Page 26 - Read Online
P. 26

Page 219                        Zhu et al. Intell Robot 2022;2(3):200­222  I http://dx.doi.org/10.20517/ir.2022.13


                   underwater target. IEEE Trans Veh Technol 2020;69:6782­87. DOI
               40.  Singh Y, Sharma S, Sutton R, Hatton D, Khan A. A constrained A* approach towards optimal path planning for an unmanned surface
                   vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Engineering 2018;169:187­201. DOI
               41.  Khatib O. Real­time obstacle avoidance for manipulators and mobile robots. In: Proceedings. 1985 IEEE International Conference on
                   Robotics and Automation. vol. 2; 1985. pp. 500­5. DOI
               42.  Zhu D, Yang SX. Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential
                   field. J Navig 2021;74:955­67. DOI
               43.  Ralli E, Hirzinger G. Fast path planning for robot manipulators using numerical potential fields in the configuration space. In: IROS ’94.
                   Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems. Advanced Robotic Systems and the Real
                   World (Cat. No.94CH3447­0). vol. vol.3. New York, NY, USA; 1994. pp. 1922­9. DOI
               44.  Zhou Z, Wang J, Zhu Z, Yang D, Wu J. Tangent navigated robot path planning strategy using particle swarm optimized artificial potential
                   field. Optik 2018;158:639­51. DOI
               45.  Lin Z, Yue M, Wu X, Tian H. An improved artificial potential field method for path planning of mobile robot with subgoal adaptive
                   selection. In: Intelligent Robotics and Applications. 12th International Conference, ICIRA 2019. Proceedings: Lecture Notes in Artificial
                   Intelligence (LNAI 11740). vol. pt.I. Cham, Switzerland; 2019. pp. 211­20. DOI
               46.  Xin L, Zhan­Qing W, Xu­Yang C. Path planning with improved artificial potential field method based on decision tree. In: 2020 27th
                   Saint Petersburg International Conference on Integrated Navigation Systems (ICINS). Piscataway, NJ, USA; 2020. p. 5 pp. DOI
               47.  Abdur Rahman M, Abul Kalam Azad M. To escape local minimum problem for multi­agent path planning using improved artificial
                   potential field­based regression search method. In: ACM International Conference Proceeding Series. Singapore, Singapore; 2017. pp.
                   371­6. DOI
               48.  Alvarez A, Caiti A, Onken R. Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Oceanic
                   Eng 2004;29:418–29. DOI
               49.  Cheng CT, Fallahi K, Leung H, Tse CK. A genetic algorithm­inspired UUV path planner based on dynamic programming. IEEE Trans
                   Syst Man Cybern, C, Appl Rev 2012;42:1128­34. DOI
               50.  Ma YN, Gong YJ, Xiao CF, Gao Y, Zhang J. Path planning for autonomous underwater vehicles: an ant colony algorithm incorporating
                   alarm pheromone. IEEE Trans Veh Technol 2019;68:141–54. DOI
               51.  Han G, Zhou Z, Zhang T, et al. Ant­colony­based complete­coverage path­planning algorithm for underwater gliders in ocean areas with
                   thermoclines. IEEE Trans Veh Technol 2020;69:8959­71. DOI
               52.  Mo H, Xu L. Research of biogeography particle swarm optimization for robot path planning. Neurocomputing 2015;148:91­9. DOI
               53.  Lee CC. Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Trans Syst Man Cybern 1990;20:404­18. DOI
               54.  Lee CC. Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Trans Syst Man Cybern 1990;20:419­35. DOI
               55.  Kim YG, Bui LD. An obstacle­avoidance technique for autonomous underwater vehicles based on BK­products of fuzzy relation. Fuzzy
                   Sets Syst 2006;157:560­77. DOI
               56.  Ali F, Kim EK, Kim YG. Type­2 fuzzy ontology­based semantic knowledge for collision avoidance of autonomous underwater vehicles.
                   Inf Sci 2015;295:441­64. DOI
               57.  LeBlanc K, Saffiotti A. Multirobot object localization: a fuzzy fusion approach. IEEE Trans Syst Man Cybern B, Cybern 2009;39:1259­
                   76. DOI
               58.  Ling S. A real­time collision­free path planning of a rust removal robot using an improved neural network. J Shanghai Jiaotong Univ,
                   Sci 2017;22:633­40. DOI
               59.  Ghatee M, Mohades A. Motion planning in order to optimize the length and clearance applying a Hopfield neural network. Expert Syst
                   Appl 2009;36:4688­95. DOI
               60.  Li H, Yang SX, Biletskiy Y. Neural network based path planning for a multi­robot system with moving obstacles. In: 2008 IEEE
                   International Conference on Automation Science and Engineering (CASE 2008). Piscataway, NJ, USA; 2008. pp. 163­8. DOI
               61.  Zhu D, Yang SX. Bio­inspired neural network­based optimal path planning for UUVs under the effect of ocean currents. IEEE Trans
                   Veh Technol 2021. DOI
               62.  Noguchi Y, Maki T. Path planning method based on artificial potential field and reinforcement learning for intervention AUVs. In: 2019
                   IEEE Underwater Technology (UT). Piscataway, NJ, USA; 2019. pp. 1­6. DOI
               63.  Li Z, Luo X. Autonomous underwater vehicles (AUVs) path planning based on Deep Reinforcement Learning. In: 2021 11th Interna­
                   tional Conference on Intelligent Control and Information Processing (ICICIP). Piscataway, NJ, USA; 2021. pp. 125­9. DOI
               64.  Wang Z, Zhang S, Feng X, Sui Y. Autonomous underwater vehicle path planning based on actor­multi­critic reinforcement learning.
                   Proc Inst Mech Eng, I, J Syst Control Eng 2021;235:1787­96. DOI
               65.  Batalin MA, Sukhatme GS. Spreading out: a local approach to multi­robot coverage. In: Distributed Autonomous Robotic Systems 5.
                   Tokyo; 2002. pp. 373­82.
               66.  Parlaktuna O, Sipahioglu A, Kirlik G, Yazici A. Multi­robot sensor­based coverage path planning using capacitated arc routing approach.
                   In: 2009 IEEE International Conference on Control Applications (CCA). Piscataway, NJ, USA; 2009. pp. 1146­51. DOI
               67.  Janchiv A, Batsaikhan D, Kim Gh, Lee SG. Complete coverage path planning for multi­robots based on. In: 2011 11th International
                   Conference on Control, Automation and Systems; 2011. pp. 824–27.
               68.  Rekleitis I, New A, Rankin E, Choset H. Efficient boustrophedon multi­robot coverage: an algorithmic approach. Ann Math Artif Intell
                   2008;52:109­42. DOI
               69.  Hazon N, Kaminka GA. On redundancy, efficiency, and robustness in coverage for multiple robots. Robotics and Autonomous Systems
   21   22   23   24   25   26   27   28   29   30   31