Accompaniment and collision avoidance for a cane-type robot using dynamic cost maps and MPC

Abstract This paper presents a dynamic object collision avoidance algorithm for a cane-type accompanying robot designed to support individuals who can walk independently but experience anxiety. The proposed method ensures a consistent light-touch contact support point while preserving the user’s voluntary walking behavior in dynamic environments. Our approach comprises two core components. First, we propose a tentative target point selection algorithm based on a dynamic cost map. This map reflects not only static objects but also dynamic objects, allowing the robot to determine an optimal target from candidate points positioned around the user. Second, we develop a velocity command generation method using Model Predictive Control tailored for accompaniment tasks. This formulation incorporates dynamic obstacle constraints to compute optimal velocity commands in real-time. To evaluate the effectiveness of the proposed algorithm, we conducted experiments in a living lab and with a real robot. The results demonstrate that the proposed framework achieves safe and smooth accompaniment while effectively avoiding dynamic objects.