Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking

2026-09-22

AI Summary

研究通过学习方法解决被动轮式空地两栖车辆在有限感知下的模式切换和复杂地形跟踪问题，提出基于学习的模式选择器和强化学习控制策略。

Abstract

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.

Problem

• Air-Ground Motion Control
• Temporal Mode Switching
• Cross-Terrain Tracking
• Passive-Wheeled TABVs

Innovation

• learning-based motion control
• mode selector
• reinforcement learning
• cross-terrain tracking
• autonomous mode switching