A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem

TL;DR

This paper introduces a benchmark for multi-UAV task assignment using an extended Team Orienteering Problem model, and evaluates three intelligent algorithms to establish a standard for future comparisons.

Contribution

It models a new extended Team Orienteering Problem for multi-UAV task assignment and provides a benchmark with experimental evaluation of three algorithms.

Findings

Genetic Algorithm outperforms others in certain scenarios

Ant Colony Optimization shows strong convergence properties

Particle Swarm Optimization offers a good balance between speed and accuracy

Abstract

A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.