Discrete Pigeon-Inspired Optimization-Simulated Annealing Algorithm and Optimal Reciprocal Collision Avoidance Scheme for Fixed-Wing UAV Formation Assembly

Abstract

Multi-UAV system is an important part of unmanned system, which plays an indispensable role in military field and civil agriculture. First, task assignment model including complex constraints is established and Discrete Pigeon-Inspired Optimization-Simulated Annealing algorithm (DPIO-SA) is proposed to solve it, which updates the speed and position of pigeons through exchange and cross operations. Then, Genetic Algorithm (GA) is adopted to optimize the UAVs’ locations in formation. The experimental results show that the average fitness value of DPIO-SA is 13.5% higher than DPIO; After running the algorithm for 30 times, the number of times that DPIO-SA algorithm reaches the global optimum is 15, while DPIO is 2. Both mean DPIO-SA is easier to jump out of local extremum. To describe the fixed-wing UAV, the Unicycle model is adopted. PID control is used to control the fixed-wing’s heading and speed. Aiming at the collision avoidance, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is proposed, which allows fixed-wings to avoid collisions without having to communicate with each other. In the algorithm, the velocity region is divided by the definition of velocity obstacle, and the optimal velocity is obtained by linear planning algorithm. This enables the fixed-wings’ formation to find the right velocity in real time and effectively to avoid collision. Experiments show that 24 fixed-wings completed the formation assembly after running 16.4 s. Finally, according to the Contract Network Algorithm (CNA), the task scheduling problem is solved by the interaction among fixed-wings.