Path planning of two robots in dynamic workspace based on potential field

Matoui, Fethi; Boussaid, Boumedyen; Abdelkrim, Mohamed Naceur

doi:10.1109/aset.2017.7983703

Cited by 2 publications

(2 citation statements)

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“…However, if the robot is far from the obstacle, the trajectory of motion of the robot is not affected. 37…”

Section: Principal Of the Artificial Potential Field Methodsmentioning

confidence: 99%

“…However, if the robot is far from the obstacle, the trajectory of motion of the robot is not affected. 37 In this article, qo j is the obstacle coordinate j (j = 1, 2,€ , M) and p(q, qoj) is the Euclidean distance between the robot and obstacle j. M is the number of obstacles. The expression of repulsive force produced by obstacle j and applied to the robot is presented in (21):…”

Section: Repulsive Potential Forcementioning

confidence: 99%

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Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach

2018

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The current study is set up to investigate the problem of planning the trajectories of a multi-robot system. It puts emphasis on the idea of using distributed architecture to plan the trajectories of a group of wheeled mobile robots. Each robot must be able to detect and avoid collision with both static and dynamic obstacles present in its environment/neighborhood. This study seeks to improve the artificial potential field (APF) method in order to have good trajectory planning of a multi-robot system. In order to address the purpose of this work, we employed the hybrid approach, which is a combination of three techniques: the APF method; the neighborhood system; and the notion of priority between the robots. Moreover, the minimum local problem is handled in this paper using the non-minimum speed algorithm. The implemented approach is adapted to solve the trajectory planning problem for a multi-robot system. So, the problem of intersection of robots at the same passage point is solved through using the method of assignment of priority between robots. We also used the neighborhood detection technique to reduce the influence area of each robot and to optimize the time of calculation. The overall system equations associated to the robots are updated at each time of simulation to react at any condition that might suddenly emerge. The approach is implemented with MATLAB/Simulink and Solidworks/Simmechanics.

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“…However, if the robot is far from the obstacle, the trajectory of motion of the robot is not affected. 37…”

Section: Principal Of the Artificial Potential Field Methodsmentioning

confidence: 99%

Section: Repulsive Potential Forcementioning

confidence: 99%