research-article

Cooperative Artificial Intelligence for underwater robotic swarm

Authors:

Chengcai WangAuthors Info & Claims

Volume 164, Issue C

https://doi.org/10.1016/j.robot.2023.104410

Published: 01 June 2023 Publication History

Abstract

Underwater Robots such as Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) has played an important role in many tasks, such as marine environmental monitoring, underwater resource exploration, oil and gas industries, hydrographic surveys, military missions, etc. Underwater robotic swarm is a team of cooperative underwater robots which focuses on controlling multiple underwater robots to work in an organic group. In contrast to a single underwater robot, underwater robotic swarm represents higher operation efficiency and better stability while executing complex tasks. However, it needs higher intelligence to realize complementary cooperation than a single robot. It is beneficial to researchers to present a comprehensive survey of the state of the art of cooperative research for underwater robotic swarm. We observe that the research of Artificial Intelligence (AI) for multiple underwater robots is still in an early stage. In this paper, we study different collaborative operation mode in detail, such as formation control, task allocation, path planning, obstacle avoidance, flocking control etc. We propose different classification frameworks for these research topics and it also can be used to compare different methods and help engineers choose suitable methods for various applications. To achieve better cooperative performance of underwater robots, there are several key factors, including multi-source heterogeneous sensing, cooperative communication and navigation, information fusion and decision. Moreover, cooperative AI for underwater robotic swarm has different kinds of interesting and helpful applications. Finally, several possible applied AI methods including meta-heuristic algorithms, deep learning method and distributed learning method are accomplishing to cooperation of underwater robotic swarm.

References

[1]

Perlman Howard, How much water is there on, in, and above the earth?, in: How Much Water is There on Earth, from the USGS Water Science School, Vol. 2, 2016.

[2]

National Research Council, Exploration of the Seas: Voyage Into the Unknown, The National Academies Press, Washington, DC, ISBN 978-0-309-08927-2, 2003,. URL: https://www.nap.edu/catalog/10844/exploration-of-the-seas-voyage-into-the-unknown.

[3]

Zhou Ziye, Liu Jincun, Yu Junzhi, A survey of underwater multi-robot systems, IEEE/CAA J. Autom. Sin. 9 (1) (2022) 1–18,.

[4]

R. Wernli, AUV commercialization-who’s leading the pack?, in: OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No. 00CH37158), Vol. 1, 2000, pp. 391–395, vol.1.

[5]

Peng Zhouhua, Wang Jiasen, Wang Jun, Constrained control of autonomous underwater vehicles based on command optimization and disturbance estimation, IEEE Trans. Ind. Electron. 66 (5) (2019) 3627–3635,.

[6]

Benjamin T. Champion, Matthew A. Joordens, Underwater swarm robotics review, in: 2015 10th System of Systems Engineering Conference (SoSE), 2015, pp. 111–116, https://doi.org/10.1109/SYSOSE.2015.7151953.

[7]

Yuan Chengzhi, He Haibo, Wang Cong, Cooperative deterministic learning-based formation control for a group of nonlinear uncertain mechanical systems, IEEE Trans. Ind. Inform. 15 (1) (2019) 319–333,.

[8]

Oh Kwang-Kyo, Park Myoung-Chul, Ahn Hyo-Sung, A survey of multi-agent formation control, Automatica (ISSN ) 53 (2015) 424–440,. URL: http://www.sciencedirect.com/science/article/pii/S0005109814004038.

Digital Library

[9]

Lewis M., Tan Kar-Han, High precision formation control of mobile robots using virtual structures, Auton. Robot. 4 (1997) 387–403,.

Digital Library

[10]

Egerstedt M., Xiaoming Hu, Formation constrained multi-agent control, IEEE Trans. Robot. Autom. 17 (6) (2001) 947–951,.

[11]

Ailon A., Zohar I., Control strategies for driving a group of nonholonomic kinematic mobile robots in formation along a time-parameterized path, IEEE/ASME Trans. Mechatronics 17 (2) (2012) 326–336,.

[12]

Liu Yang, Jia Yingmin, An iterative learning approach to formation control of multi-agent systems, Systems Control Lett. (ISSN ) 61 (1) (2012) 148–154,. URL: http://www.sciencedirect.com/science/article/pii/S0167691111002660.

[13]

Do K.D., Pan J., Nonlinear formation control of unicycle-type mobile robots, Robot. Auton. Syst. (ISSN ) 55 (3) (2007) 191–204,. URL: http://www.sciencedirect.com/science/article/pii/S0921889006001618.

Digital Library

[14]

Desai J.P., Ostrowski J.P., Kumar V., Modeling and control of formations of nonholonomic mobile robots, IEEE Trans. Robot. Autom. 17 (6) (2001) 905–908,.

[15]

Cui Rongxin, Sam Ge Shuzhi, Voon Ee How Bernard, Sang Choo Yoo, Leader–follower formation control of underactuated autonomous underwater vehicles, Ocean Eng. (ISSN ) 37 (17) (2010) 1491–1502,. URL: http://www.sciencedirect.com/science/article/pii/S0029801810001678.

[16]

Millán P., Orihuela L., Jurado I., Rubio F.R., Formation control of autonomous underwater vehicles subject to communication delays, IEEE Trans. Control Syst. Technol. 22 (2) (2014) 770–777,.

[17]

Wang Duansong, Ge Shuzhi Sam, Fu Mingyu, Li Dongyu, Bioinspired neurodynamics based formation control for unmanned surface vehicles with line-of-sight range and angle constraints, Neurocomputing (ISSN ) (2020),. URL: http://www.sciencedirect.com/science/article/pii/S0925231220303301.

[18]

Balch T., Arkin R.C., Behavior-based formation control for multirobot teams, IEEE Trans. Robot. Autom. 14 (6) (1998) 926–939,.

[19]

Lawton J.R.T., Beard R.W., Young B.J., A decentralized approach to formation maneuvers, IEEE Trans. Robot. Autom. 19 (6) (2003) 933–941,.

[20]

Lee Giroung, Chwa Dongkyoung, Decentralized behavior-based formation control of multiple robots considering obstacle avoidance, Intell. Serv. Robot. (ISSN ) 11 (1) (2018) 127–138,.

Digital Library

[21]

Hosseinzadeh Yamchi Mohammad, Mahboobi Esfanjani Reza, Distributed predictive formation control of networked mobile robots subject to communication delay, Robot. Auton. Syst. (ISSN ) 91 (2017) 194–207,. URL: http://www.sciencedirect.com/science/article/pii/S0921889016301014.

Digital Library

[22]

Karkoub Mansour, Wang Huiwei, Wu Tzu-Sung, Distributed Newton and quasi-Newton methods for formation control of autonomous vehicles, Ships Offshore Struct. 15 (2019) 1–14,.

[23]

Liu Hao, Lyu Yafei, Lewis Frank L., Wan Yan, Robust time-varying formation control for multiple underwater vehicles subject to nonlinearities and uncertainties, Internat. J. Robust Nonlinear Control 29 (9) (2019) 2712–2724,. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rnc.4517. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.4517.

[24]

Xiao Hanzhen, Chen C.L.P., Yu Dengxiu, Two-level structure swarm formation system with self-organized topology network, Neurocomputing (ISSN ) 384 (2020) 356–367,. URL: http://www.sciencedirect.com/science/article/pii/S0925231219316558.

Digital Library

[25]

Peng Z., Wang D., Chen Z., Hu X., Lan W., Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics, IEEE Trans. Control Syst. Technol. 21 (2) (2013) 513–520,.

[26]

Park Bong Seok, Adaptive formation control of underactuated autonomous underwater vehicles, Ocean Eng. (ISSN ) 96 (2015) 1–7,. URL: http://www.sciencedirect.com/science/article/pii/S0029801814004739.

[27]

Liang Xiao, Qu Xingru, Wang Ning, Li Ye, Zhang Rubo, Swarm control with collision avoidance for multiple underactuated surface vehicles, Ocean Eng. (ISSN ) 191 (2019),. URL: http://www.sciencedirect.com/science/article/pii/S0029801819306559.

[28]

Gao Jian, An Xuman, Proctor Alison, Bradley Colin, Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles, Ocean Eng. (ISSN ) 142 (2017) 666–675,. URL: http://www.sciencedirect.com/science/article/pii/S0029801817303852.

[29]

Wang Jinqiang, Wang Cong, Wei Yingjie, Zhang Chengju, Sliding mode based neural adaptive formation control of underactuated AUVs with leader-follower strategy, Appl. Ocean Res. (ISSN ) 94 (2020),. URL: http://www.sciencedirect.com/science/article/pii/S0141118719305863.

[30]

Guo Yuyan, Qin Hongde, Xu Bin, Han Yi, Fan Quan-Yong, Zhang Pengchao, Composite learning adaptive sliding mode control for AUV target tracking, Neurocomputing (ISSN ) 351 (2019) 180–186,. URL: http://www.sciencedirect.com/science/article/pii/S092523121930373X.

Digital Library

[31]

Zhao Enjiao, Chao Tao, Wang Songyan, Yang Ming, Finite-time formation control for multiple flight vehicles with accurate linearization model, Aerosp. Sci. Technol. (ISSN ) 71 (2017) 90–98,. URL: http://www.sciencedirect.com/science/article/pii/S1270963817308052.

[32]

Sun Yanchao, Chen Liangliang, Qin Hongde, Wang Wenjia, Distributed finite-time coordinated tracking control for multiple Euler–Lagrange systems with input nonlinearity, Nonlinear Dynam. (ISSN ) 95 (3) (2019) 2395–2414,. URL: https://doi.org/10.1007/s11071-018-4699-7.

[33]

Wang S., Na J., Ren X., RISE-based asymptotic prescribed performance tracking control of nonlinear servo mechanisms, IEEE Trans. Syst. Man Cybern. 48 (12) (2018) 2359–2370,.

[34]

O. Hassanein, S. G. Anavatti, T. Ray, Fuzzy modeling and control for Autonomous Underwater Vehicle, in: The 5th International Conference on Automation, Robotics and Applications, 2011, pp. 169–174, https://doi.org/10.1109/ICARA.2011.6144876.

[35]

He W., Dong Y., Adaptive fuzzy neural network control for a constrained robot using impedance learning, IEEE Trans. Neural Netw. Learn. Syst. 29 (4) (2018) 1174–1186,.

[36]

Wang Jinqiang, Wang Cong, Wei Yingjie, Zhang Chengju, Command filter based adaptive neural trajectory tracking control of an underactuated underwater vehicle in three-dimensional space, Ocean Eng. (ISSN ) 180 (2019) 175–186,. URL: http://www.sciencedirect.com/science/article/pii/S0029801819301507.

[37]

Wu H., Song S., You K., Wu C., Depth control of model-free AUVs via reinforcement learning, IEEE Trans. Syst. Man Cybern. 49 (12) (2019) 2499–2510,.

[38]

Cui R., Yang C., Li Y., Sharma S., Adaptive neural network control of AUVs with control input nonlinearities using reinforcement learning, IEEE Trans. Syst. Man Cybern. 47 (6) (2017) 1019–1029,.

[39]

Yansheng Yang, Gang Feng, Junsheng Ren, A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems, IEEE Trans. Syst. Man Cybern. A 34 (3) (2004) 406–420,.

Digital Library

[40]

Miao Baobin, Li Tieshan, Luo Weilin, A DSC and MLP based robust adaptive NN tracking control for underwater vehicle, Neurocomputing (ISSN ) 111 (2013) 184–189,. URL: http://www.sciencedirect.com/science/article/pii/S0925231213000787.

Digital Library

[41]

Shen Zhipeng, Bi Yannan, Wang Yu, Guo Chen, MLP neural network-based recursive sliding mode dynamic surface control for trajectory tracking of fully actuated surface vessel subject to unknown dynamics and input saturation, Neurocomputing (ISSN ) 377 (2020) 103–112,. URL: http://www.sciencedirect.com/science/article/pii/S0925231219313864.

Digital Library

[42]

Sariel S., Balch T., Erdogan N., Naval mine countermeasure missions, IEEE Robot. Autom. Mag. 15 (1) (2008) 45–52,.

[43]

Luo L., Chakraborty N., Sycara K., Distributed algorithms for multirobot task assignment with task deadline constraints, IEEE Trans. Autom. Sci. Eng. 12 (3) (2015) 876–888,.

[44]

Ferri G., Munafo A., Tesei A., LePage K., A market-based task allocation framework for autonomous underwater surveillance networks, in: OCEANS 2017 - Aberdeen, 2017, pp. 1–10,.

[45]

Otte Michael, Kuhlman Michael J., Sofge Donald, Auctions for multi-robot task allocation in communication limited environments, Auton. Robots (ISSN ) 44 (3) (2020) 547–584,.

[46]

Ayari Asma, Bouamama Sadok, ACD 3 GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization, Assem. Autom. (2019),. ahead-of-print.

[47]

Dutta Ayan, Czarnecki Emily, Ufimtsev Vladimir, Asaithambi Asai, Correlation clustering-based multi-robot task allocation: a tale of two graphs, ACM SIGAPP Appl. Comput. Rev. 19 (2020) 5–16,.

Digital Library

[48]

Li Jing, Yang Fan, Task assignment strategy for multi-robot based on improved grey wolf optimizer, J. Ambient Intell. Humaniz. Comput. (ISSN ) 11 (12) (2020) 6319–6335,.

[49]

Zhu D., Cao X., Sun B., Luo C., Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system, IEEE Trans. Cogn. Dev. Syst. 10 (2) (2018) 304–313,.

[50]

Dorigo M., Gambardella L.M., Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1 (1) (1997) 53–66,.

Digital Library

[51]

J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of ICNN’95 - International Conference on Neural Networks, Vol. 4, 1995, pp. 1942–1948, https://doi.org/10.1109/ICNN.1995.488968, vol.4.

[52]

Mirjalili Seyedali, Mirjalili Seyed Mohammad, Lewis Andrew, Grey wolf optimizer, Adv. Eng. Softw. (ISSN ) 69 (2014) 46–61,. URL: https://www.sciencedirect.com/science/article/pii/S0965997813001853.

Digital Library

[53]

Yazdani Danial, Nadjaran Toosi Adel, Meybodi Mohammad Reza, Fuzzy adaptive artificial fish swarm algorithm, in: Li Jiuyong (Ed.), AI 2010: Advances in Artificial Intelligence, Springer Berlin Heidelberg, Berlin, Heidelberg, ISBN 978-3-642-17432-2, 2011, pp. 334–343.

[54]

Passino K.M., Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Syst. Mag. 22 (3) (2002) 52–67,.

[55]

Karaboga Dervis, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report - TR06, Erciyes University, 2005.

[56]

Chen X., Zhang P., Du G., Li F., Ant colony optimization based memetic algorithm to solve bi-objective multiple traveling salesmen problem for multi-robot systems, IEEE Access 6 (2018) 21745–21757,.

[57]

Wei C., Ji Z., Cai B., Particle swarm optimization for cooperative multi-robot task allocation: A multi-objective approach, IEEE Robot. Autom. Lett. 5 (2) (2020) 2530–2537,.

[58]

Zhen Ziyang, Xing Dongjing, Gao Chen, Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm, Aerosp. Sci. Technol. (ISSN ) 76 (2018) 402–411,. URL: http://www.sciencedirect.com/science/article/pii/S1270963817301736.

[59]

Cai Wenyu, Zhang Meiyan, Zheng Rosa, Task assignment and path planning for multiple autonomous underwater vehicles using 3D dubins curves †, Sensors 17 (2017) 1607,.

[60]

Zhou Xing, Wang Huaimin, Ding Bo, Hu Tianjiang, Shang Suning, Balanced connected task allocations for multi-robot systems: An exact flow-based integer program and an approximate tree-based genetic algorithm, Expert Syst. Appl. (ISSN ) 116 (2019) 10–20,. URL: http://www.sciencedirect.com/science/article/pii/S0957417418305724.

[61]

Soulignac M., Feasible and optimal path planning in strong current fields, IEEE Trans. Robot. 27 (1) (2011) 89–98,.

Digital Library

[62]

Ammar Adel, Bennaceur Hachemi, Châari Imen, Koubâa Anis, Alajlan Maram, Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments, Soft Comput. (ISSN ) 20 (10) (2016) 4149–4171,.

Digital Library

[63]

Duchoň František, Babinec Andrej, Kajan Martin, Beňo Peter, Florek Martin, Fico Tomáš, Jurišica Ladislav, Path planning with modified a star algorithm for a mobile robot, Procedia Eng. (ISSN ) 96 (2014) 59–69,. URL: http://www.sciencedirect.com/science/article/pii/S187770581403149X. Modelling of Mechanical and Mechatronic Systems.

[64]

Li J., Lee M., Park S., Kim J., Real time path planning for a class of torpedo-type AUVs in unknown environment, in: 2012 IEEE/OES Autonomous Underwater Vehicles, AUV, 2012, pp. 1–6,.

[65]

Chen Z., Zhang Y., Zhang Y., Nie Y., Tang J., Zhu S., A hybrid path planning algorithm for unmanned surface vehicles in complex environment with dynamic obstacles, IEEE Access 7 (2019) 126439–126449,.

[66]

B. Sun, D. Zhu, Three dimensional D*Lite path planning for Autonomous Underwater Vehicle under partly unknown environment, in: 2016 12th World Congress on Intelligent Control and Automation, WCICA, 2016, pp. 3248–3252, https://doi.org/10.1109/WCICA.2016.7578444.

[67]

Petres C., Pailhas Y., Patron P., Petillot Y., Evans J., Lane D., Path planning for autonomous underwater vehicles, IEEE Trans. Robot. 23 (2) (2007) 331–341,.

Digital Library

[68]

E. Galceran, M. Carreras, Efficient seabed coverage path planning for ASVs and AUVs, in: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 88–93, https://doi.org/10.1109/IROS.2012.6385553.

[69]

Rekleitis Ioannis, New Ai Peng, Rankin Edward Samuel, Choset Howie, Efficient boustrophedon multi-robot coverage: an algorithmic approach, Ann. Math. Artif. Intell. (ISSN ) 52 (2) (2008) 109–142,.

Digital Library

[70]

Viet Hoang Huu, Dang Viet-Hung, Laskar Md Nasir Uddin, Chung TaeChoong, BA*: an online complete coverage algorithm for cleaning robots, Appl. Intell. (ISSN ) 39 (2) (2013) 217–235,.

Digital Library

[71]

Horváth Ernö, Pozna C., Precup Radu-Emil, Robot coverage path planning based on iterative structured orientation, Acta Polytech. Hung. 15 (2018) 231–249,.

[72]

Francis A., Faust A., Chiang H.-T.L., Hsu J., Kew J.C., Fiser M., Lee T.-W.E., Long-range indoor navigation with PRM-RL, IEEE Trans. Robot. 36 (4) (2020) 1115–1134,.

[73]

Sandström R., Uwacu D., Denny J., Amato N.M., Topology-guided roadmap construction With Dynamic Region sampling, IEEE Robot. Autom. Lett. 5 (4) (2020) 6161–6168,.

[74]

J. Denny, N.M. Amato, Toggle PRM: Simultaneous mapping of C-free and C-obstacle - A study in 2D, in: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 2632–2639, https://doi.org/10.1109/IROS.2011.6095102.

[75]

Karaman Sertac, Frazzoli Emilio, Sampling-based algorithms for optimal motion planning, Int. J. Robot. Res. (ISSN ) 30 (7) (2011) 846–894,.

Digital Library

[76]

Cui R., Li Y., Yan W., Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*, IEEE Trans. Syst. Man Cybern. 46 (7) (2016) 993–1004,.

[77]

Jeong In-Bae, Lee Seung-Jae, Kim Jong-Hwan, Quick-RRT*: Triangular inequality-based implementation of RRT* with improved initial solution and convergence rate, Expert Syst. Appl. (ISSN ) 123 (2019) 82–90,. URL: http://www.sciencedirect.com/science/article/pii/S0957417419300326.

Digital Library

[78]

Xu Jiajun, Park Kyoung-Su, A real-time path planning algorithm for cable-driven parallel robots in dynamic environment based on artificial potential guided RRT, Microsyst. Technol. (ISSN ) 26 (11) (2020) 3533–3546,.

Digital Library

[79]

Fan Xiaojing, Guo Yinjing, Liu Hui, Wei Bowen, Lyu Wenhong, Improved artificial potential field method applied for AUV path planning, Math. Probl. Eng. (ISSN ) 2020 (2020),.

[80]

Montiel Oscar, Orozco-Rosas Ulises, Sepúlveda Roberto, Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles, Expert Syst. Appl. (ISSN ) 42 (12) (2015) 5177–5191,. URL: http://www.sciencedirect.com/science/article/pii/S0957417415001402.

Digital Library

[81]

Wang Ning, Xu Hongwei, Li Chengzhong, Yin Jianchuan, Hierarchical path planning of unmanned surface vehicles: A fuzzy artificial potential field approach, Int. J. Fuzzy Syst. (ISSN ) (2020),.

[82]

Lumelsky Vladimir J., Stepanov Alexander A., Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape, Algorithmica (ISSN ) 2 (1) (1987) 403–430,.

Digital Library

[83]

Wei Dunwen, Wang Feiran, Ma Hongjiao, Autonomous path planning of AUV in large-scale complex marine environment based on swarm hyper-heuristic algorithm, Appl. Sci. (ISSN ) 9 (13) (2019),. URL: https://www.mdpi.com/2076-3417/9/13/2654.

[84]

Yan Zheping, Li Jiyun, Wu Yi, Zhang Gengshi, A real-time path planning algorithm for AUV in unknown underwater environment based on combining PSO and waypoint guidance, Sensors 19 (2018) 20,.

[85]

Gul Faiza, Rahiman Wan, Alhady S.S.N., Ali Ahmad, Mir Imran, Jalil Abdul, Meta-heuristic approach for solving multi-objective path planning for autonomous guided robot using PSO–GWO optimization algorithm with evolutionary programming, J. Ambient Intell. Humaniz. Comput. (ISSN ) (2020),.

[86]

Che Gaofeng, Liu Lijun, Yu Zhen, An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle, J. Ambient Intell. Humaniz. Comput. (ISSN ) 11 (8) (2020) 3349–3354,.

[87]

Yan Mingzhong, Zhu Daqi, Yang Simon X., A novel 3-D bio-inspired neural network model for the path planning of an AUV in underwater environments, Intell. Autom. Soft Comput. 19 (4) (2013) 555–566,.

[88]

Zhu Daqi, Tian Chen, Sun Bing, Luo Chaomin, Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm, J. Intell. Robot. Syst. (ISSN ) 94 (1) (2019) 237–249,.

Digital Library

[89]

Wang B., Liu Z., Li Q., Prorok A., Mobile robot path planning in dynamic environments through globally guided reinforcement learning, IEEE Robot. Autom. Lett. 5 (4) (2020) 6932–6939,.

[90]

Konar A., Goswami Chakraborty I., Singh S.J., Jain L.C., Nagar A.K., A deterministic improved Q-learning for path planning of a mobile robot, IEEE Trans. Syst. Man Cybern. 43 (5) (2013) 1141–1153,.

[91]

K. Balan, C. Luo, Optimal Trajectory Planning for Multiple Waypoint Path Planning using Tabu Search, in: 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference, UEMCON, 2018, pp. 497–501, https://doi.org/10.1109/UEMCON.2018.8796810.

[92]

Tharwat Alaa, Elhoseny Mohamed, Hassanien Aboul Ella, Gabel Thomas, Kumar Arun, Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm, Cluster Comput. (ISSN ) 22 (2) (2019) 4745–4766,.

Digital Library

[93]

Ma Jianwei, Liu Yang, Zang Shaofei, Wang Lin, Robot path planning based on genetic algorithm fused with continuous bezier optimization, Comput. Intell. Neurosci. (ISSN ) 2020 (2020),.

Digital Library

[94]

Wang Y., Cai W., Zheng Y.R., Dubins curves for 3D multi-vehicle path planning using spline interpolation, in: OCEANS 2017 - Anchorage, 2017, pp. 1–5.

[95]

B. Shi, Y. Su, C. Wang, L. Wan, Y. Qi, Recovery Path Planning Algorithm Based on Dubins Curve for Autonomous Underwater Vehicle, in: 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications, USYS, 2018, pp. 1–5, https://doi.org/10.1109/USYS.2018.8778859.

[96]

Ulyanov Sergey, Bychkov Igor, Maksimkin Nikolay, Event-based path-planning and path-following in unknown environments for underactuated autonomous underwater vehicles., Appl. Sci. (2076-3417) 10 (21) (2020).

[97]

R. T. Rodrigues, A. P. Aguiar, A. Pascoal, A coverage planner for AUVs using B-splines, in: 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV, 2018, pp. 1–6, https://doi.org/10.1109/AUV.2018.8729760.

[98]

Wang Xiaowei, Yao Xuliang, Zhang Le, Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances, J. Intell. Robot. Syst. (ISSN ) 99 (3) (2020) 891–908,.

Digital Library

[99]

Ru Jingyu, Yu Shuangjiang, Wu Hao, Li Yuhan, Wu Chengdong, Jia Zixi, Xu Hongli, A multi-AUV path planning system based on the omni-directional sensing ability, J. Mar. Sci. Eng. (ISSN ) 9 (8) (2021),. URL: https://www.mdpi.com/2077-1312/9/8/806.

[100]

Khatib Oussama, Real-time obstacle avoidance for manipulators and mobile robots, 1, 1985, pp. 500–505,.

[101]

Min Zhang, Yi Shen, Qiang Wang, Yibo Wang, Dynamic artificial potential field based multi-robot formation control, in: 2010 IEEE Instrumentation Measurement Technology Conference Proceedings, 2010, pp. 1530–1534, https://doi.org/10.1109/IMTC.2010.5488238.

[102]

Yao Peng, Qi ShengBo, Obstacle-avoiding path planning for multiple autonomous underwater vehicles with simultaneous arrival, Sci. China Technol. Sci. (ISSN ) 62 (1) (2019) 121–132,.

[103]

Nelson Derek R., Barber D. Blake, McLain Timothy W., Beard Randal W., Vector field path following for miniature air vehicles, IEEE Trans. Robot. 23 (3) (2007) 519–529,.

Digital Library

[104]

Ko Inyoung, Kim Beobkyoon, Park Frank Chongwoo, Randomized path planning on vector fields, Int. J. Robot. Res. 33 (13) (2014) 1664–1682,. eprint: https://doi.org/10.1177/0278364914545812.

Digital Library

[105]

Sun Yuanxi, Gu Rui, Chen Xiaohong, Sun Rui, Xin Liming, Bai Long, Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy, Ocean Eng. (ISSN ) 259 (2022),. URL: https://www.sciencedirect.com/science/article/pii/S002980182201246X.

[106]

McGuire K.N., de Croon G.C.H.E., Tuyls K., A comparative study of bug algorithms for robot navigation, Robot. Auton. Syst. (ISSN ) 121 (2019),. URL: https://www.sciencedirect.com/science/article/pii/S0921889018306687.

Digital Library

[107]

Ng James, Bräunl Thomas, Performance comparison of bug navigation algorithms, J. Intell. Robot. Syst. (ISSN ) 50 (1) (2007) 73–84,.

Digital Library

[108]

Zeng Zheng, Sammut Karl, Lian Lian, He Fangpo, Lammas Andrew, Tang Youhong, A comparison of optimization techniques for AUV path planning in environments with ocean currents, Robot. Auton. Syst. (ISSN ) 82 (2016) 61–72,. URL: http://www.sciencedirect.com/science/article/pii/S0921889016301713.

Digital Library

[109]

Wu Yu, Low Kin Huat, Lv Chen, Cooperative path planning for heterogeneous unmanned vehicles in a search-and-track mission aiming at an underwater target, IEEE Trans. Veh. Technol. 69 (6) (2020) 6782–6787,.

[110]

Dubins L.E., Amer. J. Math. 79 (3) (1957) 497–516. (ISSN: 00029327, 10806377). URL: http://www.jstor.org/stable/2372560.

[111]

Cao Junliang, Cao Junjun, Zeng Zheng, Yao Baoheng, Lian Lian, Toward optimal rendezvous of multiple underwater gliders: 3D path planning with combined sawtooth and spiral motion, J. Intell. Robot. Syst. (ISSN ) 85 (1) (2017) 189–206,.

Digital Library

[112]

Reynolds Craig W., Flocks, herds, and schools: A distributed behavioral model, in: Seminal Graphics: Pioneering Efforts that Shaped the Field, Association for Computing Machinery, New York, NY, USA, ISBN 158113052X, 1998, pp. 273–282. URL: https://doi.org/10.1145/280811.281008.

[113]

Olfati-Saber R., Flocking for multi-agent dynamic systems: algorithms and theory, IEEE Trans. Automat. Control 51 (3) (2006) 401–420,.

[114]

Gu D., Wang Z., Leader–follower flocking: Algorithms and experiments, IEEE Trans. Control Syst. Technol. 17 (5) (2009) 1211–1219,.

[115]

B.K. Sahu, B. Subudhi, B.K. Dash, Flocking control of multiple autonomous underwater vehicles, in: 2012 Annual IEEE India Conference, INDICON, 2012, pp. 257–262, https://doi.org/10.1109/INDCON.2012.6420625.

[116]

Sahu B.K., Subudhi B., Flocking control of multiple AUVs based on fuzzy potential functions, IEEE Trans. Fuzzy Syst. 26 (5) (2018) 2539–2551,.

Digital Library

[117]

Antonelli Gianluca, Arrichiello Filippo, Chiaverini Stefano, Flocking for multi-robot systems via the null-space-based behavioral control, Swarm Intell. (ISSN ) 4 (1) (2009) 37,.

[118]

Cucker F., Smale S., Emergent behavior in flocks, IEEE Trans. Automat. Control 52 (5) (2007) 852–862,.

[119]

Jia Yongnan, Wang Long, Experimental implementation of distributed flocking algorithm for multiple robotic fish, Control Eng. Pract. (ISSN ) 30 (2014) 1–11,. URL: https://www.sciencedirect.com/science/article/pii/S096706611400149X.

[120]

Zhang H., Liu B., Cheng Z., Chen G., Model predictive flocking control of the cucker-smale multi-agent model with input constraints, IEEE Trans. Circuits Syst. I. Regul. Pap. 63 (8) (2016) 1265–1275,.

[121]

S. Lee, H. Myung, Interval type-2 fuzzy logic controllers for flocking behavior, in: 5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011), 2011, pp. 270–273, https://doi.org/10.1109/DEST.2011.5936637.

[122]

Chiesa Stefano, Taraglio Sergio, Pagnottelli. Stefano, Valigi Paolo, Flocking approach to spatial configuration control in underwater swarms, in: Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, SciTePress, INSTICC, ISBN 978-989-8565-21-1, 2012, pp. 313–316,.

[123]

Zhao Xiao-Wen, Chi Ming, Guan Zhi-Hong, Hu Bin, Zhang Xian-He, Flocking of multiple three-dimensional nonholonomic agents with proximity graph, J. Franklin Inst. B (ISSN ) 354 (8) (2017) 3617–3633,. URL: https://www.sciencedirect.com/science/article/pii/S0016003217301217.

[124]

Zhang Wei, Zeng Jia, Zhang Jun, Li Zixuan, H∞ consensus tracking of recovery system for multiple unmanned underwater vehicles with switching networks and disturbances, Ocean Eng. (ISSN ) 245 (2022),. URL: https://www.sciencedirect.com/science/article/pii/S0029801822000609.

[125]

Florian Berlinger, Paula Wulkop, Radhika Nagpal, Self-Organized Evasive Fountain Maneuvers with a Bioinspired Underwater Robot Collective, in: 2021 IEEE International Conference on Robotics and Automation, ICRA, 2021, pp. 9204–9211, https://doi.org/10.1109/ICRA48506.2021.9561407.

[126]

Berlinger Florian, Gauci Melvin, Nagpal Radhika, Implicit coordination for 3D underwater collective behaviors in a fish-inspired robot swarm, Science Robotics 6 (50) (2021),. eprint: https://robotics.sciencemag.org/content/6/50/eabd8668.full.pdf. URL: https://robotics.sciencemag.org/content/6/50/eabd8668.

[127]

Kim Jin Hoe, Yoo Sung Jin, Distributed event-triggered adaptive output-feedback formation tracking of uncertain underactuated underwater vehicles in three-dimensional space, Appl. Math. Comput. (ISSN ) 424 (2022),. URL: https://www.sciencedirect.com/science/article/pii/S0096300322001321.

Digital Library

[128]

A. Pedro Aguiar, Single and multiple motion control of autonomous robotic vehicles, in: 2017 11th International Workshop on Robot Motion and Control (RoMoCo), 2017, pp. 172–184, https://doi.org/10.1109/RoMoCo.2017.8003910.

[129]

Yan Zheping, Yang Zewen, Jiang Anzuo, Teng Yanbin, Liu Xiangling, Wei Shilin, Coordinated control for trajectory tracking of multiple UUVs with input saturation, in: OCEANS 2019 - Marseille, 2019, pp. 1–5,.

[130]

Bian Jingwei, Xiang Ji, Three-dimensional coordination control for multiple autonomous underwater vehicles, IEEE Access 7 (2019) 63913–63920,.

[131]

Attallah Aly, Datar Adwait, Werner Herbert, Flocking of linear parameter varying agents: Source seeking application with underwater vehicles, IFAC-PapersOnLine (ISSN ) 53 (2) (2020) 7305–7311,. URL: https://www.sciencedirect.com/science/article/pii/S2405896320313422. 21st IFAC World Congress.

[132]

Liang Xiao, Qu Xingru, Hou Yuanhang, Li Ye, Zhang Rubo, Finite-time unknown observer based coordinated path-following control of unmanned underwater vehicles, J. Franklin Inst. B (ISSN ) 358 (5) (2021) 2703–2721,. URL: https://www.sciencedirect.com/science/article/pii/S0016003221000533.

[133]

Wang Hao, Tian Yu, Xu Hongli, Neural adaptive command filtered control for cooperative path following of multiple underactuated autonomous underwater vehicles along one path, IEEE Trans. Syst. Man Cybern. 52 (5) (2022) 2966–2978,.

[134]

Cao Xiang, Sun Hongbing, Jan Gene Eu, Multi-AUV cooperative target search and tracking in unknown underwater environment, Ocean Eng. (ISSN ) 150 (2018) 1–11,. URL: https://www.sciencedirect.com/science/article/pii/S0029801817307655.

[135]

Qi Xue, Cai Zhi-Jun, Cooperative pursuit control for multiple underactuated underwater vehicles with time delay in three-dimensional space, Robotica 39 (6) (2021) 1101–1115,.

[136]

Song Dalei, Gan Wenhao, Yao Peng, Zang Wenchuan, Zhang Zhixuan, Qu Xiuqing, Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning, Ocean Eng. (ISSN ) 250 (2022),. URL: https://www.sciencedirect.com/science/article/pii/S002980182200378X.

[137]

Paull L., Saeedi S., Seto M., Li H., AUV navigation and localization: A review, IEEE J. Ocean. Eng. 39 (1) (2014) 131–149,.

[138]

Lin C., Wang H., Fu M., Yuan J., Gu J., A gated recurrent unit-based particle filter for unmanned underwater vehicle state estimation, IEEE Trans. Instrum. Meas. 70 (2021) 1–12,.

[139]

Sun Jie, Hu Feng, Jin Wenming, Wang Jin, Wang Xu, Luo Yeteng, Yu Jiancheng, Zhang Aiqun, Model-aided localization and navigation for underwater gliders using single-beacon travel-time differences, Sensors (ISSN ) 20 (3) (2020),. URL: https://www.mdpi.com/1424-8220/20/3/893.

[140]

Allotta B., Caiti A., Costanzi R., Fanelli F., Fenucci D., Meli E., Ridolfi A., A new AUV navigation system exploiting unscented Kalman filter, Ocean Eng. (ISSN ) 113 (2016) 121–132,. URL: https://www.sciencedirect.com/science/article/pii/S0029801815007271.

[141]

Aggarwal Achint, Kampmann Peter, Lemburg Johannes, Kirchner Frank, Haptic object recognition in underwater and deep-sea environments, J. Field Robotics 32 (1) (2015) 167–185,. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21538. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21538.

Digital Library

[142]

Guerneve Thomas, Subr Kartic, Petillot Yvan, Three-dimensional reconstruction of underwater objects using wide-aperture imaging SONAR, J. Field Robotics 35 (6) (2018) 890–905,. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21783. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21783.

[143]

Cai L., Sun Q., Xu T., Ma Y., Chen Z., Multi-AUV collaborative target recognition based on transfer-reinforcement learning, IEEE Access 8 (2020) 39273–39284,.

[144]

Kim J., Cooperative localization and unknown currents estimation using multiple autonomous underwater vehicles, IEEE Robot. Autom. Lett. 5 (2) (2020) 2365–2371,.

[145]

Oubei Hassan, Retamal Jose, Janjua Bilal, Wang Huai-Yung, Tsai Cheng-Ting, Chi Yu-Chieh, Ng Tien Khee, Kuo Hao-Chung, He Jr-Hau, Alouini Mohamed-Slim, Lin Gong-Ru, Ooi Boon, 48 Gbit/s 16-QAM-OFDM transmission based on compact 450-nm laser for underwater wireless optical communication, Opt. Express 23 (2015) 23302,.

[146]

Pompili Dario, Akyildiz Ian F., Overview of networking protocols for underwater wireless communications, Commun. Mag. (ISSN ) 47 (1) (2009) 97–102,.

Digital Library

[147]

Gulbahar B., Akan O.B., A communication theoretical modeling and analysis of underwater magneto-inductive wireless channels, IEEE Trans. Wireless Commun. 11 (9) (2012) 3326–3334,.

[148]

Wei D., Yan L., Huang C., Wang J., Chen J., Pan M., Fang Y., Dynamic magnetic induction wireless communications for autonomous-underwater-vehicle-assisted underwater IoT, IEEE Internet Things J. 7 (10) (2020) 9834–9845,.

[149]

Yan Zheping, Yang Zewen, Pan Xiaoli, Zhou Jiajia, Wu Di, Virtual leader based path tracking control for multi-UUV considering sampled-data delays and packet losses, Ocean Eng. (ISSN ) 216 (2020),. URL: https://www.sciencedirect.com/science/article/pii/S0029801820310076.

[150]

Liang H., Fu Y., Kang F., Gao J., Qiang N., A behavior-driven coordination control framework for target hunting by UUV intelligent swarm, IEEE Access 8 (2020) 4838–4859,.

[151]

Liang Hongtao, Fu Yanfang, Gao Jie, Bio-inspired self-organized cooperative control consensus for crowded UUV swarm based on adaptive dynamic interaction topology, Appl. Intell. (ISSN ) (2021),.

Digital Library

[152]

L.I.U. Ming-Yong, L.I. Wen-Bai, PEI Xuan, Convex optimization algorithms for cooperative localization in autonomous underwater vehicles, Acta Automat. Sinica (ISSN ) 36 (5) (2010) 704–710,. URL: https://www.sciencedirect.com/science/article/pii/S1874102909600318.

[153]

Xu Bo, Xiao Yong ping, Gao Wei, Zhang Yong gang, Liu Ya Long, Liu Yang, Dual-model reverse CKF algorithm in cooperative navigation for USV, Math. Probl. Eng. (ISSN ) 2014 (2014),.

[154]

Li Qian, Ben Yueyang, Naqvi Syed Mohsen, Neasham Jeffrey A., Chambers Jonathon A., Robust student’s t -based cooperative navigation for autonomous underwater vehicles, IEEE Trans. Instrum. Meas. 67 (8) (2018) 1762–1777,.

[155]

Xu Bo, Guo Yu, Wang Lianzhao, Zhang Jiao, A novel robust Gaussian approximate smoother based on EM for cooperative localization with sensor fault and outliers, IEEE Trans. Instrum. Meas. 70 (2021) 1–14,.

[156]

Xu Bo, Li Shengxin, Razzaqi Asghar A., Zhang Jiao, Cooperative localization in harsh underwater environment based on the MC-ANFIS, IEEE Access 7 (2019) 55407–55421,.

[157]

Xiao Guangdi, Wang Bo, Deng Zhihong, Fu Mengyin, Ling Yun, An acoustic communication time delays compensation approach for master–slave AUV cooperative navigation, IEEE Sens. J. 17 (2) (2017) 504–513,.

[158]

Zhang Lichuan, Li Yichen, Liu Lu, Tao Xiuye, Cooperative navigation based on cross entropy: Dual leaders, IEEE Access 7 (2019) 151378–151388,.

[159]

Peng Y., Cosman P.C., Underwater image restoration based on image blurriness and light absorption, IEEE Trans. Image Process. 26 (4) (2017) 1579–1594,.

Digital Library

[160]

Martínez-Barberá Humberto, Bernal-Polo Pablo, Herrero-Pérez David, Sensor modeling for underwater localization using a particle filter, Sensors (ISSN ) 21 (4) (2021),. URL: https://www.mdpi.com/1424-8220/21/4/1549.

[161]

Guo Jia, He Bo, Sha Qixin, Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV, Ocean Eng. (ISSN ) 148 (2018) 386–400,. URL: https://www.sciencedirect.com/science/article/pii/S0029801817306820.

[162]

L. Fu, F. Xie, D. Wang, G. Meng, The overview for UAV Air-Combat Decision method, in: The 26th Chinese Control and Decision Conference (2014 CCDC), 2014, pp. 3380–3384, https://doi.org/10.1109/CCDC.2014.6852760.

[163]

Yao Hongfei, Wang Hongjian, Wang Ying, UUV autonomous decision-making method based on dynamic influence diagram, Complexity (ISSN ) 2020 (2020),.

Digital Library

[164]

Mnih Volodymyr, Kavukcuoglu Koray, Silver David, Rusu Andrei A., Veness Joel, Bellemare Marc G., Graves Alex, Riedmiller Martin, Fidjeland Andreas K., Ostrovski Georg, Petersen Stig, Beattie Charles, Sadik Amir, Antonoglou Ioannis, King Helen, Kumaran Dharshan, Wierstra Daan, Legg Shane, Hassabis Demis, Human-level control through deep reinforcement learning, Nature (ISSN ) 518 (7540) (2015) 529–533,.

[165]

Han Guangjie, Gong Aini, Wang Hao, Martínez-García Miguel, Peng Yan, Multi-AUV collaborative data collection algorithm based on Q-learning in underwater acoustic sensor networks, IEEE Trans. Veh. Technol. 70 (9) (2021) 9294–9305,.

[166]

Zhuo Xiaoxiao, Liu Meiyan, Wei Yan, Yu Guanding, Qu Fengzhong, Sun Rui, AUV-aided energy-efficient data collection in underwater acoustic sensor networks, IEEE Internet Things J. 7 (10) (2020) 10010–10022,.

[167]

Jaffe Jules S., Franks Peter J.S., Roberts Paul L.D., Mirza Diba, Schurgers Curt, Kastner Ryan, Boch Adrien, A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics, Nature Commun. (ISSN ) 8 (1) (2017) 14189,.

[168]

Zheng Huanyang, Wang Ning, Wu Jie, Minimizing deep sea data collection delay with autonomous underwater vehicles, J. Parallel Distrib. Comput. (ISSN ) 104 (2017) 99–113,. URL: https://www.sciencedirect.com/science/article/pii/S0743731517300126.

[169]

Li Guannan, Chen Chao, Geng Chao, Li Meng, Xu Hongli, Lin Yang, A pheromone-inspired monitoring strategy using a swarm of underwater robots, Sensors (ISSN ) 19 (19) (2019),. URL: https://www.mdpi.com/1424-8220/19/19/4089.

[170]

Gupta Shalabh, Hare James, Zhou Shengli, Cooperative coverage using autonomous underwater vehicles in unknown environments, in: 2012 Oceans, 2012, pp. 1–5,.

[171]

Sousselier Thomas, Dreo Johann, Sevaux Marc, Line formation algorithm in a swarm of reactive robots constrained by underwater environment, Expert Syst. Appl. (ISSN ) 42 (12) (2015) 5117–5127,. URL: https://www.sciencedirect.com/science/article/pii/S0957417415001475.

Digital Library

[172]

N. D. Griffiths Sànchez, P. A. Vargas, M. S. Couceiro, A Darwinian Swarm Robotics Strategy Applied to Underwater Exploration, in: 2018 IEEE Congress on Evolutionary Computation, CEC, 2018, pp. 1–6, https://doi.org/10.1109/CEC.2018.8477738.

[173]

Tsiogkas N., Saigol Z., Lane D., Distributed multi-AUV cooperation methods for underwater archaeology, in: OCEANS 2015 - Genova, 2015, pp. 1–5,.

[174]

Cao X., Zhu D., Yang S.X., Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments, IEEE Trans. Neural Netw. Learn. Syst. 27 (11) (2016) 2364–2374,.

[175]

Liu Yuan, Wang Min, Su Zhou, Luo Jun, Xie Shaorong, Peng Yan, Pu Huayan, Xie Jiajia, Zhou Rui, Multi-AUVs cooperative target search based on autonomous cooperative search learning algorithm, J. Mar. Sci. Eng. (ISSN ) 8 (11) (2020),. URL: https://www.mdpi.com/2077-1312/8/11/843.

[176]

Xiang Cao, Daqi Zhu, A survey of cooperative hunting control algorithms for multi-AUV systems, in: Proceedings of the 32nd Chinese Control Conference, 2013, pp. 5791–5795.

[177]

Wei Na, Liu Mingyong, Cheng Weibin, Decision-making of underwater cooperative confrontation based on MODPSO, Sensors (ISSN ) 19 (9) (2019),. URL: https://www.mdpi.com/1424-8220/19/9/2211.

[178]

Cao Xiang, Xu Xinyuan, Hunting algorithm for multi-AUV based on dynamic prediction of target trajectory in 3D underwater environment, IEEE Access 8 (2020) 138529–138538,.

[179]

Chen Mingzhi, Zhu Daqi, A novel cooperative hunting algorithm for inhomogeneous multiple autonomous underwater vehicles, IEEE Access 6 (2018) 7818–7828,.

[180]

Ge Hengqing, Chen Guibin, Xu Guang, Multi-AUV cooperative target hunting based on improved potential field in a surface-water environment, Appl. Sci. (ISSN ) 8 (6) (2018),. URL: https://www.mdpi.com/2076-3417/8/6/973.

[181]

Cao Xiang, Guo Liqiang, A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments, Int. J. Adv. Robot. Syst. 16 (4) (2019),. eprint: https://doi.org/10.1177/1729881419870664.

[182]

Zhang Zhongshan, Long Keping, Wang Jianping, Dressler Falko, On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches, IEEE Commun. Surv. Tutor. 16 (1) (2014) 513–537,.

[183]

Tang Jun, Liu Gang, Pan Qingtao, A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends, IEEE/CAA J. Autom. Sin. 8 (10) (2021) 1627–1643,.

[184]

Boussaïd Ilhem, Lepagnot Julien, Siarry Patrick, A survey on optimization metaheuristics, Inform. Sci. (ISSN ) 237 (2013) 82–117,. URL: https://www.sciencedirect.com/science/article/pii/S0020025513001588. Prediction, Control and Diagnosis using Advanced Neural Computations.

Digital Library

[185]

Yu Xue, Chen Wei-Neng, Hu Xiao-Min, Gu Tianlong, Yuan Huaqiang, Zhou Yuren, Zhang Jun, Path planning in multiple-AUV systems for difficult target traveling missions: A hybrid metaheuristic approach, IEEE Trans. Cogn. Dev. Syst. 12 (3) (2020) 561–574,.

[186]

Yunhong Ma, Heng Zhang, Yaozhong Zhang, Ruizhou Gao, Zhao Xu, Jie Yang, Coordinated Optimization Algorithm Combining GA with Cluster for Multi-UAVs to Multi-tasks Task Assignment and Path Planning, in: 2019 IEEE 15th International Conference on Control and Automation, ICCA, 2019, pp. 1026–1031, https://doi.org/10.1109/ICCA.2019.8899987.

[187]

Zheng Zeng, Andrew Lammas, Karl Sammut, Fangpo He, Optimal path planning based on annular space decomposition for AUVs operating in a variable environment, in: 2012 IEEE/OES Autonomous Underwater Vehicles, AUV, 2012, pp. 1–9, https://doi.org/10.1109/AUV.2012.6380759.

[188]

Liu Xuxun, Routing protocols based on ant colony optimization in wireless sensor networks: A survey, IEEE Access 5 (2017) 26303–26317,.

[189]

Zhang Qilei, Lin Jinying, Sha Qixin, He Bo, Li Guangliang, Deep interactive reinforcement learning for path following of autonomous underwater vehicle, IEEE Access 8 (2020) 24258–24268,.

[190]

Cao Xiang, Sun Changyin, Yan Mingzhong, Target search control of AUV in underwater environment with deep reinforcement learning, IEEE Access 7 (2019) 96549–96559,.

[191]

Queralta Jorge Peña, Taipalmaa Jussi, Can Pullinen Bilge, Sarker Victor Kathan, Nguyen Gia Tuan, Tenhunen Hannu, Gabbouj Moncef, Raitoharju Jenni, Westerlund Tomi, Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision, IEEE Access 8 (2020) 191617–191643,.

[192]

Cai Wenyu, Xie Qinan, Zhang Meiyan, Lv Shuaishuai, Yang Junyi, Stream-function based 3D obstacle avoidance mechanism for mobile AUVs in the internet of underwater things, IEEE Access 9 (2021) 142997–143012,.

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cover image Robotics and Autonomous Systems

Robotics and Autonomous Systems Volume 164, Issue C

Jun 2023

293 pages

ISSN:0921-8890

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Elsevier B.V.

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North-Holland Publishing Co.

Netherlands

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Published: 01 June 2023

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Cited By

Feng HHu QZhao ZFeng XJiang C(2025)A varied-width path planning method for multiple AUV formationComputers and Industrial Engineering10.1016/j.cie.2024.110746199:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.cie.2024.110746
Bai GChen YHu XCui J(2024)Correction Redistribution Mechanism Based on Forward-Reverse Solutions and Real-Time Path Dynamic Adaptive Re-Planning for Multi-AUVs Collaborative SearchIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335290225:7(7583-7601)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3352902
Ardiny HBeigzadeh A(2024)Enhancing radioactive environment exploration with bio-inspired swarm roboticsRobotics and Autonomous Systems10.1016/j.robot.2024.104794181:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.robot.2024.104794

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