A Navigation and Control Strategy for Miniature Legged Robots

Abstract

This paper reports on a model-based control strategy for miniature legged robots tasked with navigation in cluttered environments. Our approach uses a new model for crawling locomotion to derive closed-form expressions of state propagation. The latter enable the development of a feedback control navigation strategy. The strategy consists of a waypoint tracking controller that steers the system along desired paths and an outer control loop that updates the reference path to account for uncertainty. This strategy allows noise-resilient navigation for miniature legged robots and is experimentally validated on an eight-legged robot that navigates in obstacle-cluttered environments.