Terrain characterization and locomotion adaptation in a small-scale lizard-inspired robot

TL;DR

This paper presents a small-scale lizard-inspired robot that adapts to complex terrains by using proprioceptive signals to estimate terrain depth and adjusting its movement patterns accordingly, improving locomotion performance.

Contribution

It introduces a systematic framework for perception and control in small-scale robots, enabling terrain adaptation through simple linear models and low-complexity feedback control.

Findings

Proposed a linear model for body movement based on granular depth.

Achieved 95% accuracy in terrain depth estimation using joint torque signals.

Implemented a linear feedback controller that enhances locomotion on unknown terrains.

Abstract

Unlike their large-scale counterparts, small-scale robots are largely confined to laboratory environments and are rarely deployed in real-world settings. As robot size decreases, robot-terrain interactions fundamentally change; however, there remains a lack of systematic understanding of what sensory information small-scale robots should acquire and how they should respond when traversing complex natural terrains. To address these challenges, we develop a Small-scale, Intelligent, Lizard-inspired, Adaptive Robot (SILA Bot) capable of adapting to diverse substrates. We use granular media of varying depths as a controlled yet representative terrain paradigm. We show that the optimal body movement pattern (ranging from standing-wave bending that assists limb retraction on flat ground to traveling-wave undulation that generates thrust in deep granular media) can be parameterized and approximated as a linear function of granular depth. Furthermore, proprioceptive signals, such as joint torque, provide sufficient information to estimate granular depth via a K-Nearest Neighbors classifier, achieving 95% accuracy. Leveraging these relationships, we design a simple linear feedback controller that modulates body phase and substantially improves locomotion performance on terrains with unknown depth. Together, these results establish a principled framework for perception and control in small-scale locomotion and enable effective terrain-adaptive locomotion while maintaining low computational complexity.