Abstract：When the autonomous and remotely operated vehicle (ARV) performs close-range underwater docking, there exist some problems, such as poor performance of the positioning method and relatively large errors of the positioning information. For those problems, an underwater visual positioning algorithm based on the reflective tape scheme is designed. Firstly, a Q-learning based hybrid whale optimization algorithm (QHWOA) is proposed to improve convergence accuracy and convergence speed. With the reflective tape placed outside the interface of the TMS (tether management system) as the target, the docking image is segmented through Otsu's objective function optimized by QHWOA. Then, a keypoint extracting algorithm based on PCA (principal component analysis) dimensionality reduction is proposed to extract key contours and keypoints. Finally, SRPnP (a simple, robust and fast method for the perspective-n-point problem) algorithm is used to calculate relative pose of the ARV and the TMS. Through underwater docking experiment, the errors of key point extraction in pixel and visual positioning are calculated, which verify that the positioning errors meet the accuracy requirements of underwater docking. The algorithm can output effective positioning information during underwater docking, and guide ARV docking with TMS.
[1] 赵俊海，张美荣，王帅，等． ROV中继器的应用研究及发 展趋势[J]．中国造船， 2014， 55(3)： 222-232. Zhao J H, Zhang M R, Wang S, et al. Application and development trend of tether management system (TMS) for ROV[J]. Shipbuilding of China, 2014, 55(3): 222-232. [2] 郑荣，吕厚权，于闯，等． AUV与自主移动坞站对接的 技术研究及系统设计实现[J]．机器人， 2019， 41(6)： 713- 721. Zheng R, Lü H Q, Yu C, et al. Technical research, system design and implementation of docking between AUV and autonomous mobile dock station[J]. Robot, 2019, 41(6): 713-721. [3] Li D J, Zhang T, Yang C J. Terminal underwater docking of an autonomous underwater vehicle using one camera and one light[J]. Marine Technology Society Journal, 2016, 50(6): 58- 68. [4] Zhong L J, Li D J, Lin M W, et al. A fast binocular localisation method for AUV docking[J]. Sensors, 2019, 19(7). DOI: 10.3390/s19071735. [5] Palomeras N, Vallicrosa G, Mallios A, et al. AUV homing and docking for remote operations[J]. Ocean Engineering, 2018, 154: 106-120. [6] Palomeras N, Penalver A, Massot Campos M, et al. I-AUV docking and panel intervention at sea[J]. Sensors, 2016, 16(10). DOI: 10.3390/s16101673. [7] 王丙乾，唐元贵．面向ARV水下对接的视觉引导信息特 征点匹配算法研究[J]．海洋技术学报， 2018， 37(5)： 9-15. Wang B Q, Tang Y G. Research on feature matching algorithm of visual-guidance-information for ARV underwater docking [J]. Journal of Ocean Technology, 2018, 37(5): 9-15. [8] 魏志祥，吴超．面向ARV的视觉辅助水下对接方法研 究[J]．海洋工程， 2021， 39(1)： 91-99,152. Wei Z X, Wu C. Study on docking method of ARV based on monocular vision[J]. The Ocean Engineering, 2021, 39(1): 91- 99,152. [9] Yan Z P, Zhang J Z, Tang J L. Modified water wave optimization algorithm for underwater multilevel thresholding image segmentation[J]. Multimedia Tools and Applications, 2020, 79(43- 44): 32415-32448. [10] Abd El Aziz M, Ewees A A, Hassanien A E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J]. Expert Systems with Applications, 2017, 83: 242-256. [11] Yin P Y. Multilevel minimum cross entropy threshold selection based on particle swarm optimization[J]. Applied Mathematics and Computation, 2007, 184(2): 503-513. [12] Oliva D, Hinojosa S, Abd El Aziz M, et al. Context based image segmentation using antlion optimization and sine cosine algorithm[J]. Multimedia Tools and Applications, 2018, 77(19): 25761-25797. [13] Agrawal S, Panda R, Bhuyan S, et al. Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm[J]. Swarm and Evolutionary Computation, 2013, 11: 16-30. [14] Samma H, Lim C P, Saleh J M. A new reinforcement learningbased memetic particle swarm optimizer[J]. Applied Soft Computing, 2016, 43: 276-297. [15] Zamli K Z, Din F, Ahmed B S, et al. A hybrid Q-learning sinecosine-based strategy for addressing the combinatorial test suite minimization problem[J]. PLoS ONE, 2018, 13(5). DOI: 10. 1371/journal.pone.0195675. [16] Lang C B, Jia H M. Kapur’s entropy for color image segmentation based on a hybrid whale optimization algorithm[J]. Entropy, 2019, 21(3). DOI: 10.3390/e21030318. [17] Ewees A A, Abd El Aziz M, Oliva D. Image segmentation via multilevel thresholding using hybrid optimization algorithms[J]. Journal of Electronic Imaging, 2018, 27(6). DOI: 10. 1117/1.JEI.27.6.063008. [18] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. [19] Merzban M H, Elbayoumi M. Efficient solution of Otsu multilevel image thresholding: A comparative study[J]. Expert Systems with Applications, 2019, 116: 299-309. [20] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. [21] Yang X S, Deb S. Cuckoo search via Lévy flights[C]//World Congress on Nature & Biologically Inspired Computing. Piscataway, USA: IEEE, 2009: 210-214. [22] Price K V, Storn R M, Lampinen J A. Differential evolution: A practical approach to global optimization[M]. Berlin, Germany: Springer, 2005. [23] Tan Z P, Li K S, Wang Y. An improved cuckoo search algorithm for multilevel color image thresholding based on modified fuzzy entropy[J]. Journal of Ambient Intelligence and Humanized Computing, 2021. DOI: 10.1007/s12652-021-03001-6. [24] 刘琛，林盈，胡晓敏．差分演化算法各种更新策略的对比 分析[J]．计算机科学与探索， 2013， 7(11)： 983-993. Liu C, Lin Y, Hu X M. Analyses and comparisons of different update strategies for differential evolution[J]. Journal of Frontiers of Computer Science and Technology, 2013, 7(11): 983- 993. [25] Watkins C J C H, Dayan P. Q-learning[J]. Machine Learning, 1992, 8(3-4): 279-292. [26] Guo M Z, Liu Y, Malec J. A new Q-learning algorithm based on the metropolis criterion[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(5): 2140- 2143. [27] Li M M, Wang H F, Yang L F, et al. Fast hybrid dimensionality reduction method for classification based on feature selection and grouped feature extraction[J]. Expert Systems with Applications, 2020, 150. DOI: 10.1016/j.eswa.2020.113277. [28] 夏军营．空间目标的单目视觉位姿测量方法研究[D]．长 沙：国防科学技术大学， 2012. Xia J X. Researches on monocular vision based pose measurements for space targets[D]. Changsha: National University of Defense Technology, 2012. [29] Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395. [30] Wang P, Xu G L, Cheng Y H, et al. A simple, robust and fast method for the perspective-n-point problem[J]. Pattern Recognition Letters, 2018, 108: 31-37. [31] Mousavirad S J, Ebrahimpour-Komleh H. Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms[J]. Evolutionary Intelligence, 2017, 10(1-2): 45-75.
