面向复杂环境的机械臂避障路径规划算法OA

In order to solve the problems of the obstacle avoidance path planning problem of robotic arms in complex environments,and high randomness,slow convergence and poor feasibility of the results of the rapidly expanding random tree(RRT)algorithm,various improvements were made to the traditional RRT algorithm.A probabilistic sampling strategy was introduced to intelligently guide the algorithm to search in the solution space and adapt more flexibly to the complexity of the problem.A greedy algorithm was employed to accelerate the convergence process of the algorithm and improve the optimization of the paths.In order to enhance the smoothness and continuity of the generated paths,cubic B-spline curve optimization was introduced to make the generated paths more consistent with the actual motion requirements.With these improvements,the time saving was about 7.73 s compared to the unimproved RRT when it was not used,and the path length was reduced by about 257.84 cm compared to the final path after the smoothing process.The time efficiency was improved by about 46%,and the path planning length was reduced by about 11.2%.