Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot
For researchers in soft robotics, this work provides a practical framework for optimizing gaits of compliant worm robots in constrained environments, addressing the challenge of predictive modeling with deformable anchoring.
This paper presents a modeling and optimization framework for a compliant worm robot traversing corrugated pipes, achieving robust gait optimization for speed-power trade-off. Experimental results validate the framework's ability to capture dominant locomotion and energy-consumption behavior.
Worm-inspired robots provide an effective locomotion strategy for constrained environments by combining cyclic body deformation with alternating anchoring. For compliant robots, however, the interaction between deformable anchoring structures and the environment makes predictive modeling and deployable gait optimization challenging. This paper presents an experimentally grounded modeling and optimization framework for a compliant worm robot capable of traversing corrugated pipes. First, a hybrid dynamic locomotion model is derived, in which the robot motion is represented by continuous dynamics within a corrugation groove and discrete switching of anchoring positions between adjacent grooves. A slack-aware actuation model is further introduced to map the commanded gait input to the realized body-length change, and an energy model is developed based on physics and calibrated with empirical power measurement. Based on these models, a multi-objective gait optimization problem is formulated to maximize average speed while minimizing average power. To reduce the fragility of nominal boundary-seeking solutions, a kinematic robustness margin is introduced into the anchoring-transition conditions, leading to a margin-based robust gait optimization framework. Experimental results show that the proposed framework captures the dominant locomotion and energy-consumption behavior of the robot over the tested conditions, and enables robust gait optimization for achieving speed-power trade-off.