Simultaneous Optimization of Placement Planning and Motion Planning for a Single Robotic Arm Using Genetic Algorithm

Industrial robots are commonly used in factories for efficient high-mix low-volume production. It is required to determine the optimal placement and posture of a robot arm to pick up and place a workpiece to minimize the total operation time. We propose an efficient optimization approach for solving the placement planning and motion planning problems of a six-axis robot arm using a genetic algorithm (GA). To obtain a feasible solution to the problem of minimizing the total operation time, the proposed method conducts placement planning using GA and motion planning using Robot Operation System (ROS) simulation with a Rapidly-exploring Random Tree Star (RRT*) for a six-axis robot arm. The performance of the proposed algorithm is compared with that of a conventional method that uses particle swarm optimization. The computational results show that the proposed algorithm can reduce approximately 26% of the motion planning time compared to the conventional method. The proposed method can provide a near-optimal layout and motion planning solution within a practical computing time.