Walking on Rough Terrain with Any Number of Legs

TL;DR

This paper introduces a novel control architecture for multi-legged robots that effectively navigates rough terrain by combining ground contact coupling with fictive locomotion, validated across various robot sizes in simulation.

Contribution

It presents a new segmental control architecture that bridges event cascade and CPG controllers, enabling adaptive and lightweight locomotion in complex environments.

Findings

Validated in simulation for robots with 6 to 16 legs

Tightly couples to ground contact for stability

Produces fictive locomotion when contact is absent

Abstract

Robotics would gain by replicating the remarkable agility of arthropods in navigating complex environments. Here we consider the control of multi-legged systems which have 6 or more legs. Current multi-legged control strategies in robots include large black-box machine learning models, Central Pattern Generator (CPG) networks, and open-loop feed-forward control with stability arising from mechanics. Here we present a multi-legged control architecture for rough terrain using a segmental robot with 3 actuators for every 2 legs, which we validated in simulation for robots with 6 to 16 legs. Segments have identical state machines, and each segment also receives input from the segment in front of it. Our design bridges the gap between WalkNet-like event cascade controllers and CPG-based controllers: it tightly couples to the ground when contact is present, but produces fictive locomotion when ground contact is missing. The approach may be useful as an adaptive and computationally lightweight controller for multi-legged robots, and as a baseline capability for scaffolding the learning of machine learning controllers.