Dynamic Resonance Frequency Identification for Economic Insect-Scale Legged Robot Locomotion

Advances in robotics and manufacturing processes have enabled the development of extremely small robotic devices approaching the size of insects. In this domain, legged microrobots (microbots) offer numerous potential applications and characteristics to explore. However, controlling such small multi-legged robots presents significant challenges in achieving the desired behavior. Primarily, due to the robot's small size, it can only operate with a tiny battery, therefore, an extremely computationally efficient controller is needed. Tiny robots are also susceptible to damage and control methodologies are needed that also ensure longevity. This paper presents a novel approach for creating a control algorithm for a multi-legged system that dynamically identifies and operates a microbot at its resonance frequency of movement. At the resonance frequency, a microbot's leg oscillations achieve maximum amplitude with minimal energy, resulting in optimal locomotion efficiency. By imposing a sinusoidal control for actuating each of the robot's legs, a two-part controller is developed. The controller is trained using soft actor-critic-based reinforcement learning on a custom model of the mClari microbot. A successful simulation-based demonstration of the learning-based controller is shown by varying the underlying surface (such as wet, soft, etc.) of the microbot where the controller dynamically identifies and switches to the corresponding resonance frequency, achieving efficient and adaptive locomotion.