Researching | Research advances in the development status and key technology of unmanned marine vehicle swarm operation

[5] SHANEN SHAH A F M, ILHAN H, TURELI U. CB-MAC: a novel cluster-based MAC protocol for VANETs[J]. IET Intelligent Transport Systems, 13, 587-595(2019).

[6] [6] JAVADI M, MOSTAFAEI H, CHOWDHURRY M U, et al. Learning automaton based topology control protocol f extending wireless sens wks lifetime[JOL]. Journal of wk Computer Applications, 2018, 122: 128–136.

[7] [7] XU W, DANESHM, LIU W. A data privacy protective mechanism f WBAN[JOL]. Wireless Communications Mobile Computing, 2015: 421–430. https:doi.g10.1002wcm.

[8] GUI J S, ZHOU K. Flexible adjustments between energy and capacity for topology control in heterogeneous wireless multi-hop networks[J]. Journal of Network and Systems Management, 24, 789-812(2016).

[9] WAQAS A, MAHMOOD H, ZARIFZADEH S. A data privacy protective mechanism for WBAN[J]. Computer Networks, 3819, 24-30(2019).

[10] HAYAT S, YANMAZ E, MUZAFFAR R. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint[J]. IEEE Communications Surveys & Tutorials, 18, 2624-2661(2016).

[15] WANG Q Y, CUI X F, LI Y B et al. Performance enhancement of a USV INS/CNS/DVL integration navigation system based on an adaptive information sharing factor federated filter[J]. Sensors, 17, 239(2017).

[16] XIAO F Y. Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy[J]. Information Fusion, 46, 23-32(2019).

[20] [20] AN S, KIM H J. Simultaneous task assignment path planning using mixedinteger linear programming potential field method[C]2013 13th International Conference on Control, Automation Systems (ICCAS 2013). Gwangju, South Kea: IEEE, 2014.

[21] ZHU D Q, HUANG H, YANG S X. Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace[J]. IEEE Transactions on Cybernetics, 43, 504-514(2013).

[22] LEVCHUK G M, LEVCHUK Y N, MEIRINA C et al. Normative design of project-based organizations-part III: modeling congruent, robust, and adaptive organizations[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 34, 337-350(2004).

[23] HOANG P D, RABAEY J M. Scheduling of DSP programs onto multiprocessors for maximum throughput[J]. IEEE Transactions on Signal Processing, 41, 2225-2235(1993).

[26] [26] HAKSAR R N, SCHWAGER M. Distributed deep reinfcement learning f fighting fest fires with a wk of aerial robots[C]2018 IEEERSJ International Conference on Intelligent Robots Systems (IROS). Madrid, Spain: IEEE, 2018.

[27] HU J, XIE L, LUM K Y et al. Multiagent information fusion and cooperative control in target search[J]. IEEE Transactions on Control Systems Technology, 21, 1223-1235(2013).

[28] [28] JIN Y N, WU Y X, FAN N J. Research on distributed cooperative control of swarm UAVs f persistent coverage[C]Proceedings of the 33rd Chinese Control Conference. Nanjing, China: IEEE, 2014.

[29] SMITH R G. The contract net protocol: high-level communication and control in a distributed problem solver[J]. IEEE Transactions on Computers, C-29, 1104-1113(1980).

[30] FU X W, FENG P, GAO X G. Swarm UAVs task and resource dynamic assignment algorithm based on task sequence mechanism[J]. IEEE Access, 7, 41090-41100(2019).

[31] [31] HUNT S, MENG Q, HINDE C J. An extension of the consensusbased bundle algithm f multiagent tasks with task based requirements[C]2012 11th International Conference on Machine Learning Applications. Boca Raton, FL, USA: IEEE, 2013.

[32] SHANMUGAVEL M, TSOURDOS A, WHITE B et al. Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs[J]. Control Engineering Practice, 18, 1084-1092(2010).

[33] [33] LIU J, HE Y F, ZHANG A P. An improved mutliACS algithm f the waste collection vehicle arc routing problem with turn constraints[C]2014 Tenth International Conference on Computational Intelligence Security. Kunming, China: IEEE, 2015.

[34] ARI I, AKSAKALLI V, AYDOĞDU V et al. Optimal ship navigation with safety distance and realistic turn constraints[J]. European Journal of Operational Research, 229, 707-717(2013).

[35] KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research, 5, 90-98(1986).

[36] EDISON E, SHIMA T. Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms[J]. Computers & Operations Research, 38, 340-356(2011).

[37] NIKOLOS I K, VALAVANIS K P, TSOURVELOUDIS N C et al. Evolutionary algorithm based offline/online path planner for UAV navigation[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 33, 898-912(2003).

[38] BRINTAKI A N, NIKOLOS I K. Coordinated UAV path planning using differential evolution[J]. Operational Research, 5, 487-502(2005).

[39] [39] MITTAL S, DEB K. Threedimensional offline path planning f UAVs using multiobjective evolutionary algithms[C]2007 IEEE Congress on Evolutionary Computation. Singape: IEEE, 2007.

[40] [40] ÖZALP N, SAHINGOZ O K. Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algithms[C]2013 International Conference on Unmanned Aircraft Systems (ICUAS). Atlanta, GA, USA: IEEE, 2013: 4957.

[41] [41] SHEN X N. A quantum evolutionary algithm f robot path planning in dynamic environment[C]Proceedings of the 32nd Chinese Control Conference. Xi''an, China: IEEE, 2013.

[42] [42] CHENG Z, SUN Y, LIU Y L. Path planning based on immune geic algithm f UAV[C]2011 International Conference on Electric Infmation Control Engineering. Wuhan, China: IEEE, 2011.

[43] [43] HASIRCIOGLU I, TOPCUOGLU H R, ERMIS M. 3D path planning f the navigation of unmanned aerial vehicles by using evolutionary algithms[C]Proceedings of the 10th Annual Conference on Geic Evolutionary Computation. Atlanta, GA, USA: ACM, 2008.

[44] [44] ALSABBAN W H, GONZALEZ L F, SMITH R N. Windenergy based path planning f unmanned aerial vehicles using Markov decision processes[C]2013 IEEE International Conference on Robotics Automation. Karlsruhe, Germany: IEEE, 2013.

[45] RAGI S, CHONG E K P. UAV path planning in a dynamic environment via partially observable Markov decision process[J]. IEEE Transactions on Aerospace and Electronic Systems, 49, 2397-2412(2013).

[46] [46] FEYZABADI S, CARPIN S. Riskaware path planning using hirerachical constrained markov decision processes[C]2014 IEEE International Conference on Automation Science Engineering (CASE). Taipei, Taiwan, China: IEEE, 2014.

[47] ZHANG B C, MAO Z L, LIU W Q et al. Geometric reinforcement learning for path planning of UAVs[J]. Journal of Intelligent & Robotic Systems, 77, 391-409(2015).

[48] [48] YANG H Y, GUO Q, XU X, et al. Selflearning PD algithms based on approximate dynamic programming f robot motion planning[C]2014 International Joint Conference on Neural wks (IJCNN). Beijing, China: IEEE, 2014.

[49] KONAR A, CHAKRABORTY I G, SINGH S J et al. A deterministic improved Q-learning for path planning of a mobile robot[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43, 1141-1153(2013).

[50] ASL S V, DAVARZANI Z, STAJI S. Planning flying robot navigation in a three-dimensional space by optimaztion combining Q-learning and Monte Carlo algorithms[J]. International Journal of Hybrid Information Technology, 8, 297-306(2015).

[51] JI Z J, WANG Z D, LIN H et al. Interconnection topologies for multi-agent coordination under leader-follower framework[J]. Automatica, 45, 2857-2863(2009).

[52] XIAO H Z, CHEN C L P. Leader-follower consensus multi-robot formation control using neurodynamic-optimization-based nonlinear model predictive control[J]. IEEE Access, 7, 43581-43590(2019).

[53] LAWTON J R T, BEARD R W, YOUNG B J. A decentralized approach to formation maneuvers[J]. IEEE Transactions on Robotics & Automation, 19, 933-941(2004).

[54] JADBABAIE A, LIN J, MORSE A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules[J]. IEEE Transactions on Automatic Control, 48, 988-1001(2003).

[57] DO K D, PAN J. Nonlinear formation control of unicycle-type mobile robots[J]. Robotics and Autonomous Systems, 55, 191-204(2007).

[58] RAMAZANI S, SELMIC R, DE QUEIROZ M. Rigidity-based multiagent layered formation control[J]. IEEE Transactions on Cybernetics, 47, 1902-1913(2017).

[59] ZHOU D J, WANG Z J, SCHWAGER M. Agile coordination and assistive collision avoidance for quadrotor swarms using virtual structures[J]. IEEE Transactions on Robotics, 34, 916-923(2018).

[61] LIU H, MENG Q Y, PENG F C et al. Heterogeneous formation control of multiple UAVs with limited-input leader via reinforcement learning[J]. Neurocomputing, 412, 63-71(2020).

[62] LA H M, SHENG W H. Distributed sensor fusion for scalar field mapping using mobile sensor networks[J]. IEEE Transactions on Cybernetics, 43, 766-778(2013).

[64] LI L, WANG X, WANG K F et al. Parallel testing of vehicle intelligence via virtual-real interaction[J]. Science Robotics, 4, eaaw4106(2019).

[66] KANG Y, YIN H, BERGER C. Test your self-driving algorithm: an overview of publicly available driving datasets and virtual testing environments[J]. IEEE Transactions on Intelligent Vehicles, 4, 171-185(2019).