Coverage Maximization Topology Control for UAV-Swarm Networks with Robust Connectivity Maintenance

Unmanned aerial vehicle (UAV) swarm networks face an inherent conflict between coverage maximization and topology robustness. To tackle this problem, this paper proposes a distributed biconnected coverage optimization (DBCO) algorithm based on adaptive potential field fusion. In each iteration, every UAV first exchanges position information with its k-hop neighbors and performs local topology detection to estimate algebraic connectivity and identify bridge edges. Then, each UAV computes three gradient components to guide its movement. The coverage gradient drives the UAV toward uncovered regions while reducing overlapping areas, the connectivity maintenance gradient prevents communication link disconnection, and the topology repair gradient eliminates bridge edges by guiding their endpoints to form alternative paths. Finally, each UAV updates its three-dimensional position along the weighted sum of these gradients, where the weights are adaptively adjusted to balance coverage expansion and topology repair. Simulation results demonstrate that DBCO achieves 2-3 times higher coverage rate than existing biconnectivity methods, while attaining the highest biconnectivity success rate and approximately 50% improvement in algebraic connectivity (from 1.5 to 2.3 at 110 nodes).