Shoujiang Xu, Edmond S.L. Ho, and Hubert P.H. Shum
[1] G. Li, S. Tong, F. Cong, A. Yamashita, et al., Improved artificialpotential field-based simultaneous forward search method forrobot path planning in complex environment, Proc. 2015IEEE/SICE International Symposium on System Integration(SII), Nagoya, Japan, 2015, 760–765. [2] Z. Wu and L. Feng, Obstacle prediction-based dynamic pathplanning for a mobile robot. International Journal of Advancements in Computing Technology, 4(3), 2012, 118–124. [3] J.S. Oh, Y.H. Choi, J.B. Park, and Y.F. Zheng, Completecoverage navigation of cleaning robots using triangular-cell-based map, IEEE Transactions on Industrial Electronics, 51(3),2004, 718–726. [4] O. Takahashi and R.J. Schilling, Motion planning in a planeusing generalized Voronoi diagrams, IEEE Transactions onRobotics and Automation, 5(2), 1989, 143–150. [5] D.J. Bennet and C.R. McInnes, Distributed control of multi-robot systems using bifurcating potential fields, Robotics andAutonomous Systems, 58(3), 2010, 256–264. [6] C. Cai and S. Ferrai, Information-driven sensor path planningby approximate cell decomposition, IEEE Transactions onSystems, Man, and Cybernetics, Part B: Cybernetics, 39(3),2009, 672–689. [7] A. Mohammadi, M. Rahimi, and A.A. Suratgar, A new pathplanning and obstacle avoidance algorithm in dynamic environment, Proc. of The 22nd Iranian Conf. on ElectricalEngineering, Tehran, Iran, 2014, 1301–1306. [8] H. Miao and Y. Tian, Dynamic robot path planning using anenhanced simulated annealing approach, Applied Mathematicsand Computation, 222, 2013, 420–437. [9] H. Lee, T. Yaniss, and B. Lee, Grafting: a path replanning technique for rapidly-exploring random trees in dynamicenvironments, Advanced Robotics, 26(18) 2012, 2145–2168. [10] N.A. Shitagh and L.D. Jalal, Path planning of intelligent mobilerobot using modified genetic algorithm, International Journalof Soft Computing and Engineering(IJSCE), 3(2), 2013, 31–36. [11] A.M. Rao, K. Ramji, B.S.K. Sundadra Siva Rao, V. Vasa, et al.,Navigation of non-holonomic mobile robot using neuro-fuzzyLogic with integrated safe boundary, International Journal ofAutomation and Computing, 14(3), 2017, 285–294. [12] L. Deng, X. Ma, J. Gu, Y. Li, Multi-robot dynamic formationpath planning with improved polyclonal artificial immunealgorithm, Control and Intelligent Systems, 42(3), 2014, 1–4. [13] M. T. Khan, M.U. Qadir, A. Abid, F. Nasir, et al., Robotfault detection using an artificial immune system, Control andIntelligent Systems, 43(2), 2015, 129–132. [14] M. Dorigo, V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agent, IEEE Transactionson Systems, Man, and Cybernetics-Part B: Cybernetics, 26(1),1996, 29–41. [15] Y. He, Q. Zeng, J. Liu, G. Xu, et al., Path planning for indoorUAV Based on ant colony Optimization, Proc. 25th ChineseControl and Decision Conf. (CCDC), Guiyang, China, 2013,2919–2923. [16] Y. Miao, A. M. Khamis, F. Karray, and M.S. Kamel, A novelapproach to path planning for autonomous mobile robots,Control and Intelligent Systems, 39(4), 2011, 235–244. [17] I. Chaari, A. Koubaa, S. Trigui, H. Bennaceur, et al., Smart-PATH: An efficient hybrid ACO-GA algorithm for solving theglobal path planning problem of mobile robots, InternationalJournal of Advanced Robotics System, 11(7), 2014, 1–15. [18] Y. Zhang, Z. Liu, and L. Chang, A new adaptive artificialpotential field and rolling window method for mobile robotpath planning, Proc. 29th Chinese Control and Decision Conf.(CCDC), Chongqing, China, 2017, 7714–7718. [19] F. Zhou, Rolling path plan of mobile robot based on automaticdiffluent ant algorithm, International Journal of Robotics andAutomation, 3(2), 2014, 112–117. [20] Q. Zhu, J. Hu, W. Cai, et al., A new robot navigation algorithmfor dynamic unknown environments based on dynamic pathre-computation and an improved scout ant algorithm, AppliedSoft Computing, 11(8), 2011, 4667–4676.
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