Flight Structure Optimization of Modular Reconfigurable UAVs

Abstract-- 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. 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 Figure 1: The optimal structure configuration with five modular ensuring over-actuation while ensuring trajectory UAVs with different installed equipment. Each module is tracking accuracy and (ii) significantly reduce computational equipped with either a manipulator, an RGBD camera, a Lidar, costs compared to traditional enumeration-based methods.