A Collaborative Navigation Algorithm for Unmanned Aerial Vehicles Based on Joint Cognition and Risk Perception

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable environmental map, while successor UAVs integrate this prior knowledge with real-time perceptions to form a joint cognitive representation. A dynamic trust mechanism quantitatively evaluates cognitive reliability, enabling risk-aware path planning that balances safety and efficiency. Simulations and physical experiments demonstrate that JCRP reduces the path length of follower UAVs by approximately 41.39% and improves the safe decision ratio by 10.9 percentage points over baseline methods. These results validate the method’s robustness in complex scenarios, such as maze-like environments, highlighting its potential for applications in search-and-rescue.