发表机构
The State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University; Department of Dynamics and Control, Beihang University; Harbin Institute of Technology; Jilin University（浙江大学控制科学与工程学院工业控制技术国家重点实验室; 北京航空航天大学动力学与控制系; 哈尔滨工业大学; 吉林大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对被动轮式空地两栖车辆，提出基于学习的空地运动控制框架，包含学习型模式选择器和强化学习轨迹跟踪策略，实现有限感知下可靠切换与跨地形稳健跟踪，实验验证优于传统方法。
AI 中文摘要
被动轮式空地两栖车辆（TABVs）结合了空中机动性与节能的地面运动能力。然而，在面向实际应用时，有限机载感知下的可靠空地模式切换以及跨多种地形的稳健地面轨迹跟踪仍然具有挑战性。在本工作中，我们提出了一种基于学习的被动轮式TABV空地运动控制框架：1）一个学习型模式选择器，用于自主空地运动模式切换。该选择器利用历史单点飞行时间（ToF）测量值、机器人状态以及未来参考信息来确定当前的运动模式。2）一个用于轨迹跟踪的强化学习控制策略。该策略将本体感觉观测与未来参考信息相结合，以预测轨迹变化。对于地面运动，多地形训练和动力学随机化使得在不同地形上实现稳健跟踪成为可能。仿真和真实世界实验证明了在有限感知下的可靠空地切换以及在不同地形条件下的准确地面跟踪。在具有挑战性的过渡中，学习型选择器优于基于规则的模式选择器，而地面控制器在所有测试条件下均实现了比PID更低的定位均方根误差（RMSE），并在NMPC失效的情况下保持了良好的跟踪性能。集成这些能力后，系统通过多次自主模式切换跟踪了一条101米长的空地轨迹，定位RMSE为0.08米。
英文摘要
Passive-wheeled terrestrial-aerial bimodal vehicles (TABVs) combine aerial mobility with energy-efficient ground locomotion. However, reliable air-ground mode switching under limited onboard perception and robust ground trajectory tracking across diverse terrains remain challenging when targeting real-world applications. In this work, we propose a learning-based air-ground motion control framework for passive-wheeled TABVs: 1) a learned mode selector for autonomous air-ground motion mode switching. The selector uses historical single-point time-of-flight (ToF) measurements and robot states together with future reference information to determine the active locomotion mode. 2) a reinforcement learning control policy for trajectory tracking. The policy combines proprioceptive observations with future reference information to anticipate trajectory changes. For ground locomotion, multi-terrain training and dynamics randomization enable robust tracking across different terrains. Simulation and real-world experiments demonstrate reliable air-ground switching under limited perception and accurate ground tracking across diverse terrain conditions. The learned selector outperforms a rule-based mode selector in challenging transitions, while the ground controller achieves lower position RMSE than PID across all tested conditions and maintains decent tracking where NMPC fails. With these capabilities integrated, the system tracks a 101m air-ground trajectory through multiple autonomous mode transitions with a position RMSE of 0.08m.
