Mechanical Arm Path Planning with Informed-RRT* Algorithm Incorporating Improved Artificial Potential Field
Aiming at the problems of poor targeting,long exploration time,low iteration efficiency and poor path quality of the Informed-RRT* algorithm,a mechanical arm path planning algorithm incorporating the Informed-RRT* algorithm with improved artificial potential field is proposed.In random point exploration,an improved artificial potential field method that introduces a new repulsive field force function is proposed to guide the expansion of random points and limit the randomness of the path direction.In path expansion,an adaptive step-size method is proposed to judge by the angle θ of the triangle constituted by the sampling points,the optimal parent node,and the target point,and the expansion is carried out by using different step-sizes to shorten the exploration time.In the process of path optimization,ellipsoid subset sampling to improve the iteration efficiency and final path quality.The results show that the designed IAPF-IRRT* reduces the planning time of the algorithm by 31.56%,the path length by 9.23%,and the node utilization by 24.23%compared with the Informed-RRT*algorithm,and the searching efficiency of the algorithm is significantly improved and optimized.After the generated path is imported into the mechanical arm model,the mechanical arm is able to complete obstacle avoidance and run smoothly to the target point.
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