Path Planning for Multi-Copter UAV Formation Employing a Generalized Particle Swarm Optimization

TL;DR

This paper presents a generalized particle swarm optimization algorithm for multi-copter UAV formation path planning, effectively avoiding obstacles and maintaining formation during complex missions.

Contribution

It introduces a novel generalized PSO-based path planning method for multi-UAV formations in complex environments, including a path development scheme for individual drones.

Findings

The proposed GEPSO algorithm successfully plans feasible paths in obstacle-rich environments.

Simulation and experiments confirm the effectiveness of the approach.

The method maintains formation integrity during flight.

Abstract

The paper investigates the problem of path planning techniques for multi-copter uncrewed aerial vehicles (UAV) cooperation in a formation shape to examine surrounding surfaces. We first describe the problem as a joint objective cost for planning a path of the formation centroid working in a complicated space. The path planning algorithm, named the generalized particle swarm optimization algorithm, is then presented to construct an optimal, flyable path while avoiding obstacles and ensuring the flying mission requirements. A path-development scheme is then incorporated to generate a relevant path for each drone to maintain its position in the formation configuration. Simulation, comparison, and experiments have been conducted to verify the proposed approach. Results show the feasibility of the proposed path-planning algorithm with GEPSO.