Large Workspace Frontal Human Following for Mobile Robots Utilizing 2D LiDAR

Abstract

Robots following humans is an efficient and practical feature, particularly in the context of service robotics. However, the majority of existing research has focused on the robot following the human from behind, with relatively little attention given to the robot operating in front of the human. This following-in-front approach, where the robot remains within the user’s field of view, is more reassuring and facilitates further human-robot interaction. Unlike traditional following methods, frontal following requires awareness of the user’s orientation and intentions. New challenges will arise in developing a tracker that can accurately estimate a user’s pose based on a knee height 2D LiDAR, especially when the legs frequently occlude each other. There is also a need to ensure the safety of the user while addressing how the robot can keep up with the user in situations where it falls behind. Our contribution lies in proposing a novel 2D LiDAR-based frontal human following method that accommodates various motion patterns of the target user. Specifically, inspired by human walking gait, we develop an accurate and robust human pose tracker that takes into account leg occlusion and the data association problem. We build a large workspace velocity field that enables a holonomic mobile robot to follow and come gradually and safely in front of the user regardless of their relative position. We evaluate the performance of our approach through a rich set of experimental scenarios, and demonstrate its effectiveness in achieving reliable frontal human following. Our studies suggest this approach has potential applications in warehouses, industrial factories or for visually impaired persons.