With the development of intelligent control technology,mobile robot obstacle avoidance planning has been widely used in intelligent manufacturing.In order to make up for the problem of rapid convergence of ant colony(ACO)algorithm and local optimization,potential field jump points were introduced to optimize the ant colony algorithm,and the global pheromone concen-tration of the optimal path was updated and filled out.A mobile robot obstacle avoidance planning based on potential field jump point optimization ant Colony algorithm(PJPACO)was designed.The control flow of PJPACO is given,and simulation and ex-perimental analysis are carried out.The simulation results show that the PJPACO algorithm has fewer path inflection points,fast-er convergence speed and better path.Through experimental verification,it is found that PJPACO algorithm can obtain a shorter path finding time than ACO algorithm,and the robot can achieve a shorter walking path.The ACO algorithm built after adding PJP algorithm can plan the robot path more efficiently.This research has a very good practical guiding significance for improving the work efficiency of mobile robots,and can be extended to target recognition and other fields.
关键词
机器人避障/全局规划/人工势场法/路径优化
Key words
Robot Obstacle Avoidance/Global Planning/Artificial Potential Field Method/Path Optimization
维普
万方数据