Motion planning with obstacle avoidance of an UR3 robot using charge system search

TLDR: A particle swarm optimization with the charge search system (CSS) to find the optimal path planning with obstacle avoidance for a cyber-physical system (CPS) of a future intelligent factory.

Abstract: For a cyber-physical system (CPS) of a future intelligent factory, a robotic manipulator is requested to co-work with human efficiently and safely in an environment with flexible arrangements. Therefore, an autonomous path planning of robotic manipulator is the most necessary issue to be resolved for the factory automation. For the robotic manipulator, optimizations and artificial intelligence (AI) methods are widely used to investigate the autonomous dynamic path-planning tasks with obstacle avoidance. Among these methods, the Rapidly Exploring Random Tree (RRT) algorithm has been widely used in path planning for a complex environment, because the RRT algorithm has the advantages of perfect expansion, probability completeness, and fast exploring speed. However, for some practical cases, the existing RRT algorithm may obtain a discontinuous solution of the angular trajectory. To solve the above problem, we studied a particle swarm optimization with the charge search system (CSS) to find the optimal path planning with obstacle avoidance. The steps of the proposed method are mentioned as follows: (1) establish the configuration space with the obstacle regions, (2) formulate the motion planning with obstacle using the CSS method and (3) use the PSO method to solve the path planning problem. Finally, the simulation of the path-planning task with obstacle avoidance is visually illustrated using the software RoboDK and the proposed method is implemented by the real-time experiments of the UR3 robot.