Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot

• URL: http://arxiv.org/abs/2604.12031v1
• Date: Mon, 13 Apr 2026 20:20:03 GMT
• Title: Dynamic Modeling and Robust Gait Optimization of a Compliant Worm Robot
• Abstract summary: This paper presents an experimentally grounded modeling and optimization framework for a compliant worm robot capable of traversing corrugated pipes.<n>A multi-objective gait optimization problem is formulated to maximize average speed while minimizing average power.<n>Results show that the proposed framework captures the dominant locomotion and energy-consumption behavior of the robot.
• Score: 6.048766517808481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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