A parameter-less decoupling strategic adjustments method for shape reconfiguration of UAV swarm

Abstract

Unmanned aerial vehicle (UAV) swarms need to efficiently reconfigure different 3D shapes to meet complex task requirements, such as those in agriculture, industry, and battlefield environments. This paper presents a novel parameter-less decoupling strategy adjustment method (PD-SAM) to enable 3D shape reconfiguration of fixed-wing UAV swarms. The PD-SAM includes an improved behavior-rule-based decision-making approach that decouples the decision process into two main components: motion trend adjustment and dynamic position adjustment. Unlike traditional behavior-rule-based methods that rely on weighted parameters for combination, the proposed method reduces the number of parameters and eliminates the need for parameter optimization. Additionally, a mapping mechanism is introduced to translate macroscopic shape parameters into microscopic parameters. By mapping preset swarm shape parameters to microscopic rule actions, this mechanism facilitates flexible and spontaneous swarm reconfiguration. This approach is independent of the UAV swarm size and exhibits high scalability, making it suitable for swarm reconfiguration across a wide range of swarm sizes. Finally, with the aim of validating the effectiveness of the proposed method, this study employs flight control models suitable for real-world flight experiments, alongside high-fidelity aircraft dynamics models. The proposed PD-SAM algorithm is tested through software in the loop simulation and compared with a classical behavior-rule-based approach DWAR (Dynamically Weighting Autonomous Rules), as well as a optimization-based method HPSOGA (Hybrid Particle Swarm Optimization and Genetic Algorithm). Simulation results validate that the proposed PD-SAM method reduces the average error by 74.44 % and the average standard deviation of the swarm motion trend by 51.83 % compared to the DWAR method during the swarm shape reconfiguration process. In terms of computational resource consumption, the PD-SAM method is 3 to 4 orders of magnitude lower than the HPSOGA method.