Flight Structure Optimization of Modular Reconfigurable UAVs

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 omnidirectional thrust generators, we validate that the proposed approach can (i) adeptly identify suboptimal configurations ensuring both over-actuation and trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.

Authors

• Yao Su 0001
• Ziyuan Jiao
• Zeyu Zhang 0001
• Jingwen Zhang
• Hang Li
• Meng Wang 0051
• Hangxin Liu

Keywords

• Trajectory tracking
• Heuristic algorithms
• Autonomous aerial vehicles
• Vectors
• Computational efficiency
• Trajectory
• Systems simulation
• Vehicle dynamics
• Genetic algorithms
• Quadrotors
• Unmanned Aerial Vehicles
• Modular Reconfigurable
• Dynamic Properties
• Crossover Operator
• Tree Representation
• Inertial Parameters
• Optimization Problem
• Evaluation Of Function
• Search Algorithm
• Fitness Function
• Weight Parameters
• Tree Structure
• Position Vector
• Constant Matrix
• Structural Configuration
• Subtree
• Modular System
• Skew-symmetric
• Translational Dynamics
• Improvement In Computational Efficiency
• Structural Optimization Problems
• Aerial Robots