This study proposes an improved rapidly-exploring random tree(RRT)obstacle avoidance algorithm for the path planning problem of live-working robotic arms in complex distribution network environments.The algorithm in-troduces a dynamic sampling function based on obstacle distribution to dynamically adjust the sampling points,en-hancing the efficiency and accuracy of path planning.Combined with the cost function of the A*algorithm,the path is further simplified and smoothed to reduce the inflection points and optimise the motion trajectory of the robot's robotic arm.Simulation results demonstrate the algorithm's effectiveness in shortening the path planning time and re-ducing path length,with a 70.3％reduction in the number of sampling points and a 68.3％decrease in planning time in 3D simulations,proving its effectiveness in the field of live-working robotics.
关键词
RRT/路径规划/动态采样/机械臂/路径平滑
Key words
rapidly-exploring random tree(RRT)/path planning/dynamic sampling/robotic arm/path smoothing
维普
万方数据