Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments

Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats.