Spiking control systems for soft robotics: a rhythmic case study in a soft robotic crawler

Abstract

Inspired by spiking neural feedback, we propose a spiking controller for efficient locomotion in a soft robotic crawler. Its bistability, akin to neural fast positive feedback, combined with a sensorimotor slow negative feedback loop, generates rhythmic spiking. The closed-loop system is robust through the quantized actuation, and negative feedback ensures efficient locomotion with minimal external tuning. Using bifurcation analysis, we characterize how the sensorimotor gain-coupling body and controller dynamics-governs the emergence of qualitatively distinct dynamical regimes, including resting and crawling behaviors associated with peristaltic waves. Dimensional analysis formalizes a separation of mechanical and electrical timescales, and Geometric Singular Perturbation theory explains the geometry of the relaxation oscillations leading to endogenous crawling. Within this singularly perturbed framework, we further formulate and analytically solve an optimization problem, proving that locomotion speed is maximized when mechanical resonance is achieved via a matching of neuromechanical scales. Given the importance and ubiquity of rhythms and waves in soft-bodied locomotion, we envision that spiking control systems could be utilized in a variety of soft-robotic morphologies and modular distributed architectures, yielding significant robustness, adaptability, and energetic gains across scales.