This paper presents a Genetic Algorithm (GA) designed to reconfigure a large group of modular Unmanned Aerial Vehicles (UAVs), each with different weights and inertia parameters, into an over-actuated flight structure with improved dynamic properties. Previous research efforts either utilized expert knowledge to design flight structures for a specific task or relied on enumeration-based algorithms that required extensive computation to find an optimal one. However, both approaches encounter challenges in accommodating the heterogeneity among modules. Our GA addresses these challenges by incorporating the complexities of over-actuation and dynamic properties into its formulation. Additionally, we employ a tree representation and a vector representation to describe flight structures, facilitating efficient crossover operations and fitness evaluations within the GA framework, respectively. Using cubic modular quadcopters capable of functioning as omni-directional thrust generators, we validate that the proposed approach can (i) adeptly identify suboptimal configurations ensuring over-actuation while ensuring trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.

翻译：本文提出一种遗传算法（GA），用于将具有不同重量和惯性参数的大型模块化无人机（UAV）集群重构成具有更优动态特性的过驱动飞行结构。以往的研究要么利用专家知识为特定任务设计飞行结构，要么依赖基于枚举的算法，后者需要大量计算才能找到最优结构。然而，这两种方法在适应模块间的异质性方面均面临挑战。我们的遗传算法通过将过驱动特性和动态特性的复杂性纳入其问题表述，有效应对了这些挑战。此外，我们采用树状表示和向量表示来描述飞行结构，分别促进了遗传算法框架内高效的交叉操作和适应度评估。通过使用能够作为全向推力发生器工作的立方体模块化四旋翼无人机，我们验证了所提方法能够：（i）熟练地识别确保过驱动特性同时保证轨迹跟踪精度的次优构型；（ii）相比传统的基于枚举的方法，显著降低计算成本。