Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization

TL;DR

This paper presents a novel path planning method for UAV swarms that maintains formation using a teaching-learning-based optimization algorithm, incorporating enhancements to generate safe and efficient paths for inspection tasks.

Contribution

It introduces a formation-aware path planning approach for UAV swarms utilizing an improved teaching-learning-based optimization algorithm with multiple mechanisms.

Findings

Successfully maintains UAV formation during path planning

Generates safe and efficient paths for inspection tasks

Demonstrates effectiveness through simulations and experiments

Abstract

This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal and safe UAV operation. To optimize the fitness function and obtain a suboptimal path, we employ the teaching-learning-based optimization algorithm and then further enhance it with mechanisms such as mutation, elite strategy, and multi-subject combination. A number of simulations and experiments have been conducted to evaluate the proposed method. The results demonstrate that the algorithm successfully generates valid paths for the UAVs to fly in a triangular formation for an inspection task.