Enhanced Teaching-Learning-Based Optimization for 3D Path Planning of Multicopter UAVs

Hoang, VT; Phung, MD

Enhanced Teaching-Learning-Based Optimization for 3D Path Planning of Multicopter UAVs

Hoang, VT Phung, MD

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Advanced Mechanical Engineering, Automation, and Sustainable Development 2021 (AMAS2021), 2022, pp. 743-753
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Hoang, VT
dc.contributor.author	Phung, MD
dc.date	2021-11-04
dc.date.accessioned	2023-07-05T23:04:03Z
dc.date.available	2023-07-05T23:04:03Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of the International Conference on Advanced Mechanical Engineering, Automation, and Sustainable Development 2021 (AMAS2021), 2022, pp. 743-753
dc.identifier.isbn	9783030996659
dc.identifier.issn	2195-4356
dc.identifier.issn	2195-4364
dc.identifier.uri	http://hdl.handle.net/10453/171218
dc.description.abstract	This paper introduces a new path planning algorithm for unmanned aerial vehicles (UAVs) based on the teaching-learning-based optimization (TLBO) technique. We first define an objective function that incorporates requirements on the path length and constraints on the movement and safe operation of UAVs to convert the path planning into an optimization problem. The optimization algorithm named Multi-subject TLBO is then proposed to minimize the formulated objective function. The algorithm is developed based on TLBO but enhanced with new operations including mutation, elite selection and multi-subject training to improve the solution quality and speed up the convergence rate. Comparison with state-of-the-art algorithms and experiments with real UAVs have been conducted to evaluate the performance of the proposed algorithm. The results confirm its validity and effectiveness in generating optimal, collision-free and flyable paths for UAVs in complex operating environments.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Proceedings of the International Conference on Advanced Mechanical Engineering, Automation, and Sustainable Development 2021 (AMAS2021)
dc.relation.ispartof	International Conference on Advanced Mechanical Engineering, Automation, and Sustainable Development
dc.relation.ispartofseries	Lecture Notes in Mechanical Engineering
dc.relation.isbasedon	10.1007/978-3-030-99666-6_107
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Enhanced Teaching-Learning-Based Optimization for 3D Path Planning of Multicopter UAVs
dc.type	Conference Proceeding
utslib.location.activity	Ha Long, Quang Ninh, Viet Nam
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-07-05T23:04:02Z
pubs.finish-date	2021-11-07
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2021-11-04
dc.location	Switzerland

Abstract:

This paper introduces a new path planning algorithm for unmanned aerial vehicles (UAVs) based on the teaching-learning-based optimization (TLBO) technique. We first define an objective function that incorporates requirements on the path length and constraints on the movement and safe operation of UAVs to convert the path planning into an optimization problem. The optimization algorithm named Multi-subject TLBO is then proposed to minimize the formulated objective function. The algorithm is developed based on TLBO but enhanced with new operations including mutation, elite selection and multi-subject training to improve the solution quality and speed up the convergence rate. Comparison with state-of-the-art algorithms and experiments with real UAVs have been conducted to evaluate the performance of the proposed algorithm. The results confirm its validity and effectiveness in generating optimal, collision-free and flyable paths for UAVs in complex operating environments.

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http://hdl.handle.net/10453/171218