MPC-Based Human-Accompanying Control Strategy for Improving the Motion Coordination Between the Target Person and the Robot

摘要

Social robots have gained widespread attention for their potential to assist people in diverse domains, such as living assistance and logistics transportation. Human-accompanying, i.e., walking side-by-side with a person, is an expected and essential capability for social robots. However, due to the complexity of motion coordination between the target person and the mobile robot, the accompanying action is still unstable. In this study, we propose a human-accompanying control strategy to improve the motion coordination for better practicability of the human-accompanying robot. Our approach allows the robot to adapt to the motion variations of the target person and avoid obstacles while accompanying them. First, a human-robot interaction model based on the separation-bearing-orientation scheme is developed to ascertain the relative position and orientation between the robot and the target person. Then, a human-accompanying controller based on behavioral dynamics and model predictive control (MPC) is designed to avoid obstacles and simultaneously track the direction and velocity of the target person. Experimental results indicate that the proposed method can effectively achieve side-by-side accompanying by simultaneously controlling the relative position, direction, and velocity between the target person and robot.