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A multi-threaded back-and-forth algorithm for planning unmanned aerial vehicles

Published online by Cambridge University Press:  30 June 2025

S. Aslan Open the ORCID record for S. Aslan [Opens in a new window]  and
T. Erkin Open the ORCID record for T. Erkin [Opens in a new window]
S. Aslan
Affiliation:
Aeronautical Engineering, Erciyes University, Kayseri, Turkey
T. Erkin*
Affiliation:
Aeronautical Engineering, Erciyes University, Kayseri, Turkey
*
Corresponding author: T. Erkin; Email: tevfikerkin@erciyes.edu.tr
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Abstract

A path being planned for an unmanned aerial vehicle (UAV) or its armed conformation called the unmanned combat aerial vehicle (UCAV) has a critical importance on the flight safety and success of the task including reconnaissance, surveillance, monitoring or destroying. The Back-and-Forth (BaF) algorithm is one of the most recent greedy techniques that executes a heuristic approach for generating two backbone candidates and a combination procedure for bringing advantageous segments of them in order to solve the geometric description of the path planning problem. This study was devoted to a thread-based parallel implementation of the BaF algorithm, also named the multi-threaded BaF (tBaF for short). The threads of the tBaF algorithm increase their search bounds gradually according to the assigned thread indexes and execute the heuristic approach and combination procedure of the BaF for calculating paths. A set of detailed experiments was carried out with the aim of evaluating the performance of the tBaF, and its results were compared with the BaF and 14 other meta-heuristic-based path planners over three battlefield scenarios and their 12 test cases containing the preidentified enemy threats. Comparative studies showed that the different local and global search capabilities of the threads gained with the newly introduced thread-based organisation give a significant contribution to the path planning performance and allow the tBaF to be ranked among the top three planners even though it requires at least from four to eight times less function calls than the tested meta-heuristics.

Keywords

Back-and-ForthParallelizationMulti-threadingGeometric path planningUnmanned aerial vehicles

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Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 26
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Hall, J. and Anderson, D. Reactive route selection from pre-calculated trajectories – application to micro-UAV path planning, Aeronaut J, 115, 2011, (1172), pp 635–640.10.1017/S0001924000006321CrossRefGoogle Scholar
Gutierrez-Martinez, M., Rojo-Rodriguez, E., Cabriales-Ramirez, L., Estabridis, K. and Garcia-Salazar, O. Genetic algorithm-based path planning of quadrotor UAVs on a 3d environment, Aeronaut J, 2025, 129, (1334), pp 902–938.10.1017/aer.2024.132CrossRefGoogle Scholar
Thoma, A., Thomessen, K., Gardi, A., Fisher, A. and Braun, C. Prioritising paths: An improved cost function for local path planning for UAV in medical applications, Aeronaut J, 2023, 127, (1318), pp 2125–2142.10.1017/aer.2023.68CrossRefGoogle Scholar
Lim, D., Park, J., Han, D., Jang, H., Park, W. and Lee, D. UAV path planning with derivative of the heuristic angle, Int J Aeronaut Space Sci, 2021, 22, pp 140–150.10.1007/s42405-020-00323-1CrossRefGoogle Scholar
Noh, G., Park, J., Han, D. and Lee, D. Selective goal aiming rapidly exploring random tree path planning for UAVs, Int J Aeronaut Space Sci, 2021, 22, pp 1397–1412.10.1007/s42405-021-00406-7CrossRefGoogle Scholar
Ait-Saadi, A., Soukane, A., Meraihi, Y., Gabis, A.B., Mirjalili, S. and Ramdane-Cherif, A. UAV path planning using optimization approaches: A survey, Arch Comput Methods Eng, 2022, 29, (6), pp 4233–4284.10.1007/s11831-022-09742-7CrossRefGoogle Scholar
Aslan, S. Back-and-forth (BaF): A new greedy algorithm for geometric path planning of unmanned aerial vehicles, Computing, 2024, 106, pp 2811–2849.10.1007/s00607-024-01309-7CrossRefGoogle Scholar
Zhang, Y., Wu, L. and Wang, S. UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization, Math Probl Eng, 2013, 2013, pp 1–9.Google Scholar
Zhu, W. and Duan, H. Chaotic predator–prey biogeography-based optimization approach for UCAV path planning, Aerosp Sci Technol, 2014, 32, (1), pp 153–161.10.1016/j.ast.2013.11.003CrossRefGoogle Scholar
Duan, H. and Qiao, P. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning, Int J Intell Comput Cybernet, 2014, 7(1), pp 24–37.10.1108/IJICC-02-2014-0005CrossRefGoogle Scholar
Li, B., Gong, L. and Yang, W. An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning, Sci World J, 2014, 2014, p 232704.Google ScholarPubMed
Tang, Z. and Zhou, Y. A glowworm swarm optimization algorithm for uninhabited combat air vehicle path planning, J Intell Syst, 2015, 24, (1), pp 69–83.Google Scholar
Zhang, X. and Duan, H. An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning, Appl Soft Comput, 2015, 26, pp 270–284.10.1016/j.asoc.2014.09.046CrossRefGoogle Scholar
Zhou, Q., Zhou, Y. and Chen, X. A wolf colony search algorithm based on the complex method for uninhabited combat air vehicle path planning, Int J Hybr Inform Technol, 2014, 7, (1), pp 183–200.Google Scholar
Zhang, B. and Duan, H. Three-dimensional path planning for uninhabited combat aerial vehicle based on predator-prey pigeon-inspired optimization in dynamic environment. IEEE/ACM Trans Comput Biol Bioinf, 2015, 14, (1), pp 97–107.10.1109/TCBB.2015.2443789CrossRefGoogle Scholar
Chen, Y., Yu, J., Mei, Y., Wang, Y. and Su, X. Modified central force optimization (MCFO) algorithm for 3D UAV path planning, Neurocomputing, 2016, 171, pp 878–888.10.1016/j.neucom.2015.07.044CrossRefGoogle Scholar
Zhou, Y., Bao, Z., Wang, R., Qiao, S. and Zhou, Y.. Quantum wind driven optimization for unmanned combat air vehicle path planning, Appl Sci, 2015, 5, (4), pp 1457–1483.10.3390/app5041457CrossRefGoogle Scholar
Wang, G., Chu, H.C.E. and Mirjalili, S. Three-dimensional path planning for UCAV using an improved bat algorithm, Aerosp Sci Technol, 2016, 49, pp 231–238.10.1016/j.ast.2015.11.040CrossRefGoogle Scholar
Zhang, Sen., Zhou, Y., Li, Z., and Pan, Wei.. Grey wolf optimizer for unmanned combat aerial vehicle path planning. Adv Eng Software, 99:121–136, 2016.10.1016/j.advengsoft.2016.05.015CrossRefGoogle Scholar
Liu, Y., Zhang, X., Guan, X. and Delahaye, D. Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization, Aerosp Sci Technol, 2016, 58, pp 92–102.10.1016/j.ast.2016.08.017CrossRefGoogle Scholar
Liu, Y., Zhang, X., Zhang, Y. and Guan, X. Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach. Chin J Aeronaut, 2019, 32(6), 1504–1519.10.1016/j.cja.2019.03.026CrossRefGoogle Scholar
Luo, Q., Li, L. and Zhou, Y. A quantum encoding bat algorithm for uninhabited combat aerial vehicle path planning, Int J Innov Comput Appl, 2017, 8, (3), pp 182–193.10.1504/IJICA.2017.086642CrossRefGoogle Scholar
Zhang, Q., Wang, R., Yang, J., Ding, K., Li, Y. and Hu, J. Modified collective decision optimization algorithm with application in trajectory planning of UAV, Appl Intell, 2018, 48, (8), pp 2328–2354.10.1007/s10489-017-1082-1CrossRefGoogle Scholar
Miao, F., Zhou, Y. and Luo, Q. A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem, J Oper Res Soc, 2019, 70, (1), pp 21–52.10.1080/01605682.2017.1418151CrossRefGoogle Scholar
Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R. and Tuba, M. Unmanned combat aerial vehicle path planning by brain storm optimization algorithm, Stud Inform Control, 2018, 27, (1), pp 15–24.10.24846/v27i1y201802CrossRefGoogle Scholar
Lin, Na., Tang, J., Li, X. and Zhao, L. A novel improved bat algorithm in UAV path planning, J Comput Mater Contin, 2019, 61, pp 323–344.Google Scholar
Pan, J., Liu, N. and Chu, S. A hybrid differential evolution algorithm and its application in unmanned combat aerial vehicle path planning, IEEE Access, 2020, 8, pp 17691–17712.10.1109/ACCESS.2020.2968119CrossRefGoogle Scholar
Qu, C., Gai, W., Zhong, M. and Zhang, J. A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning, Appl Soft Comput, 2020, 89, p 106099.10.1016/j.asoc.2020.106099CrossRefGoogle Scholar
Qu, C., Gai, W., Zhang, J. and Zhong, M. A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning, Knowl-Based Syst, 2020, 194, p 105530.10.1016/j.knosys.2020.105530CrossRefGoogle Scholar
Yi, J., Lu, M. and Zhao, X. Quantum inspired monarch butterfly optimisation for UCAV path planning navigation problem, Int J Bio-Inspir Computat, 2020, 15, (2), pp 75–89.10.1504/IJBIC.2020.106428CrossRefGoogle Scholar
Chen, Y., Pi, D., and Xu, Y. Neighborhood global learning based flower pollination algorithm and its application to unmanned aerial vehicle path planning, Expert Syst Appl, 2021, 170, p 114505.10.1016/j.eswa.2020.114505CrossRefGoogle Scholar
Zhu, H., Wang, Y., Ma, Z. and Li, X. A comparative study of swarm intelligence algorithms for UCAV path-planning problems, Mathematics, 2021, 9, (2), p 171.10.3390/math9020171CrossRefGoogle Scholar
Zhu, H., Wang, Y. and Li, X. UCAV path planning for avoiding obstacles using cooperative co-evolution spider monkey optimization, Knowl-Based Syst, 2022, 246, p 108713.10.1016/j.knosys.2022.108713CrossRefGoogle Scholar
Xianjin Zhou, F.G., Fang, Xi. and Lan, Z. Improved bat algorithm for UAV path planning in three-dimensional space, IEEE Access, 2021, 9, pp 20100–20116.10.1109/ACCESS.2021.3054179CrossRefGoogle Scholar
Xu, H., Jiang, S. and Zhang, A. Path planning for unmanned aerial vehicle using a mix-strategy-based gravitational search algorithm, IEEE Access, 2021, 9, pp 57033–57045.10.1109/ACCESS.2021.3072796CrossRefGoogle Scholar
Jiang, Wei., Lyu, Y., Li, Y., Guo, Y. and Zhang, W. UAV path planning and collision avoidance in 3D environments based on POMPD and improved grey wolf optimizer, Aerosp Sci Technol, 2022, 121, p 107314.10.1016/j.ast.2021.107314CrossRefGoogle Scholar
Jarray, R., Al-Dhaifallah, M., Rezk, H. and Bouallègue, S. Parallel cooperative coevolutionary grey wolf optimizer for path planning problem of unmanned aerial vehicles, Sensors, 2022, 22, (5), p 1826.10.3390/s22051826CrossRefGoogle ScholarPubMed
Du, N., Zhou, Y., Deng, W. and Luo, Q. Improved chimp optimization algorithm for three-dimensional path planning problem, Multimed Tools Appl, 2022, 81, (19), pp 27397–27422.10.1007/s11042-022-12882-4CrossRefGoogle Scholar
Wang, X., Pan, J., Yang, Q., Kong, L., Snášel, V. and Chu, S. Modified Mayfly algorithm for UAV path planning, Drones, 2022, 6, (5), p 134.10.3390/drones6050134CrossRefGoogle Scholar
Niu, Y., Yan, X., Wang, Y. and Niu, Y. An adaptive neighborhood-based search enhanced artificial ecosystem optimizer for UCAV path planning, Expert Syst Appl, 2022, 208, p 118047.10.1016/j.eswa.2022.118047CrossRefGoogle Scholar
Niu, Y., Yan, X., Wang, Y. and Niu, Y. Three-dimensional UCAV path planning using a novel modified artificial ecosystem optimizer, Expert Syst Appl, 2023, 217, p 119499.10.1016/j.eswa.2022.119499CrossRefGoogle Scholar
Jia, Y., Qu, L. and Li, X. A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning, Eng Appl Artif Intell, 2022, 116, p 105410.10.1016/j.engappai.2022.105410CrossRefGoogle Scholar
Jia, Y., Qu, L. and Li, X. Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced grey wolf optimizer, Artif Intell Rev, 2023, 56, (10), pp 12257–12314.10.1007/s10462-023-10481-9CrossRefGoogle Scholar
Zhang, C., Zhou, W., Qin, W. and Tang, W. A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm, Expert Syst Appl, 2023, 215, p 119243.10.1016/j.eswa.2022.119243CrossRefGoogle Scholar
Ait-Saadi, A., Meraihi, Y., Soukane, A., Ramdane-Cherif, A., and Benmessaoud Gabis, A. A novel hybrid chaotic Aquila optimization algorithm with simulated annealing for unmanned aerial vehicles path planning, Comput Electr Eng, 2022, 104, p 108461.10.1016/j.compeleceng.2022.108461CrossRefGoogle Scholar
Ait-Saadi, A., Meraihi, Y., Soukane, A., Yahia, S., Ramdane-Cherif, A. and Gabis, A.B. An enhanced African vulture optimization algorithm for solving the unmanned aerial vehicles path planning problem, Comput Electr Eng, 2023, 110, p 108802.10.1016/j.compeleceng.2023.108802CrossRefGoogle Scholar
Yu, X., Jiang, N., Wang, X. and Li, M. A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning, Expert Syst Appl, 2023, 215, 119327.10.1016/j.eswa.2022.119327CrossRefGoogle Scholar
Chowdhury, A. and De, D. RGSO-UAV: Reverse glowworm swarm optimization inspired UAV path-planning in a 3D dynamic environment, Ad Hoc Netw, 2023, 140, p 103068.10.1016/j.adhoc.2022.103068CrossRefGoogle Scholar
Chen, B., J.Y., Zhang, H. and Yang, M. An improved spherical vector and truncated mean stabilization based bat algorithm for UAV path planning, IEEE Access, 2023, 11, pp 2396–2409.10.1109/ACCESS.2023.3234057CrossRefGoogle Scholar
Huang, C., Zhou, X., Ran, X., Wang, J., Chen, H. and Deng, W. Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning, Eng Appl Artif Intell, 2023, 121, p 105942.10.1016/j.engappai.2023.105942CrossRefGoogle Scholar
Zhang, X., Zhang, X. and Miao, Y. Cooperative global path planning for multiple unmanned aerial vehicles based on improved fireworks algorithm using differential evolution operation, Int J Aeronaut Space Sci, 2023, 24, (5), pp 1346–1362.10.1007/s42405-023-00578-4CrossRefGoogle Scholar
Yu, X. and Luo, W. Reinforcement learning-based multi-strategy cuckoo search algorithm for 3D UAV path planning, Expert Syst Appl, 2023, 223, p 119910.10.1016/j.eswa.2023.119910CrossRefGoogle Scholar
Hu, G., Zhong, J. and Wei, G. SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning, Expert Syst Appl, 2023, 223, p 119941.10.1016/j.eswa.2023.119941CrossRefGoogle Scholar
Wang, Z., Sun, G., Zhou, K. and Zhu, L. A parallel particle swarm optimization and enhanced sparrow search algorithm for unmanned aerial vehicle path planning, Heliyon, 2023, 9, (4), p e14784.10.1016/j.heliyon.2023.e14784CrossRefGoogle ScholarPubMed
Deng, L., Chen, H., Zhang, X., and Liu, H. Three-dimensional path planning of UAV based on improved particle swarm optimization, Mathematics, 2023, 11, (9), p 1987.10.3390/math11091987CrossRefGoogle Scholar
Aslan, S. A hospitalization mechanism based immune plasma algorithm for path planning of unmanned aerial vehicles, Int J Mach Learn Cybern, 2024, 15, pp 3169–3199.10.1007/s13042-023-02087-yCrossRefGoogle Scholar
Aslan, S. and Demirci, S. An immune plasma algorithm with q-learning based pandemic management for path planning of unmanned aerial vehicles, Egypt Inform J, 2024, 26, p 100468.10.1016/j.eij.2024.100468CrossRefGoogle Scholar
Ye, C., Shao, P., Zhang, S. and Wang, W. Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators, ISA Trans, 2024, 149, pp 196–216.10.1016/j.isatra.2024.04.010CrossRefGoogle ScholarPubMed
Haris, M., Bhatti, D.M.S. and Nam, H. A fast-convergent hyperbolic tangent PSO algorithm for UAVs path planning, IEEE Open J Vehicul Technol, 2024, 5, pp 681–694.10.1109/OJVT.2024.3391380CrossRefGoogle Scholar
Aslan, S. and Erkin, T. A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle, Adv Eng Inf, 2023, 55, p 101829.10.1016/j.aei.2022.101829CrossRefGoogle Scholar