Obstacle Avoidance Techniques for Robot Path Planning

https://doi.org/10.24237/djes.2019.12107

Authors

  • Arif A. AL-Qassar Department of Control and system Engineering, University, of Technology, Iraq
  • Ali N. Abdulnabi Department of Control and system Engineering, College of Engineering, University, of Technology, Iraq

Keywords:

robot path planning, obstacle avoidance, Bézier curve, A-star algorithm

Abstract

This paper presents a collision-free path planning approaches based on Bézier curve and A-star algorithm for robot manipulator system. The main problem of this work is to finding a feasible collision path planning from initial point to final point to transport the robot arm from the preliminary to the very last within the presence of obstacles, a sequence of joint angles alongside the path have to be determined. To solve this problem several algorithms have been presented among which it can be mention such as Bug algorithms, A-Star algorithms, potential field algorithms, Bézier curve algorithm and intelligent algorithms. In this paper obstacle avoidance algorithms were proposed Bézier and A-Star algorithms, through theoretical studies and simulations with several different cases, it's found verify the effectiveness of the methods suggested. It's founded the Bézier algorithm is smoothing accurate, and effective as compare with the A-star algorithm, but A-star is near to shortest and optimal path to free collision avoidance. The time taken and the elapsed time to traverse from its starting position and to reach the goal are recorded the tabulated results show that the elapsed time with different cases to traverse from the start location to destination using A-star Algorithm is much less as compared to the time taken by the robot using Bézier Algorithm to trace the same path. The robot used was the Lab-Volt of 5DOF Servo Robot System Model 5250 (RoboCIM5250)

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Published

2019-03-01

How to Cite

[1]
Arif A. AL-Qassar and A. N. Abdulnabi, “Obstacle Avoidance Techniques for Robot Path Planning”, DJES, vol. 12, no. 1, pp. 56–65, Mar. 2019.