UAV swarm collaborative coverage control using GV division and planning algorithm

Abstract

Unmanned aerial vehicle (UAV) swarm coverage is one of the key technologies for multi-UAV cooperation, which plays an important role in collaborative investigation, detection, rescue and other applications. Aiming at the coverage optimisation problem of UAV in the target area, a collaborative visual coverage control method under positioning uncertainty is presented. First, the visual perception area with imprecise localisation, UAV model and sensor model are created based on the given task environment. Second, a regional division algorithm for the target task area is designed based on the principle of Guaranteed Voronoi (GV) diagram. Then a visual area coverage planning algorithm is designed, in which the task area is allocated to the UAV according to the corresponding weight coefficient of each area, and the input control law is adjusted by the expected state information of the UAV, so that the optimal coverage quality target value and the maximum coverage of the target area can be achieved. Finally, three task scenarios for regional division and coverage planning are simulated respectively, the results show that the proposed area coverage planning algorithm can realise the optimal regional distribution and can obtain more than 90% coverage in different scenarios.

Optimization of artificial bee colony deployment based on rapid depth-first search

“…Multiple UAVs work together to achieve the expansion of the mission capability of a single UAV and the improvement of the overall combat effectiveness of multiple UAVS through mutual complementary capabilities and action coordination.Liu proposed a cooperative visual coverage control method under the uncertainty of localization for the optimization of UAVs' coverage in the target area 2 ; Chengcheng investigated how to improve the control accuracy of formation maintenance and effective obstacle avoidance for the formation maintenance and obstacle avoidance problem of UAVs in clusters. formation keeping control accuracy and effective obstacle avoidance 3 .…”