Multiobjective Obstacle Avoidance Motion Planning for a New Material Handling Robot

A new material handling robot applied in production line must have collision-free motion planning and good kinematics performance. A motion planning method that can satisfy obstacle avoidance and kinematic requirements simultaneously is proposed to solve the problem. This method is conducted during trajectory parameterization, collision detection, fitness building, and particle swarm optimization (PSO). First, a finite number of intermediate nodes are used to construct a piecewise polynomial function as the parameterized trajectory in the joint space. Next, the collision detection between the obstacles and the gripper is executed through bounding box simplification and intersection test of geometric objects. A fitness function combining collision and kinematics performance is then defined by using weighted coefficient and penalty function methods. Finally, the motion planning problem is solved with PSO algorithm. This method has better obstacle avoidance flexibility, smoothness, small impact, and minimal time consumption during the motion process, and its effectiveness is verified through virtual prototype simulation.