Joint Terrestrial-Aerial Path Planning for Tensegrone Robot
Songyuan Liu, Zhe Jing, Siyuan Hao, Jingshuo Lyu, Zichen Tao, Yun Gui, Hao Fang, Qingkai Yang · Unmanned Systems · 2025
This paper explores the joint terrestrial-aerial path planning challenge for the tensegrone robot, which combines a six-bar tensegrity structure with drone capabilities. Due to its unique construction, the tensegrone robot can both roll on the ground and fly in the air. To solve this joint path planning problem, we introduce a method based on probabilistic roadmaps. This approach leverages the topological properties of the feasible space and eliminates the necessity of calculating execution-specific trajectories. Moreover, to address the issue of achieving precise postures during the transition from rolling to flying mode, we propose a two-stage trajectory generation method. The effectiveness and robustness of the proposed methods are validated through simulation tests.