An Improved Second-Order Consensus Multi-UAV Distributed Formation Method

To address the long formation time and total travel distance of large-scale UAV collaborative formations, as well as formation control errors caused by delays and interference, we first used the Kuhn–Munkres algorithm combined with the distributed Starfish optimization algorithm to find the optimal position allocation solution. We then used a second-order consistency method that incorporated prediction delay compensation and a dynamically weighted artificial potential field to rapidly form the target formation. Finally, we deployed this method in a unit box integrating an onboard computer and Pixhawk, and used Gazebo, ROS, and PX4 to build a system for multi-UAV formations to conduct three-dimensional, full-coverage detection of a selected area. Taking eight drones as an example, the total formation time was shortened by 18.71%, and the total distance was reduced by 15.73%. The maximum improvement in speed consistency error reached 93.05%, enhancing the efficiency and stability of formation generation. This approach is suitable for situations where drones need to quickly form a specific formation to cover a large area.