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This paper introduces a decentralized control architecture for multi-legged robots operating on rough terrain, using a segmental approach with 3 actuators controlling every 2 legs. The architecture combines elements of event cascade controllers and CPGs, enabling both ground contact-driven control and fictive locomotion. Simulations validate the controller's effectiveness on robots with 6 to 16 legs, demonstrating its potential as a computationally efficient and adaptive solution.
Achieve robust locomotion for multi-legged robots on rough terrain with a surprisingly simple, decentralized control architecture that blends event-driven and CPG-based approaches.
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.
