SOURCE-LINKED INTELLIGENCE
Learning Air-Ground Motion Control with Temporal Mode Switching and Cross-Terrain Tracking
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 tog
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T15:16:59.000Z
First collected: 2026-09-23T04:11:12.117Z. This is not the publication date.