Walking on Rough Terrain with Any Number of Legs

TL;DR

This 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.

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.

Figures (6)

• Figure 1: Modular mechanical design of the robot. A segment (left) has 3 DOF: two leg motors rotate about the x-axis, and a yaw motor rotates about the z-axis. This modular design can be chained with backbone joints to create a hexapod (middle), or, e.g. a decapod (right). We build a hexapedal version (right, photo)
• Figure 2: State machine architecture used for hierarchical yaw-leg coordination.
• Figure 3: The 3-segment hexapod robot simulated on a rough terrain in the MuJoCo simulator.
• Figure 4: Robustness of Phase Convergence. The phase error decays rapidly on both ideal Flat Terrain (Blue) and Random Rough Terrain (Red Dashed). Despite the significant ground irregularities in the random terrain case, the distributed controller successfully synchronizes the legs, demonstrating that the gait stability is robust to environmental noise. This performance is consistent across both the (a) 6-legged and (b) 16-legged configurations.
• Figure 5: Body and foot trajectories, phase difference, and body posture convergence for a 3-segment robot simulated on (a) floating terrain, (b) flat terrain, (c) random terrain, (d) hill terrain, and (e) stair terrain. We omitted the body posture in the floating scene as it is not well defined