الملخص
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.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Pang, R., Li, M., Liu, X., Lai, T., Chen, J., Li, X., Zhang, R., Wang, Q., Yu, J., Piao, H., Gao, F., Xu, C., & Cao, Y. (2026). Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking. https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking
MLA 9
Pang, Ruitian, et al. "Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking." https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking.
شيكاغو (المؤلف–التاريخ)
Pang, Ruitian, Mingrui Li, Xuanting Liu, Tiancheng Lai, Juncheng Chen, Xiangyu Li, Ruibin Zhang, Qishao Wang, Jin Yu, Haiyin Piao, Fei Gao, Chao Xu, and Yanjun Cao. 2026. "Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking." https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking.
هارفارد
Pang, R., Li, M., Liu, X., Lai, T., Chen, J., Li, X., Zhang, R., Wang, Q., Yu, J., Piao, H., Gao, F., Xu, C. and Cao, Y. (2026) 'Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking', Available at: https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking.
فانكوفر
Pang R, Li M, Liu X, Lai T, Chen J, Li X, et al. Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking. https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking
IEEE
R. Pang, M. Li, X. Liu, T. Lai, J. Chen, X. Li, R. Zhang, Q. Wang, J. Yu, H. Piao, F. Gao, C. Xu, and Y. Cao, "Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking," https://omanscience.com/ar/articles/learning-air-ground-motion-control-with-temporal-mode-switching-and-cross-terrain-tracking.