Atricle

Info

Title:: Path Planning of Oil Spill Recovery System With Double USVs Based on Artificial Potential Field Method

Author(s):: Yulei Liao¹; 2; Xiaoyu Tang¹; 2; Congcong Chen¹; 3; Zijia Ren¹; Shuo Pang¹; Guocheng Zhang¹

Affilations:

Keywords:: Oil spill recovery; Double unmanned surface vehicles; Artificial potential field method; Path planning; Simulated annealing algorithm

DOI:: 10.1007/s11804-024-00437-y

Abstract:: Path planning for recovery is studied on the engineering background of double unmanned surface vehicles (USVs) towing oil booms for oil spill recovery. Given the influence of obstacles on the sea, the improved artificial potential field (APF) method is used for path planning. For addressing the two problems of unreachable target and local minimum in the APF, three improved algorithms are proposed by combining the motion performance constraints of the double USV system. These algorithms are then combined as the final APF-123 algorithm for oil spill recovery. Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery. Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment, and the speed is increased by more than 40% compared with the APF method.

References:

[1] Asl NA, Menhaj BM, Sajedin A (2013) Control of leader-follower formation and path planning of mobile robots using Asexual Reproduction Optimization (ARO). Applied Soft Computing Journal 14: 563-576. DOI: 10.1016/j.asoc.2013.07.030
[2] Bucas G, Saliot A (2002) Sea transport of animal and vegetable oils and its environmental consequences. Marine Pollution Bulletin 44(12): 1388-1396. DOI: 10.1016/S0025-326X(02)00303-X
[3] Chu Y (2022) Research on the formation control method of dual unmanned surface vehicle for oil spill containment. Master thesis, Harbin Engineering University, Harbin
[4] Drake D, Koziol S, Chabot E (2018) Mobile robot path planning with a moving goal. IEEE Access 6: 12800-12814. DOI: 10.1109/access.2018.2797070
[5] Duan H, Ma G, Luo D (2008) Optimal formation reconfiguration control of multiple UCAVs using improved particle swarm optimization. Journal of Bionic Engineering 5(4):340-347. DOI: 10.1016/S1672-6529(08)60179-1
[6] Giron-Sierra JM, Gheorghita AT, Angulo G, Jimenez JF (2014) Preparing the automatic spill recovery by two unmanned boats towing a boom: Development with scale experiments. Ocean Engineering 95(1): 23-33. DOI: 10.1016/j.oceaneng.2014.11.034
[7] Han L (2021) Multiphase sequence search based on simulated annealing algorithm. Yangtze River Information and Communication 34(2): 52-55. DOI: 10.3969/j.issn.1673-1131.2021.02.017
[8] Hao Y, Agrawal K (2005) Planning and control of UGV formations in a dynamic environment: A practical framework with experiments. Robotics and Autonomous Systems 51(2-3): 101-110. DOI: 10.1016/j.robot.2005.01.001
[9] Jiang W (2020) Research on cooperative planning and control method of double unmanned surface vehicle for oil spill roundup. Master thesis, Harbin Engineering University, Harbin
[10] Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5(1): 90-98. DOI: 10.1177/027836498600500106
[11] Lee M, Jung J (2015) Pollution risk assessment of oil spill accidents in Garorim Bay of Korea. Marine Pollution Bulletin 100(1): 297-303. DOI: 10.1016/j.marpolbul.2015.08.037
[12] Li Y, Zhang S, Chai L (2023) Cooperative obstacle avoidance trajectory planning for mobile robotic arm based on artificial potential field DDPG algorithm. Computer Integrated Manufacturing Systems: 1-15
[13] Liao Y (2012) Research on nonlinear motion control method of unmanned vehicle. PhD thesis, Harbin Engineering University, Harbin
[14] Liao Y, Jiang Q, Du T, Jiang W (2019) Redefined output model-free adaptive control method and unmanned surface vehicle heading control. IEEE Journal of Oceanic Engineering 45(3): 714-723. DOI: 10.1109/joe.2019.2896397
[15] Liu N, Tan Y, Mo W, Han H, Li L (2021) Optimization design of halbach linear generator based on simulated annealing algorithm. Transactions of China Electrotechnical Society 36(6): 1210-1218. DOI: 10.19595/j.cnki.1000-6753.tces.200442
[16] Liu T, Tian S (2006) Treatment of oil spill at sea and future development trend. China Water Transportation (Theoretical Edition) 4(11): 27-29
[17] Liu X, Dou Y (2021) Research on obstacle avoidance of small cruise vehicle based on improved artificial potential field method. Journal of Physics: Conference Series 1965(1): 12-24. DOI: 10.1088/1742-6596/1965/1/012036
[18] Liu Y, Richard B (2016) The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method. Applied Ocean Research 59: 327-344. DOI: 10.1016/j.apor.2016.06.013
[19] Manley JE (2008) Unmanned surface vehicles, 15 years of development. OCEANS MTS/IEEE, Quebec City, Canada, 1-4. DOI: 10.1109/OCEANS.2008.5152052
[20] Orozco-Rosas U, Montiel O, Sepúlveda R (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing Journal 77: 236-251. DOI: 10.1016/j.asoc.2019.01.036
[21] Orozco-Rosas U, Picos K, Pantrigo JJ, Montemayor AS, Cuesta-Infante A (2022) Mobile robot path planning using a QAPF learning algorithm for known and unknown environments. IEEE Access 10: 84648-84663. DOI: 10.1109/ACCESS.2022.3197628
[22] Qu H, Xing K, Alexander T (2013) An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120: 509-517. DOI: 10.1016/j.neucom.2013.04.020
[23] Sang H, You Y, Sun X, Zhou Y, Liu F (2021) The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations. Ocean Engineering 223: 108709. DOI: 10.1016/j.oceaneng.2021.108709
[24] Sun L, Fu Z, Tao F, Si P, Song S (2022) Research on obstacle avoidance algorithm for intelligent vehicles with improved artificial potential field. Journal of Henan University of Science & Technology (Natural Science) 43(5): 28-34+41+5-6. DOI: 10.15926/j.cnki.issn1672-6871.2022.05.005
[25] Tan G, Zou J, Zhuang J, Wan L, Sun H, Sun Z (2020) Fast marching square method based intelligent navigation of the unmanned surface vehicle swarm in restricted waters. Applied Ocean Research 95: 102018. DOI: 10.1016/j.apor.2019.102018
[26] Wang Y (2015) Research on path planning technology of unmanned boat formation based on fast marching method. Master thesis, Harbin Engineering University, Harbin
[27] Yu WQ, Lu YG (2021) UAV 3D environment obstacle avoidance trajectory planning based on improved artificial potential field method. Journal of Physics: Conference Series 1885(2): 20-22. DOI: 10.1088/1742-6596/1885/2/022020
[28] Yuan C, Weng S, He Y, Shen J, He L, Wang T (2019) Research on integrated path planning decision algorithm based on improved artificial potential field method. Transactions of the Chinese Society for Agricultural Machinery 50(9): 394-403. DOI: 10.6041/j.issn.1000-1298.2019.09.046
[29] Zang Y, Xu Z, Huang A, Ai S, Xia H, Kan R (2021) Reconstruction of heterogeneous combustion field distribution based on improved simulated annealing algorithm. Acta Physica Sinica 70(13): 229-240. DOI: 10.7498/aps.70.20202124
[30] Zhang M, Zhang Q, Wang Y, Ding Z (2020) A review of research on waterborne oil spill recovery ship. Journal of Qingdao Ocean Shipping Mariners College 41(1): 35-41. DOI: 10.3969/j.issn.2095-3747.2020.01.008
[31] Zheng H, Long M, Su H, Wang H (2021) Cooperative formation of aircraft and boats Combined with Virtual Structure and Artificial Potential Field. Proceedings of the 40th Chinese Control Conference, Shanghai 15: 634-639. DOI: 10.26914/c.cnkihy.2021.029286

Journal of Marine Science and Application[ISSN:1002-2848/CN:61-1400/f]

Info

References:

Memo

Commonly used function

Navigate

Tools

Statistics