Optimal Planning of Local Motion Trajectory for Intelligent Sorting Machines Based on Graph Optimized DWA Algorithm
The optimal motion trajectory planning of intelligent sorting robots is closely related to sorting efficiency and automa-tion level.By taking intelligent sorting robots as an example,a local motion trajectory planning algorithm based on the graph optimi-zation dynamic window approach(DWA)is innovatively analyzed.This algorithm first adopts the dynamic window method to obtain multiple trajectories,and then introduces obstacle avoidance and increases the global path,point spacing,non holonomic dynamics,acceleration,velocity,and other constraints to each motion trajectory,thereby creating the hypergraph.Finally,the general graph with open-source C++software is used to optimize the motion trajectory generated by sampling,achieve the evaluation of the motion trajectory,and find out the optimal motion path.The local motion trajectory planning algorithm of the DWA before and after the graph optimization has a relatively large estimation error in the vertical position,with a minimum and maximum difference of 0.02 m and 3.25 m,respectively,and corresponding time of 345 s and 697 s.The estimation error of the local motion trajectory planning al-gorithm for the DWA before and after graph optimization is slightly larger,with a difference of about 0.02 m/s.The local path plan-ning algorithm for improving the artificial potential field method and the local path planning algorithm for improving the time elastic band have the target motion trajectory overlaps of 72.68％and 68.25％,respectively.The local motion trajectory planning algorithm of the designed graph optimized DWA can better achieve the reasonable avoidance of obstacles,with a coincidence degree of 89.25％for the target motion trajectory.The research results effectively solve low planning efficiency in optimal motion trajectory planning for intelligent sorting robots,providing new possibilities for the development of actual mobile robot movement control technologies.
万方数据
维普
