A self-organized search and attack algorithm for multiple unmanned aerial vehicles

Abstract

This article presents a novel distributed algorithm for multiple unmanned aerial vehicles (UAVs) to solve the online search–attack mission self-organization problem under adversarial environment. The distributed search–attack mission self-organization algorithm (SAMSOA) divides the global optimization problem into some local optimization problems. Each UAV is considered as a subsystem and is assigned a separate processor to solve its local optimization problem. Meanwhile, the information exchange between UAVs can help each subsystem make the optimal decision for the multiple UAVs system. The SAMSOA algorithm consists of a normal flight mode and a threat avoidance mode. In the normal flight mode, the search–attack mission is modeled to maximize the surveillance coverage rate and minimize the targets' existence time. Then, the improved distributed ant colony optimization (ACO) algorithm is designed to generate path points. Finally, a Dubins curve is used to connect the path points smoothly. In the threat avoidance mode, the path of avoiding a threat is generated based on Dubins curve. A series of simulations are carried out to verify the online application of the proposed SAMSOA algorithm.