An extensive search strategy of UAV swarm based on hybrid ant colony optimization approach under unpredictable environment

Abstract

Large-scale collaborative searching and attacking targets with unknown attributes is an important military application scenario for multiple Unmanned Aerial Vehicles (UAV) swarms. This paper mainly focuses on the mission allocation problem of UAV formations in an uncertain search environment. In this paper, a hybrid swarm intelligence approach, called improved ant colony optimization and semi-artificial potential field (IACO-SAPF), is introduced. The IACO-SAPF algorithm enhances the pheromone update method and state transition mechanism based on the ACO method. Then, local pheromone update mechanism is improved by introducing the concept of the SAPF method to maintain the UAV flying at a safe distance from the obstacle under uncertain environment. In addition, in order to successfully destroy the target with dynamic characteristics or defensive attributes, we introduce an encirclement attack (EA) algorithm into the IACO-SAPF approach to encircle and attack the target in specific formations. Finally, as compared to the other four methods, simulation results show that the IACO-SAPF algorithm demonstrates superior robustness against wind and communication interference. Furthermore, the UAV swarm could successfully encircle and attack the target with dynamic or defensive features in complicated battlefield environment.