Hostname: page-component-5b777bbd6c-w6wnr Total loading time: 0 Render date: 2025-06-25T19:44:01.004Z Has data issue: false hasContentIssue false
  • English
  • Français

An unmanned surface vehicle berthing planning based on bacteria foraging optimization algorithm considering energy consumption constraints

Published online by Cambridge University Press:  19 June 2025

Jiming Zhang ,
Yang Long Open the ORCID record for Yang Long [Opens in a new window] ,
Jiao Deng ,
Da Qiu  and
Caihua Fang
Jiming Zhang
Affiliation:
School of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China Key Laboratory of Green Manufacturing of Super-light Elastomer Materials of State Ethnic Affairs Commission, Hubei Minzu University, Enshi 445000, China
Yang Long*
Affiliation:
School of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China Key Laboratory of Green Manufacturing of Super-light Elastomer Materials of State Ethnic Affairs Commission, Hubei Minzu University, Enshi 445000, China
Jiao Deng
Affiliation:
School of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China Key Laboratory of Green Manufacturing of Super-light Elastomer Materials of State Ethnic Affairs Commission, Hubei Minzu University, Enshi 445000, China
Da Qiu
Affiliation:
School of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China Key Laboratory of Green Manufacturing of Super-light Elastomer Materials of State Ethnic Affairs Commission, Hubei Minzu University, Enshi 445000, China
Caihua Fang
Affiliation:
Wuhan Second Ship Design and Research Institute, Wuhan 430070, China
*
Corresponding author: Yang Long; Email: longyang0718@foxmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Unmanned surface vehicles (USVs) frequently encounter inadequate energy levels while navigating to their destinations, which complicates their successful berthing in intricate harbor environments. A bacterial foraging optimization algorithm (BFO) is proposed that takes energy consumption into account and incorporates multiple constraints (MC-BFO). The energy consumption model is redefined for wind environments, enhancing the sensitivity of USVs to wind conditions. Additionally, a reward function is integrated into the algorithm, and the fitness function is reconstructed to improve the goal orientation of the USV. This approach enables the USV to maintain a reasonable path length while pursuing low energy consumption, resulting in more practical navigation. Constraining the USV’s sailing posture for smoother paths and restricting the USV’s heading and speed near the berthage facilitate safe berthing. Finally, three distinct experimental environments are established to compare the paths generated by MC-BFO, BFO, and genetic algorithm under both downwind and upwind conditions, ensuring consistency in relevant parameters. Data on sailing posture, energy consumption, and path length are collected, generalized, and analyzed. The results indicate that MC-BFO effectively reduces energy consumption while maintaining an acceptable path length, resulting in smoother and more coherent paths compared to traditional segmented planning. In conclusion, this method significantly enhances the quality of the berthing path.

Keywords

USVberthingenergy savingpath planningBFO algorithm
Type
Research Article
Information
Robotica , First View , pp. 1 - 15
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Luo, J., Zhuang, J., Jin, M., Xu, F. and Su, Y., “An energy-efficient path planning method for unmanned surface vehicle in a time-variant maritime environment,” Ocean. Eng. 301, 117544 (2024).10.1016/j.oceaneng.2024.117544CrossRefGoogle Scholar
Wu, G., Li, D., Ding, H., Shi, D. and Han, B., “An overview of developments and challenges for unmanned surface vehicle autonomous berthing,” Complex. Intell. Syst. 10(1), 981–1003 (2024).10.1007/s40747-023-01196-zCrossRefGoogle Scholar
Gao, K., Gao, M., Zhou, M. and Ma, Z., “Artificial intelligence algorithms in unmanned surface vessel task assignment and path planning: A survey,” Lect. Notes. Comput. Sc. 86, 101505 (2024).Google Scholar
Jin, X., Zhang, G., He, J., Yu, W., Jiang, S., Wang, W., Guo, D., Zheng, F., Deng, Y. and Yang, Q., “Event-triggered bundled target traversing path planning using a dynamic elliptical guidance region for unmanned surface vehicles,” Appl. Ocean. Res. 141, 103793 (2023).10.1016/j.apor.2023.103793CrossRefGoogle Scholar
Niu, H., Ji, Z., Savvaris, A. and Tsourdos, A., “Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment,” Ocean. Eng. 196, 106766 (2020).10.1016/j.oceaneng.2019.106766CrossRefGoogle Scholar
Zhang, Y., Shi, G. and Liu, J., “Dynamic energy-efficient path planning of unmanned surface vehicle under time-varying current and wind,” J. Mar. Sci. Eng. 10(6), 759 (2022).10.3390/jmse10060759CrossRefGoogle Scholar
Yuan, S., Liu, Z., Sun, Y., Wang, Z. and Zheng, L., “An event-triggered trajectory planning and tracking scheme for automatic berthing of unmanned surface vessel,” Ocean. Eng. 273, 113964 (2023).10.1016/j.oceaneng.2023.113964CrossRefGoogle Scholar
Li, B., Hou, J., Wang, X., Ma, Y., Li, D., Wang, T. and Chen, G., “High-resolution flood numerical model and dijkstra algorithm based risk avoidance routes planning,” Int. Ser. Prog. Wat. Res. 37(8), 3243–3258 (2023).Google Scholar
Li, C., Huang, X., Ding, J., Song, K. and Lu, S., “Global path planning based on a bidirectional alternating search a* algorithm for mobile robots,” Comput. Ind. Eng. 168, 108123 (2022).10.1016/j.cie.2022.108123CrossRefGoogle Scholar
Sui, F., Tang, X., Dong, Z., Gan, X., Luo, P. and Sun, J., “Aco+ pso+ a*: A bi-layer hybrid algorithm for multi-task path planning of an auv,” Comput. Ind. Eng. 175, 108905 (2023).10.1016/j.cie.2022.108905CrossRefGoogle Scholar
Ma, Y., Zhao, Y., Li, Z., Yan, X., Bi, H. and Królczyk, G., “A new coverage path planning algorithm for unmanned surface mapping vehicle based on a-star based searching,” Appl. Ocean. Res. 123, 103163 (2022).10.1016/j.apor.2022.103163CrossRefGoogle Scholar
Guojun, M., Yunlong, D., Mingze, L., Zhibin, X. and Jin, Z., “A probability smoothing bi-rrt path planning algorithm for indoor robot,” Future. Gener. Comp. Sy. 143, 349–360 (2023).Google Scholar
Jiao, K., Chen, J., Xin, B., Li, L., Zheng, Y. and Zhao, Z., “Three-dimensional path planning with enhanced gravitational search algorithm for unmanned aerial vehicle,” Robotica 42(7), 2453–2487 (2024).10.1017/S0263574724000869CrossRefGoogle Scholar
Zheng, L., Yu, W., Li, G., Qin, G. and Luo, Y., “Particle swarm algorithm path-planning method for mobile robots based on artificial potential fields,” Sensors 23(13), 6082 (2023).10.3390/s23136082CrossRefGoogle ScholarPubMed
Ait-Saadi, A., Meraihi, Y., Soukane, A., Ramdane-Cherif, A. and Gabis, A. B., “A novel hybrid chaotic aquila optimization algorithm with simulated annealing for unmanned aerial vehicles path planning,” Comput. Electr. Eng. 104, 108461 (2022).10.1016/j.compeleceng.2022.108461CrossRefGoogle Scholar
Wang, X., Liu, Z. and Li, X., “Optimal delivery route planning for a fleet of heterogeneous drones: A rescheduling-based genetic algorithm approach,” Comput. Ind. Eng. 179, 109179 (2023).10.1016/j.cie.2023.109179CrossRefGoogle Scholar
Zhang, W., Zhang, S., Wu, F. and Wang, Y., “Path planning of uav based on improved adaptive grey wolf optimization algorithm,” IEEE Access 9, 89400–89411 (2021).10.1109/ACCESS.2021.3090776CrossRefGoogle Scholar
Mohammadi, A. and Sheikholeslam, F., “Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022),” Eng. Appl. Artif. Intel. 126, 106959 (2023).10.1016/j.engappai.2023.106959CrossRefGoogle Scholar
Ahmed, Y. A. and Hasegawa, K., “Implementation of automatic ship berthing using artificial neural network for free running experiment,” IFCA. Proc. Vol. 46(33), 25–30 (2013).10.3182/20130918-4-JP-3022.00036CrossRefGoogle Scholar
Ye Li, Z. H. J., Zang, W. B. and Liao, Y. L., “Layered trajectory planning and experiment for the autoberthing of unmanned surface vehicles,” J. Harbin Eng. Univ. 40(06), 1043–1050 (2019).Google Scholar
Ding, M., Zheng, X., Liu, L., Guo, J. and Guo, Y., “Collision-free path planning for cable-driven continuum robot based on improved artificial potential field,” Robotica 42(5), 1350–1367 (2024).10.1017/S026357472400016XCrossRefGoogle Scholar
Han, S., Wang, Y., Wang, L. and He, H., “Automatic berthing for an underactuated unmanned surface vehicle: A real-time motion planning approach,” Ocean. Eng. 235, 109352 (2021).10.1016/j.oceaneng.2021.109352CrossRefGoogle Scholar
Niu, H., Lu, Y., Savvaris, A. and Tsourdos, A., “An energy-efficient path planning algorithm for unmanned surface vehicles,” Ocean. Eng. 161, 308–321 (2018).10.1016/j.oceaneng.2018.01.025CrossRefGoogle Scholar
Raju, M., Gupta, M. K., Bhanot, N. and Sharma, V. S., “A hybrid pso–bfo evolutionary algorithm for optimization of fused deposition modelling process parameters,” J. Intell. Manuf. 30(7), 2743–2758 (2019).10.1007/s10845-018-1420-0CrossRefGoogle Scholar
Dhaliwal, B. S. and Pattnaik, S. S., “Bfo–ann ensemble hybrid algorithm to design compact fractal antenna for rectenna system,” Neural. Comput. Appl. 28(S1), 917–928 (2017).10.1007/s00521-016-2402-9CrossRefGoogle Scholar
Devi, S. and Geethanjali, M., “Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation,” Expert. Syst. Appl. 41(6), 2772–2781 (2014).10.1016/j.eswa.2013.10.010CrossRefGoogle Scholar
Kalaivani, L., Maheswari, R. V., Vigneshwaran, B., Karthick, A., Kathirvelu, M. and Marquez, F. P. G., “Partial discharge based recognition of water droplets location in high voltage insulator using convolutional neural network–bacterial foraging algorithm based optimized machine learning classifier,” Measurement 221, 113527 (2023).10.1016/j.measurement.2023.113527CrossRefGoogle Scholar
Yang, C., Weng, Y., Ji, J. and Wu, T., “A knowledge guided bacterial foraging optimization algorithm for many-objective optimization problems,” Neural. Comput. Appl. 34(23), 21275–21299 (2022).10.1007/s00521-022-07611-9CrossRefGoogle Scholar
Anand, K., Vijayaraj, A. and Anand, M. V., “An enhanced bacterial foraging optimization algorithm for secure data storage and privacy-preserving in cloud,” Peer. Peer. Netw. Appl. 15(4), 2007–2020 (2022).10.1007/s12083-022-01322-7CrossRefGoogle Scholar
Manjula, A., Kalaivani, L., Gengaraj, M., Maheswari, R. V., Vimal, S. and Kadry, S., “Performance enhancement of srm using smart bacterial foraging optimization algorithm based speed and current pid controllers,” Comput Electr. Eng. 95, 107398 (2021).10.1016/j.compeleceng.2021.107398CrossRefGoogle Scholar
Long, Y., Zuo, Z., Su, Y., Li, J. and Zhang, H., “An a*-based bacterial foraging optimisation algorithm for global path planning of unmanned surface vehicles,” J. Navig. 73(6), 1247–1262 (2020).10.1017/S0373463320000247CrossRefGoogle Scholar
Wu, J. and Su, Z., “Flavoring search algorithm with applications to engineering optimization problems and robot path planning,” Appl. Math. Model. 135, 396–437 (2024).10.1016/j.apm.2024.07.002CrossRefGoogle Scholar
Long, Y., Su, Y., Shi, B., Zuo, Z. and Li, J., “A multi-subpopulation bacterial foraging optimisation algorithm with deletion and immigration strategies for unmanned surface vehicle path planning,” Intel. Serv. Robot. 14(2), 303–312 (2021).10.1007/s11370-021-00361-yCrossRefGoogle Scholar
Zhang, R., Guo, H., Andriukaitis, D., Li, Y., Królczyk, G. and Li, Z., “Intelligent path planning by an improved rrt algorithm with dual grid map,” Alex. Eng. J. 88, 91–104 (2024).10.1016/j.aej.2023.12.044CrossRefGoogle Scholar
Yang, W. J., Wang, H. B. and Wang, J. H., “Research on path planning for mobile robot based on grid and hybrid of ga/sa,” Adv. Mat. Res. 479, 1499–1503 (2012).Google Scholar
Lv, C., Yu, H., Chen, J., Zhao, N. and Chi, J., “Trajectory tracking control for unmanned surface vessel with input saturation and disturbances via robust state error ida-pbc approach,” J. Frankl. Inst. 359(5), 1899–1924 (2022).10.1016/j.jfranklin.2022.01.036CrossRefGoogle Scholar
Monex, S. and Kumar, V. H., “Multi-objective optimization approach for coverage path planning of mobile robot,” Robotica 42(7), 2125–2149 (2024).Google Scholar
Wang, L. G. and Li, H., “Dynamics and power performance of a novel wave-powered unmanned surface vehicle,” Mar. Struct. 93, 103543 (2024).10.1016/j.marstruc.2023.103543CrossRefGoogle Scholar
Gu, S., Zhou, C., Wen, Y., Xiao, C. and Knoll, A., “Motion planning for an unmanned surface vehicle with wind and current effects,” J. Mar. Sci. Eng. 10(3), 420 (2022).10.3390/jmse10030420CrossRefGoogle Scholar
Gao, G., Li, D., Liu, K., Ge, Y. and Song, C., “A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments,” Robotica 42(10), 3324–3347 (2024).10.1017/S0263574724001383CrossRefGoogle Scholar
Passino, K. M., “Biomimicry of bacterial foraging for distributed optimization and control,” Ieee. Contr. Syst. Mag. 22(3), 52–67 (2002).Google Scholar