p> <a href="https://publications.waset.org/abstracts/21940/limit-cycles-method-for-the-navigation-and-avoidance-of-any-form-of-obstacles-for-mobile-robots-in-cluttered-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21940.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">429</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">5899</span> Study and Construction on Signalling System during Reverse Motion Due to Obstacle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yasir%20Arafat">S. M. Yasir Arafat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Driving models are needed by many researchers to improve traffic safety and to advance autonomous vehicle design. To be most useful, a driving model must state specifically what information is needed and how it is processed. So we developed an “Obstacle Avoidance and Detection Autonomous Car” based on sensor application. The ever increasing technological demands of today call for very complex systems, which in turn require highly sophisticated controllers to ensure that high performance can be achieved and maintained under adverse conditions. Based on a developed model of brakes operation, the controller of braking system operation has been designed. It has a task to enable solution to the problem of the better controlling of braking system operation in a more accurate way then it was the case now a day. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automobile" title="automobile">automobile</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle" title=" obstacle"> obstacle</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a>, <a href="https://publications.waset.org/abstracts/search?q=sensing" title=" sensing"> sensing</a> </p> <a href="https://publications.waset.org/abstracts/32977/study-and-construction-on-signalling-system-during-reverse-motion-due-to-obstacle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32977.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">364</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">5898</span> Design, Analysis and Obstacle Avoidance Control of an Electric Wheelchair with Sit-Sleep-Seat Elevation Functions </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Waleed%20Ahmed">Waleed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Xiaohua"> Huang Xiaohua</a>, <a href="https://publications.waset.org/abstracts/search?q=Wilayat%20Ali"> Wilayat Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The wheelchair users are generally exposed to physical and psychological health problems, e.g., pressure sores and pain in the hip joint, associated with seating posture or being inactive in a wheelchair for a long time. Reclining Wheelchair with back, thigh, and leg adjustment helps in daily life activities and health preservation. The seat elevating function of an electric wheelchair allows the user (lower limb amputation) to reach different heights. An electric wheelchair is expected to ease the lives of the elderly and disable people by giving them mobility support and decreasing the percentage of accidents caused by users’ narrow sight or joystick operation errors. Thus, this paper proposed the design, analysis and obstacle avoidance control of an electric wheelchair with sit-sleep-seat elevation functions. A 3D model of a wheelchair is designed in SolidWorks that was later used for multi-body dynamic (MBD) analysis and to verify driving control system. The control system uses the fuzzy algorithm to avoid the obstacle by getting information in the form of distance from the ultrasonic sensor and user-specified direction from the joystick’s operation. The proposed fuzzy driving control system focuses on the direction and velocity of the wheelchair. The wheelchair model has been examined and proven in MSC Adams (Automated Dynamic Analysis of Mechanical Systems). The designed fuzzy control algorithm is implemented on Gazebo robotic 3D simulator using Robotic Operating System (ROS) middleware. The proposed wheelchair design enhanced mobility and quality of life by improving the user’s functional capabilities. Simulation results verify the non-accidental behavior of the electric wheelchair. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic%20control" title="fuzzy logic control">fuzzy logic control</a>, <a href="https://publications.waset.org/abstracts/search?q=joystick" title=" joystick"> joystick</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20body%20dynamics" title=" multi body dynamics"> multi body dynamics</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=scissor%20mechanism" title=" scissor mechanism"> scissor mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a> </p> <a href="https://publications.waset.org/abstracts/134367/design-analysis-and-obstacle-avoidance-control-of-an-electric-wheelchair-with-sit-sleep-seat-elevation-functions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134367.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">129</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">5897</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">5896</span> Design and Implementation of Neural Network Based Controller for Self-Driven Vehicle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassam%20Muazzam">Hassam Muazzam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper devises an autonomous self-driven vehicle that is capable of taking a disabled person to his/her desired location using three different power sources (gasoline, solar, electric) without any control from the user, avoiding the obstacles in the way. The GPS co-ordinates of the desired location are sent to the main processing board via a GSM module. After the GPS co-ordinates are sent, the path to be followed by the vehicle is devised by Pythagoras theorem. The distance and angle between the present location and the desired location is calculated and then the vehicle starts moving in the desired direction. Meanwhile real-time data from ultrasonic sensors is fed to the board for obstacle avoidance mechanism. Ultrasonic sensors are used to quantify the distance of the vehicle from the object. The distance and position of the object is then used to make decisions regarding the direction of vehicle in order to avoid the obstacles using artificial neural network which is implemented using ATmega1280. Also the vehicle provides the feedback location at remote location. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20self-driven%20vehicle" title="autonomous self-driven vehicle">autonomous self-driven vehicle</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=desired%20location" title=" desired location"> desired location</a>, <a href="https://publications.waset.org/abstracts/search?q=pythagoras%20theorem" title=" pythagoras theorem"> pythagoras theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20location" title=" remote location"> remote location</a> </p> <a href="https://publications.waset.org/abstracts/39101/design-and-implementation-of-neural-network-based-controller-for-self-driven-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39101.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">409</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">5895</span> Numerical Study of Heat Transfer and Laminar Flow over a Backward Facing Step with and without Obstacle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussein%20Togun">Hussein Togun</a>, <a href="https://publications.waset.org/abstracts/search?q=Tuqa%20Abdulrazzaq"> Tuqa Abdulrazzaq</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20N.%20Kazi"> S. N. Kazi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Badarudin"> A. Badarudin</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20A.%20Ariffin"> M. K. A. Ariffin</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20M.%20Zubir"> M. N. M. Zubir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heat transfer and laminar fluid flow over backward facing step with and without obstacle numerically studied in this paper. The finite volume method adopted to solve continuity, momentum and energy equations in two dimensions. Backward facing step without obstacle and with different dimension of obstacle were presented. The step height and expansion ratio of channel were 4.8mm and 2 respectively, the range of Reynolds number varied from 75 to 225, constant heat flux subjected on downstream of wall was 2000W/m2, and length of obstacle was 1.5, 3, and 4.5mm with width 1.5mm. The separation length noticed increase with increase Reynolds number and height of obstacle. The result shows increase of heat transfer coefficient for backward facing step with obstacle in compared to those without obstacle. The maximum enhancement of heat transfer observed at 4.5mm of height obstacle due to increase recirculation flow after the obstacle in addition that at backward. Streamline of velocity showing the increase of recirculation region with used obstacle in compared without obstacle and highest recirculation region observed at obstacle height 4.5mm. The amount of enhancement heat transfer was varied between 3-5% compared to backward without obstacle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=separation%20flow" title="separation flow">separation flow</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20facing%20step" title=" backward facing step"> backward facing step</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=laminar%20flow" title=" laminar flow"> laminar flow</a> </p> <a href="https://publications.waset.org/abstracts/5254/numerical-study-of-heat-transfer-and-laminar-flow-over-a-backward-facing-step-with-and-without-obstacle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5254.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">469</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">5894</span> Hybrid Control Mode Based on Multi-Sensor Information by Fuzzy Approach for Navigation Task of Autonomous Mobile Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonqlan%20Lin">Jonqlan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Y.%20Tasi"> C. Y. Tasi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20H.%20Lin"> K. H. Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the issue of the autonomous mobile robot (AMR) navigation task based on the hybrid control modes. The novel hybrid control mode, based on multi-sensors information by using the fuzzy approach, has been presented in this research. The system operates in real time, is robust, enables the robot to operate with imprecise knowledge, and takes into account the physical limitations of the environment in which the robot moves, obtaining satisfactory responses for a large number of different situations. An experiment is simulated and carried out with a pioneer mobile robot. From the experimental results, the effectiveness and usefulness of the proposed AMR obstacle avoidance and navigation scheme are confirmed. The experimental results show the feasibility, and the control system has improved the navigation accuracy. The implementation of the controller is robust, has a low execution time, and allows an easy design and tuning of the fuzzy knowledge base. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20mobile%20robot" title="autonomous mobile robot">autonomous mobile robot</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=MEMS" title=" MEMS"> MEMS</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20control%20mode" title=" hybrid control mode"> hybrid control mode</a>, <a href="https://publications.waset.org/abstracts/search?q=navigation%20control" title=" navigation control"> navigation control</a> </p> <a href="https://publications.waset.org/abstracts/26893/hybrid-control-mode-based-on-multi-sensor-information-by-fuzzy-approach-for-navigation-task-of-autonomous-mobile-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26893.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">466</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">5893</span> Super-ellipsoidal Potential Function for Autonomous Collision Avoidance of a Teleoperated UAV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Qasim">Mohammed Qasim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyoung-Dae%20Kim"> Kyoung-Dae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the design of the super-ellipsoidal potential function (SEPF), that can be used for autonomous collision avoidance of an unmanned aerial vehicle (UAV) in a 3-dimensional space. In the design of SEPF, we have the full control over the shape and size of the potential function. In particular, we can adjust the length, width, height, and the amount of flattening at the tips of the potential function so that the collision avoidance motion vector generated from the potential function can be adjusted accordingly. Based on the idea of the SEPF, we also propose an approach for the local autonomy of a UAV for its collision avoidance when the UAV is teleoperated by a human operator. In our proposed approach, a teleoperated UAV can not only avoid collision autonomously with other surrounding objects but also track the operator’s control input as closely as possible. As a result, an operator can always be in control of the UAV for his/her high-level guidance and navigation task without worrying too much about the UAVs collision avoidance while it is being teleoperated. The effectiveness of the proposed approach is demonstrated through a human-in-the-loop simulation of quadrotor UAV teleoperation using virtual robot experimentation platform (v-rep) and Matlab programs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20potential%20function" title="artificial potential function">artificial potential function</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20collision%20avoidance" title=" autonomous collision avoidance"> autonomous collision avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=teleoperation" title=" teleoperation"> teleoperation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrotor" title=" quadrotor"> quadrotor</a> </p> <a href="https://publications.waset.org/abstracts/42043/super-ellipsoidal-potential-function-for-autonomous-collision-avoidance-of-a-teleoperated-uav" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42043.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">399</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">5892</span> Obstacle Classification Method Based on 2D LIDAR Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moohyun%20Lee">Moohyun Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Soojung%20Hur"> Soojung Hur</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongwan%20Park"> Yongwan Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obstacle" title="obstacle">obstacle</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=LIDAR" title=" LIDAR"> LIDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=intensity" title=" intensity"> intensity</a> </p> <a href="https://publications.waset.org/abstracts/11838/obstacle-classification-method-based-on-2d-lidar-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11838.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">349</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">5891</span> Corporate Social Responsibility, Earnings, and Tax Avoidance: Evidence from Indonesia </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cahyaningsih%20Cahyaningsih">Cahyaningsih Cahyaningsih</a>, <a href="https://publications.waset.org/abstracts/search?q=Fu%27ad%20Rakhman"> Fu'ad Rakhman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines empirically the association between corporate social responsibility (CSR) and tax avoidance. This study also investigates the effect of earnings on the relation between CSR and tax avoidance. Effective tax rate (ETR) and cash effective tax rate (CETR) were used to measure tax avoidance. Corporate social responsibility fund (CSRF) and corporate social responsibility disclosure (CSRD) were used as proxies for CSR. Test was conducted for public firms which were listed in the Indonesia Stock Exchange during the period of 2011-2014. Based on slack resource theory, this study finds that the relation between CSR and tax avoidance is moderated by earnings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20social%20responsibility%20disclosure" title="corporate social responsibility disclosure">corporate social responsibility disclosure</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20social%20responsibility%20fund" title=" corporate social responsibility fund"> corporate social responsibility fund</a>, <a href="https://publications.waset.org/abstracts/search?q=earnings" title=" earnings"> earnings</a>, <a href="https://publications.waset.org/abstracts/search?q=tax%20avoidance" title=" tax avoidance"> tax avoidance</a> </p> <a href="https://publications.waset.org/abstracts/57745/corporate-social-responsibility-earnings-and-tax-avoidance-evidence-from-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57745.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">279</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">5890</span> Tax Avoidance and Leadership Replacement: Moderating Influence of Ownership and Political Connections</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radwan%20Hussien%20Alkebsee">Radwan Hussien Alkebsee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under the argument that reputational costs deter firms from engaging in tax avoidance activities, this paper investigates the relationship between tax avoidance and forced CEO turnover. This study is based on a broad sample of Chinese listed companies spanning the period 2011 to 2018. The findings reveal that tax avoidance is positively associated with forced CEO turnover. This suggests that firms that engage in tax avoidance experience a high rate of leadership replacement. The findings also reveal that the positive association between tax avoidance and forced CEO turnover is pronounced for state-owned firms, firms with no political connections, and firms located in “more developed” regions with extensive tax enforcement action, while it is not for private firms, firms with political connections, and firms located in “less developed” regions with weak tax enforcement actions. The baseline results remain consistent and robust for endogeneity concerns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tax%20avoidance" title="tax avoidance">tax avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=CEO%20turnover" title=" CEO turnover"> CEO turnover</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20connections" title=" political connections"> political connections</a>, <a href="https://publications.waset.org/abstracts/search?q=regional%20tax%20enforcement" title=" regional tax enforcement"> regional tax enforcement</a>, <a href="https://publications.waset.org/abstracts/search?q=China" title=" China"> China</a> </p> <a href="https://publications.waset.org/abstracts/150110/tax-avoidance-and-leadership-replacement-moderating-influence-of-ownership-and-political-connections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150110.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">152</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">5889</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">5888</span> The Effect of Tax Avoidance on Firm Value: Evidence from Amman Stock Exchange</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Abu%20Nassar">Mohammad Abu Nassar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Al%20Khalilah"> Mahmoud Al Khalilah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussein%20Abu%20Nassar"> Hussein Abu Nassar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to examine whether corporate tax avoidance practices can impact firm value in the Jordanian context. The study employs a quantitative approach using s sample of (124) industrial and services companies listed on the Amman Stock Exchange for the period from 2010 to 2019. Multiple linear regression analysis has been applied to test the study's hypothesis. The study employs effective tax rate and book-tax difference to measure tax avoidance and Tobin's Q factor to measure firm value. The results of the study revealed that tax avoidance practices, when measured using effective tax rates, do not significantly impact firm value. When the book-tax difference is used to measure tax avoidance, the study results showed a negative impact on firm value. The result of the study has not supported the traditional view of tax avoidance as a transfer of wealth from the government to shareholders for industrial and services companies listed on the Amman Stock Exchange, indicating that Jordanian firms should not use tax avoidance strategies to enhance their value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tax%20avoidance" title="tax avoidance">tax avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20tax%20rate" title=" effective tax rate"> effective tax rate</a>, <a href="https://publications.waset.org/abstracts/search?q=book-tax%20difference" title=" book-tax difference"> book-tax difference</a>, <a href="https://publications.waset.org/abstracts/search?q=firm%20value" title=" firm value"> firm value</a>, <a href="https://publications.waset.org/abstracts/search?q=Amman%20stock%20exchange" title=" Amman stock exchange"> Amman stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/151892/the-effect-of-tax-avoidance-on-firm-value-evidence-from-amman-stock-exchange" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151892.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> <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=local%20obstacle%20avoidance&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=local%20obstacle%20avoidance&page=3">3</a></li> <li class="page-item"><a class="page-link" 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