Symmetry-Oriented Dynamic Routing Planning Algorithm for Reliable Map Fusion in Distributed UAV Communication Networks

To enable distributed target searches by unmanned aerial vehicle (UAV) swarms, it is essential to collaboratively process multi-source sensing data and construct a globally consistent map. In response to the challenges posed by constrained communication and multi-hop transmission delays, this paper proposes a symmetry-oriented dynamic routing planning algorithm for reliable map fusion. The algorithm introduces a framework for the transmission and fusion of local perception maps, formulating routing tasks as an integer programming problem to determine latency-minimized transmission paths. When packet loss occurs, a dynamic re-routing strategy is triggered to ensure the continued reliability of the fusion process. The routing design preserves latency symmetry, aiming to keep transmission delays under packet loss conditions close to those under ideal, lossless scenarios. To improve scalability in large-scale UAV swarms, an approximate algorithm based on L-step forward prediction is further introduced to reduce computational complexity. The simulation results demonstrate that the proposed algorithm achieves low latency, strong robustness, and stable performance under varying communication conditions.