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Journal . 2023
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Obstacle Avoidance Trajectory Planning for Robotic Arm based on Genetic Algorithm

Authors: Masood Usama; Xianhua Li; Haohao Yu; Yamin Iqra;

Obstacle Avoidance Trajectory Planning for Robotic Arm based on Genetic Algorithm

Abstract

For 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.

Related Organizations
Keywords

Genetic Algorithm, Robotic Arm, Trajectory Planning, Obstacle Avoidance

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Top 10%
Average
Green
gold