Picking Path Optimization of Mobile Robotic Arm Based on Differential Evolution and Improved A* Algorithm - Liang Chen (IEEE, 2021)
With the development of artificial intelligence technology, robotic arm picking and path finding based on AI technology has attracted more and more attention. How to use the path search algorithm in a relatively short time is a huge challenge in the optimization of robot picking path. Most of the existing path finding methods based on the A* algorithm have the problems of long search time and non-optimal path. Most of them achieve poor results in the optimization of robotic arm picking paths. This paper proposes an AI intelligent path finding method based on differential evolution and improved A* algorithm. The method mainly achieves fast path search by accurately filtering abnormal conditions to achieve a higher convergence rate. The experimental results show that the method proposed in this paper has faster search speed and better path selection than the existing methods. The AI intelligent path finding method based on differential evolution and improved A* algorithm for robotic arm picking is feasible and effective.