Extensible reactive motion generation for quadruped guide robots

Abstract

Leash-connected quadruped guide robots offer a flexible assistance solution for blind and visually impaired people. Existing methods for robots often depend on force sensors and lack effective motion generation for complex scenarios. This paper presents an extensible reactive motion generation framework, specifically developed to enhance the locomotion of quadruped robots through the implementation of Riemannian motion policy (RMP). A motion model is presented for a human-robot system featuring a flexible leash, suitable for geometric motion policy. Based on this model, an extensible reactive motion generation RMPflow framework tailored for guide tasks is presented. Within this framework, the functionalities required for typical guide tasks are decomposed into five subtasks: goal-reaching and path-tracking, leash-tensioning and mode-switching, robot posture constraint, obstacle avoidance, and tactile paving tracking. Each subtask is equipped with a specifically designed RMP controller. To validate the approach, we conducted simulation experiments, confirming the effectiveness of the subtask RMP controllers and the extensibility of the framework. Additionally, we implemented the framework on a quadruped guide robot platform equipped with a simultaneous localization and mapping system and a panoramic camera. Real-world experiments were designed to test the integrated subtasks in a complex environment, including a maze, multiple goal locations, static and dynamic obstacles, and tactile paving. The system successfully guided three participants through the environment. Experimental results highlight the framework’s effectiveness, adaptability, and robustness.

Graphical abstract

Keywords

quadruped robot / reactive motion generation / Riemannian motion policy / human-centered robotics

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