Learning control for reliable air ground switching and tracking
Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking
Robotics
Summary
Switching between flying and driving modes reliably is tough for robots that can both fly and move on the ground. The authors of this paper created a system that uses machine learning to decide when the robot should switch modes using simple distance measurements and robot speed info. They also trained the robot to follow paths on different types of terrain more accurately than traditional methods. Tests in simulations and the real world showed their system works better at switching modes smoothly and tracking routes precisely.
What this means in practice
- •For drone operators: Improve reliability of drones that can fly and drive by enabling smooth switching and accurate path following on varied ground surfaces.
- •For robotics engineers: Develop robust motion controllers for aerial-ground robots that handle uncertain terrain and limited sensor data without manual tuning.
Authors
Ruitian Pang, Mingrui Li, Xuanting Liu, Tiancheng Lai, Juncheng Chen, Xiangyu Li, Ruibin Zhang, Qishao Wang, Jin Yu, Haiyin Piao, Fei Gao, Chao Xu, Yanjun Cao
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.