Walking on Rough Terrain with Any Number of Legs
This work addresses the challenge of agile navigation for multi-legged robots in complex environments, offering a potential baseline for machine learning controllers, though it appears incremental as it builds on existing control strategies.
The paper tackles the problem of controlling multi-legged robots on rough terrain by introducing a segmental control architecture that bridges WalkNet-like event cascades and CPG-based controllers, validated in simulation for robots with 6 to 16 legs. The result is an adaptive and computationally lightweight controller that tightly couples to ground contact and produces fictive locomotion when contact is missing.
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.