Perception-Aware Cooperative Path Planning for Multi-UAV Systems in Urban Wind Fields via Deep Reinforcement Learning
Jie Ding, Linshen Wang, Shuxin Jin, Di Wang · 2026
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Description
Jie Ding, Linshen Wang, Shuxin Jin, and Di Wang present a learning method for planning cooperative paths for several drones in city wind. Buildings and steady wind can push drones off course and weaken learned policies. Safe teamwork needs both good sensing of the environment and robust planning.
Their enhanced deep Q-network folds wind speed, obstacle distances, and drone states into a decision process, learns from multi-step returns, and replays the most instructive past experiences with extra weight. In a simulated three-dimensional city with steady wind, it produced cleaner shared routes and cut total path length by about a tenth versus the standard baseline. The authors frame it as a foundation for real wind-disturbed flight.
Catalog record
| CREATOR | Jie Ding, Linshen Wang, Shuxin Jin, Di Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 88 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |