A Robotic Dynamic Collision Avoidance Method for Human-Robot Collaborative Assembly

Human-robot collaborative assembly (HRCA) has gained the attention of researchers due to the potential to combine the operator's flexibility with the robot's precision. In HRCA, implementing robotic dynamic collision avoidance can effectively minimize injuries and equipment damage. However, it is challenging to acquire the optimal collision avoidance motion path for the robot in real time, considering the safety of the moving operator and the efficiency of assembly. In this paper, a dynamic collision avoidance method considering real-time performance and the shortest path length for HRCA is proposed. Attractive, repulsive and tangential forces are generated aimed at enabling the robot to reach the target position while avoiding the collision with the operator. To improve the success rate of collision avoidance and reduce the path length, the force weight is introduced to generate the optimal guidance force. The genetic algorithm is applied to obtain the optimal solution of the force weight with the self-built collision avoidance training scenarios. Experiments are conducted to validate the superiority of the proposed method and its applications in the real-world HRCA scenario.