采用改进RRT算法的机械臂高效路径规划OA

To address the limitations of the Rapidly-exploring random tree(RRT)algorithm and its variant,RRT*,in robotic arm path planning application,such as excessive path length,blind search,lengthy planning times,and unsmooth paths,this study proposes an improved RRT algorithm.To enhance sample guidance and search efficiency,the algorithm incorporates a target-bias sampling strategy with prior conditions and an adaptive step size approach.It optimizes the collision detection process using adaptive detection points and a binary sequential detection method.Furthermore,pruning optimization and cubic B-spline curve fitting are introduced to reduce redundant path points and improve smoothness.Simulation experiments conducted on the MATLAB platform showed that compared to the RRT* algorithm,the improved RRT algorithm reduced path length,planning time,and number of iterations by 61.66%,94.89%,and 93.85%,respectively.Compared to the RRT algorithm,these reductions were 69.64%,83.90%,and 88.00%,respectively,while achieving higher path smoothness.These results validate the efficiency of the improved algorithm.Additionally,obstacle avoidance path planning experiments for robotic arms demonstrate that the improved algorithm can quickly generate viable paths through complex environments with multiple obstacles,confirming its practical utility in robotic arm applications.