Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization
This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path plannin...
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Published in | Applied soft computing Vol. 107; p. 107376 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.08.2021
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Subjects | |
Online Access | Get full text |
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Abstract | This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV. SPSO is then used to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the UAV. To evaluate the performance of SPSO, eight benchmarking scenarios have been generated from real digital elevation model maps. The results show that the proposed SPSO outperforms not only other particle swarm optimization (PSO) variants including the classic PSO, phase angle-encoded PSO and quantum-behave PSO but also other state-of-the-art metaheuristic optimization algorithms including the genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) in most scenarios. In addition, experiments have been conducted to demonstrate the validity of the generated paths for real UAV operations. Source code of the algorithm can be found at https://github.com/duongpm/SPSO.
•Formulation of a new cost function that converts the path planning into an optimization problem in which safe operation of UAVs is guaranteed.•Development of a new algorithm named spherical vector-based particle swarm optimization (SPSO) for UAV path planning that outperforms other PSO variants and state-of-the-art metaheuristic algorithms.•Successful implementation of SPSO to generate paths for real UAV operations. |
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AbstractList | This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV. SPSO is then used to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the UAV. To evaluate the performance of SPSO, eight benchmarking scenarios have been generated from real digital elevation model maps. The results show that the proposed SPSO outperforms not only other particle swarm optimization (PSO) variants including the classic PSO, phase angle-encoded PSO and quantum-behave PSO but also other state-of-the-art metaheuristic optimization algorithms including the genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) in most scenarios. In addition, experiments have been conducted to demonstrate the validity of the generated paths for real UAV operations. Source code of the algorithm can be found at https://github.com/duongpm/SPSO.
•Formulation of a new cost function that converts the path planning into an optimization problem in which safe operation of UAVs is guaranteed.•Development of a new algorithm named spherical vector-based particle swarm optimization (SPSO) for UAV path planning that outperforms other PSO variants and state-of-the-art metaheuristic algorithms.•Successful implementation of SPSO to generate paths for real UAV operations. |
ArticleNumber | 107376 |
Author | Ha, Quang Phuc Phung, Manh Duong |
Author_xml | – sequence: 1 givenname: Manh Duong orcidid: 0000-0001-5247-6180 surname: Phung fullname: Phung, Manh Duong email: manhduong.phung@uts.edu.au organization: School of Electrical and Data Engineering, University of Technology Sydney (UTS), 15 Broadway, Ultimo NSW 2007, Australia – sequence: 2 givenname: Quang Phuc surname: Ha fullname: Ha, Quang Phuc email: quang.ha@uts.edu.au organization: School of Electrical and Data Engineering, University of Technology Sydney (UTS), 15 Broadway, Ultimo NSW 2007, Australia |
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Snippet | This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned... |
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SubjectTerms | Particle swarm optimization Path planning UAV |
Title | Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization |
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