A Risk Perception-Based 3D Path Planning Algorithm for Low-Altitude UAVs

With the rapid growth of urban airspace applications for unmanned aerial vehicles (UAVs), the demand for safe and coordinated flight of multiple UAVs in complex three-dimensional environments has become increasingly urgent. However, traditional path-planning methods primarily take the shortest distance as the optimization objective, failing to adequately account for static building obstacles and the dynamic interaction risks between UAVs, thereby struggling to ensure the safety and feasibility of simultaneous multiple UAV operations. To address these challenges, this paper proposes a risk-aware three-dimensional path-planning algorithm. Building upon a voxelized urban environment with obstacle inflation, the algorithm incorporates a building risk field and an inter-UAV risk field to characterize static environmental risks and dynamic multiple UAV risks. A first-come-first-served (FCFS) strategy is adopted to determine the planning order and generate UAV paths sequentially. During the path planning process, the algorithmgenerates the current UAV′s flight path based on the joint constraints of the global static risk field and the accumulated dynamic risk field. After each path is generated, the corresponding UAVinduced risk distribution is temporally superimposed onto the global risk map, forming a time-varying composite risk field that guides subsequent UAVs to avoid high-risk regions under the influence of the comprehensive risk. Simulation results demonstrate that while effectively mitigating potential collision risks between UAVs and buildings as well as among UAVs, the algorithm maintains favorable path length, significantly enhancing the cooperative flight safety of multiple UAVs in complex urban three-dimensional environments.