Optimisation of the combined application planning and execution time utilising repeated PRM replanning for point-to-point sequences

摘要

Motion planning and collision avoidance are well studied topics in modern industrial robotics and they are already relatively simple to adopt using packages like ROS MoveIt or Battelle PathPlan. Using probabilistic planning algorithms, collision-free trajectories can be found in as little time as hundredths of a second. However, the obtained trajectories can vary significantly for each planning request. This can result in notably longer trajectories than necessary, increasing the total time required for planning and execution. Optimisation-based motion planners such as PRM* and RRT* can find shorter paths, at the cost of a higher planning time. Our focus is put on applications that require both short planning and short trajectory execution times using existing planning tools, this with the goal of minimising the total time required for the entire application.In this paper, an easy-to-implement approach is proposed to perform path optimisation for trajectory planning applications, without altering the used planner itself, which the authors dubbedrepeated PRM. This optimisation is performed by planning, selecting and eliminating trajectories based on minimal motion time during the robot’s current movement. Testing is done in a virtual environment using ROS MoveIt with a 6-DOF St?ubli TX2-90XL, utilising the OMPL PRM motion planning algorithm, eight random pose targets and eight collision objects. Tests are performed on pose sequences of one to eight poses with 30 simulations each. The average of all total times required to perform the pose sequences with the repeated PRM approach are compared to these of sequential PRM and PRM* methods.After testing, the repeated PRM method shows an average impact on the total planning and execution time with a reduction of up to 26,35 % compared to sequential PRM depending on the length of the pose sequence, and up to 57,60 % compared to sequential PRM*, as the additional computational time required for PRM* significantly increases its total required time. The variation of the total required times of the found trajectory sequences also improves by an average of 56,16 % and 45,56 % compared to PRM and PRM* respectively.