双工业机械臂协同作业的动态路径规划与优化算法OA

Aiming at the problems of low search efficiency,local optimality and obstacle avoidance hysteresis in high-dimensional dynamic scenarios when traditional algorithms are applied to dual industrial manipulators,a global-local two-layer path optimiza-tion framework was constructed.Specifically,the Rapidly exploring Random Tree(RRT)controlled by a gravitational function was adopted and combined with the Dynamic Window Approach(DWA).Meanwhile,a task scheduling state pool was designed to realize path mutual avoidance and beat synchronization of cooperative manipulators in the shared space.A simulation evaluation of dual industrial manipulators cooperative operation in high-density obstacle and dynamic interference environments was conduc-ted based on the ROS 2 and Gazebo platforms.Compared with the unoptimized path,after the dynamic path planning and optimi-zation algorithm was enabled,the average path length was shortened by approximately 18.0％,the average energy consumption was reduced by about 18.0％,the number of conflicts decreased from 3 to 0,and the scheduling delay was also significantly short-ened.Subsequently,the algorithm was deployed in an actual metal component assembly unit to complete a two-week continuous grasping and insertion task verification.The average planning time was maintained within 85 ms,the average pose error was con-trolled within±2.3 mm,and no shutdown conflicts occurred.These results demonstrate that the algorithm has good engineering feasibility and stability.Studies have shown that the coupled strategy of gravity bias and dynamic obstacle avoidance can signifi-cantly improve the path quality and system robustness of dual industrial manipulators collaborative operations,and provide effec-tive technical support for the flexible scheduling of intelligent manufacturing production lines.