Walking on rough terrain with any number of legs.

Robotics would gain by replicating the remarkable agility of arthropods in navigating complex environments. In this simulation study, 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 the 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 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 betweenWalknet-like event cascade controllers and CPG-based controllers: it tightly couples to the ground when present, but produces fictive locomotion when ground contact is missing. It may be useful as an adaptive, computationally light-weight controller for multi-legged robots, and as baseline capability for scaffolding the learning of machine learning controllers.