Navigation for Legged Mobility: Dynamic Climbing

Abstract

Autonomous navigation through unstructured terrains has been most effectively demonstrated by animals, who utilize a large set of locomotive styles to move through their native habitats. While legged robots have recently demonstrated several of these locomotion modalities (such as walking, running, jumping, and climbing vertical walls), motion planners have yet to be able to leverage these unique mobility characteristics. In this article, we outline some of the specific motion planning challenges faced when attempting to plan for legged systems with dynamic gaits, with specific instances of these demonstrated by the dynamic climbing platform TAILS. Using a unique implementation of sampling-based model predictive optimization, we demonstrate the ability to motion plan around obstacles on vertical walls and experimentally demonstrate this on TAILS by navigating through traditionally difficult narrow gap problems.