This article presents a dynamics-based path-planning framework for tensegrity robots that accounts for environmental interaction. A systematic investigation is conducted into the mapping among locomotion gaits, actuations, and ground interaction forces of a six-bar tensegrity robot. The study addresses three coupled challenges within a unified framework: the dynamic effects of flexible-body deformation and environmental interaction, the construction of a finite gait library for realizing discrete motions, and a dynamics-informed local path-planning strategy for varying contact and friction conditions. A general dynamic model is established to describe contact friction and generate interaction-aware gaits. Subsequently, a gait library is developed that maps sequences of gait primitives to commanded motions. A lightweight local planning strategy, denoted as M1L2T3, is then formulated on this gait-primitive graph to enable efficient path selection under local map information. A proof-of-concept prototype is fabricated for experimental validation. Experimental results confirm the effectiveness of the interaction-coupled dynamic model for gait generation and locomotion control, showing reasonable agreement with theoretical predictions and validating the proposed planning strategy. The results demonstrate the value of incorporating interaction-aware gait realization into locomotion planning for tensegrity robots.
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