Flight Structure Optimization of Modular Reconfigurable UAVs

TL;DR

This paper introduces a genetic algorithm for optimizing modular UAV flight structures, effectively handling heterogeneity and over-actuation, leading to better dynamic performance and reduced computational costs.

Contribution

It presents a novel GA framework with specialized representations to optimize modular UAV configurations considering dynamic properties and heterogeneity.

Findings

Successfully identifies suboptimal configurations with over-actuation

Ensures trajectory tracking accuracy

Reduces computational costs compared to enumeration methods

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. 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.