Multi-UAV system improves drone search and rescue by sharing location beliefs
BGC-Nav: Cross-Altitude Multi-UAV Collaborative Navigation via Belief-Guided Coupling
Robotics
Summary
Finding people outdoors with a single drone is hard because the drone can only see part of the area at a time. The authors created BGC-Nav, a way for multiple drones flying at different heights to work together better without needing extra training. The high-flying drone keeps a map with uncertain guesses about where to look next, and the low-flying drone uses this as helpful advice instead of strict orders. This method helps drones find targets more reliably in tests compared to older approaches.
What this means in practice
- •For drone fleet operators: Coordinate multiple drones at different altitudes for more reliable search-and-rescue missions with less dependence on precise target locations.
- •For outdoor robotics developers: Build UAV navigation systems that use belief-guided advice from high-altitude drones to enhance low-altitude exploration autonomy.
Authors
Jiuwu Hao, Yueyang Wu, Liguo Sun, Haolin Song, Zao Zhang, Pin Lv
Abstract
Despite recent advances in Aerial Vision-and-Language Navigation (VLN), single Unmanned Aerial Vehicle (UAV) search-and-rescue in outdoor scenarios remains challenging due to partial observability, making cross-altitude collaboration a promising direction. However, existing collaborative navigation methods rely on precise target localization and rigid goal-dispatching paradigms, ignoring high-altitude observation uncertainty and suppressing low-altitude exploration autonomy. To address these issues, we introduce BGC-Nav, a training-free cross-altitude collaboration framework that enables robust navigation via Belief-Guided Coupling (BGC). Specifically, the high-altitude UAV maintains a Global Belief Memory encoding task-conditioned priors, which the low-altitude agent incorporates into decision-making via Local Exhaustion Memory (LEM), Local Attraction Augmentation (LAA), and Directional Belief Prior (DBP) as an advisory bias rather than a rigid command. Extensive experiments on ESARBench show that BGC-Nav consistently outperforms state-of-the-art single-agent and collaborative methods. Code is available at https://github.com/uestchjw/BGC-Nav.