Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems wi...

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Autores principales:	Lewis M. Pyke, Craig R. Stark
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	dynamic pathfinding swarm intelligence particle swarm optimisation flocking multi-agent systems and autonomous agents Applied mathematics. Quantitative methods T57-57.97 Probabilities. Mathematical statistics QA273-280
Acceso en línea:	https://doaj.org/article/c775b839d6d345c8a486525388fc0f34
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spelling	oai:doaj.org-article:c775b839d6d345c8a486525388fc0f342021-11-22T04:51:52ZDynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation2297-468710.3389/fams.2021.744955https://doaj.org/article/c775b839d6d345c8a486525388fc0f342021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fams.2021.744955/fullhttps://doaj.org/toc/2297-4687In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.Lewis M. PykeCraig R. StarkFrontiers Media S.A.articledynamic pathfindingswarm intelligenceparticle swarm optimisationflockingmulti-agent systems and autonomous agentsApplied mathematics. Quantitative methodsT57-57.97Probabilities. Mathematical statisticsQA273-280ENFrontiers in Applied Mathematics and Statistics, Vol 7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dynamic pathfinding swarm intelligence particle swarm optimisation flocking multi-agent systems and autonomous agents Applied mathematics. Quantitative methods T57-57.97 Probabilities. Mathematical statistics QA273-280
spellingShingle	dynamic pathfinding swarm intelligence particle swarm optimisation flocking multi-agent systems and autonomous agents Applied mathematics. Quantitative methods T57-57.97 Probabilities. Mathematical statistics QA273-280 Lewis M. Pyke Craig R. Stark Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
description	In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.
format	article
author	Lewis M. Pyke Craig R. Stark
author_facet	Lewis M. Pyke Craig R. Stark
author_sort	Lewis M. Pyke
title	Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
title_short	Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
title_full	Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
title_fullStr	Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
title_full_unstemmed	Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation
title_sort	dynamic pathfinding for a swarm intelligence based uav control model using particle swarm optimisation
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c775b839d6d345c8a486525388fc0f34
work_keys_str_mv	AT lewismpyke dynamicpathfindingforaswarmintelligencebaseduavcontrolmodelusingparticleswarmoptimisation AT craigrstark dynamicpathfindingforaswarmintelligencebaseduavcontrolmodelusingparticleswarmoptimisation
_version_	1718418185766567936

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

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