Self-organized UAV Traffic in Realistic Environments

TL;DR

This study explores self-organized UAV traffic management in realistic, noisy environments, comparing algorithms and optimizing parameters for different densities and scenarios to improve flux and safety.

Contribution

It introduces and evaluates two fundamental self-organized algorithms for UAV traffic, providing criteria for optimal parameter selection in realistic conditions.

Findings

Different scenarios require tailored algorithmic approaches.

A slower-is-faster effect influences maximal flux at certain densities.

Communication delay significantly destabilizes UAV traffic performance.

Abstract

We investigated different dense multirotor UAV traffic simulation scenarios in open 2D and 3D space, under realistic environments with the presence of sensor noise, communication delay, limited communication range, limited sensor update rate and finite inertia. We implemented two fundamental self-organized algorithms: one with constant direction and one with constant velocity preference to reach a desired target. We performed evolutionary optimization on both algorithms in five basic traffic scenarios and tested the optimized algorithms under different vehicle densities. We provide optimal algorithm and parameter selection criteria and compare the maximal flux and collision risk of each solution and situation. We found that i) different scenarios and densities require different algorithmic approaches, i.e., UAVs have to behave differently in sparse and dense environments or when they have common or different targets; ii) a slower-is-faster effect is implicitly present in our models, i.e., the maximal flux is achieved at densities where the average speed is far from maximal; iii) communication delay is the most severe destabilizing environmental condition that has a fundamental effect on performance and needs to be taken into account when designing algorithms to be used in real life.