Visual-Attention-Based Neighbor Selection for Artificial-Potential-Field UAV Formation Control.

The development of Unmanned Aerial Vehicle (UAV) formation technology is rapid, and formation flying under complex conditions has also received more attention. However, neighbor selection without reliable radio communication remains challenging because fixed-radius or fixed-topology methods may process redundant neighbor states. Based on this, this paper designs two visual-attention-based neighbor-selection algorithms, namely Visual Attention Potential Field (VAPF) and Cluster Visual Attention Potential Field (CVAPF). Both algorithms use a zoom-lens visual attention rule to retain informative neighbors before the artificial-potential-field control input is evaluated. The algorithm VAPF selects informative UAV-level neighbors for the APF controller and supports aggregation behavior in simulation. Algorithm CVAPF extends the selection rule to clusters through a dual layer communication architecture, which improves synchronization while gathering formations. In the tested redundant sensing scene, BOIDS and Optimized-flocking process 43.38 and 15.20 neighbors on average, whereas VAPF and CVAPF reduce the values to 6.31 and 3.60 while preserving the formation behavior observed in the simulations.