Obstacle Avoidance Trajectory Planning for Robotic Arm based on Genetic Algorithm
Masood Usama1, Xianhua Li2, Haohao Yu3, Yamin Iqra4
1Masood Usama, School of Mechanical Engineering, Anhui University of Science and Technology, China.
2Xianhua Li, School of Mechanical Engineering, Anhui University of Science and Technology, School of Artificial Intelligence, Anhui University of Science and Technology, China.
3Haohao Yu, School of Mechanical Engineering, Anhui University of Science and Technology, China.
4Yamin Iqra, School of Computer science and Engineering, Anhui University of Science and Technology, China.
Manuscript received on 11 October 2023 | Revised Manuscript received on 14 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 20-25 | Volume-12 Issue-4, November 2023 | Retrieval Number: 100.1/ijrte.D79461112423 | DOI: 10.35940/ijrte.D7946.1112423
Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: DFor the problem of obstacle avoidance trajectory planning of a robot arm, a robot arm obstacle avoidance method based on a genetic algorithm is proposed. It is based on the two problems that the motion process can avoid obstacles and the motion process is more stable and efficient. First, the motion of each joint is planned as a sixth-degree polynomial, and the coefficients of the sixth-degree term are set as the pending parameters, and the motion of each joint is changed by changing the pending parameters. Then, the fitness function is then constructed by calculating the collision detection, angular velocity limit detection, acceleration limit detection, and the total trajectory length and rotation angle for each joint. Finally, the fitness function is optimised using a genetic algorithm to obtain smooth, continuous, and collision-free trajectories. Matlab simulation experiments show that this method can obtain the optimal or suboptimal trajectory without collision.
Keywords: Genetic Algorithm; Obstacle Avoidance; Robotic Arm; Trajectory Planning
Scope of the Article: Robotic