Surrogate-assisted motion planning and layout design of robotic cellular manufacturing systems

A surrogate-assisted multi-objective evolutionary algorithm is proposed for simultaneous optimization of robot motion planning and layout design in robotic cellular manufacturing systems. A sequence-pair is used to represent the layout of components in a robotic cell to avoid overlapping in the evolutionary computation. The robot motion planning with Rapidly exploring Random Trees Star (RRT*) is applied to compute the total operation time of a robot arm for each layout. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to minimize the total required layout area and the operation time for a robot arm. The proposed surrogate model can estimate the robot’s operation time with 98% of accuracy without explicit computations of the motion planning algorithm. The experimental results with a physical 6 Degree of Freedom (DOF) manipulator show that the total computation time is approximately 1/400, significantly shorter than the conventional methods. • A surrogate-assisted optimization is proposed to solve layout design and motion planning. • A new multi-objective optimization problem is formulated to minimize both the area and the time. • The proposed method can significantly reduce the total computation time. • The performance of the proposed method is validated with a physical robot manipulator.