Waseem Gharbieh and Amjed Al-Mousa


  1. [1] R. Abiyev, D. Ibrahim, and B. Erin, Navigation of mobilerobots in the presence of obstacles, Advances in EngineeringSoftware, 41(10), 2010, 1179–1186.
  2. [2] M. Duguleanaa and G. Mogan, Neural networks based rein-forcement learning for mobile robots obstacle avoidance, ExpertSystems with Applications, 62, 2016, 104–115.
  3. [3] O. Khatib, Real-time obstacle avoidance for manipulators andmobile robots, The International Journal of Robotics Research,5(1), 1986, 90–98.
  4. [4] J.D.R. Mill´an and C. Torras, A reinforcement connectionistapproach to robot path finding in non-maze-like environments,Machine Learning, 8(3–4), 1992, 363–395.
  5. [5] A. Savkin and C. Wang, Seeking a path through the crowd:Robot navigation in unknown dynamic environments withmoving obstacles based on an integrated environment rep-resentation, Robotics and Autonomous Systems, 62, 2014,1568–1580.
  6. [6] J.d.R. Mill´an, Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot,Robotics and Autonomous Systems, 15(4), 1995, 275–299.
  7. [7] M. Duguleana, F.G. Barbuceanu, A. Teirelbar, and G. Mo-gan, Obstacle avoidance of redundant manipulators usingneural networks based reinforcement learning, Robotics andComputer-Integrated Manufacturing, 28(2), 2012, 132–146.
  8. [8] M.A.K. Jaradat, M. Al-Rousan, and L. Quadan, Reinforce-ment based mobile robot navigation in dynamic environment,Robotics and Computer-Integrated Manufacturing, 27(1), 2011,135–149.
  9. [9] C. Xia and A. El Kamel, Neural inverse reinforcement learningin autonomous navigation, Robotics and Autonomous Systems,84, 2016, 1–14.
  10. [10] C. Goerzen, Z. Kong, and B. Mettler, A survey of motionplanning algorithms from the perspective of autonomous uavguidance, Journal of Intelligent and Robotic Systems, 57(1),2009, 65. doi:10.1007/s10846-009-9383-1.
  11. [11] National Library of Canada, Neural network approaches to real-time motion planning and control of robotic systems, (NationalLibrary of Canada = Biblioth`eque nationale du Canada, 1999),
  12. [12] R.S. Sutton and A.G. Barto, Reinforcement learning: Anintroduction, (Cambridge, MA, USA: MIT Press, 1998).
  13. [13] S.X. Yang and M. Meng, An efficient neural network approachto dynamic robot motion planning, Neural Networks, 13(2),2000, 143–148.
  14. [14] C. Xia, A. El Kamel, A reinforcement learning method ofobstacle avoidance for industrial mobile vehicles in unknownenvironments using neural network, Proceedings of the 21stInternational Conference on Industrial Engineering, 2014,671–675.
  15. [15] R. Glasius, A. Komoda, and S.C. Gielen, Neural networkdynamics for path planning and obstacle avoidance, NeuralNetworks, 8(1), 1995, 125–133.
  16. [16] K. Maˇcek, I. Petrovi´c, N. Peri´c, A reinforcement learningapproach to obstacle avoidance of mobile robots, AdvancedMotion Control, 2002. 7th International Workshop on, IEEE,2002, 462–466.
  17. [17] S.C. Yun, S. Parasuraman, and V. Ganapathy, Mobile robotnavigation: Neural q-learning, in N. Meghanathan, D. Naga-malai, N. Chaki (eds.), Advances in computing and informationtechnology, (Berlin, Heidelberg: Springer, 2013), 178, 259–268.
  18. [18] M.J. Er and C. Deng, Obstacle avoidance of a mobile robot usinghybrid learning approach, IEEE Transactions on IndustrialElectronics, 52(3), 2005, 898–905.
  19. [19] D. Zhu, X. Cao, B. Sun, and C. Luo, Biologically inspired self-organizing map applied to task assignment and path planningof an auv system, IEEE Transactions on Cognitive and Devel-opmental Systems, 10(2), 2018, 304–313. doi:10.1109/TCDS.2017.2727678.
  20. [20] J. Ni, X. Li, M. Hua, and S.X. Yang, Bioinspired neuralnetwork-based q-learning approach for robot path planning inunknown environments, International Journal of Robotics andAutomation, 2016, 31(6). doi:10.2316/Journal.206.2016.6.206-4526.
  21. [21] X.-M. You, S. Liu, and C. Zhang, An improved ant colonysystem algorithm for robot path planning and performanceanalysis, International Journal of Robotics and Automation,33(5), 2018. doi:10.2316/Journal.206.2018.5.206-0071.578
  22. [22] L. Wang, C. Luo, M. Li, and J. Cai, Trajectory planning ofan autonomous mobile robot by evolving ant colony system,International Journal of Robotics and Automation, 32(4), 2017.doi:10.2316/Journal.206.2017.4.206-4917.
  23. [23] J. Ni, K. Wang, Q. Cao, Z. Khan, and X. Fan, A memeticalgorithm with variable length chromosome for robotpath planning under dynamic environments, Interna-tional Journal of Robotics and Automation, 32(4), 2017.doi:10.2316/Journal.206.2017.4.206-4998.
  24. [24] L. Wang, C. Luo, A hybrid genetic tabu search algorithmfor mobile robot to solve as/rs path planning, Interna-tional Journal of Robotics and Automation, 33(2), 2018.doi:10.2316/Journal.206.2018.2.206-5102.
  25. [25] C. Luo and S.X. Yang, A bioinspired neural networkfor real-time concurrent map building and complete cov-erage robot navigation in unknown environments, IEEETransactions on Neural Networks, 19(7), 2008, 1279–1298.doi:10.1109/TNN.2008.2000394.
  26. [26] J. Ni and S.X. Yang, Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environ-ments, IEEE Transactions on Neural Networks, 22(12), 2011,2062–2077. doi:10.1109/TNN.2011.2169808.
  27. [27] S.X. Yang and C. Luo, A neural network approach to completecoverage path planning, IEEE Transactions on Systems, Man,and Cybernetics, Part B (Cybernetics), 34(1), 2004, 718–724.doi:10.1109/TSMCB.2003.811769.
  28. [28] A. Zhu and S.X. Yang, A neural network approach todynamic task assignment of multirobots, IEEE Trans-actions on Neural Networks, 17(5), 2006, 1278–1287.doi:10.1109/TNN.2006.875994.
  29. [29] A. Zhu and S.X. Yang, An improved som-based approachto dynamic task assignment of multi-robots, 2010 8th WorldCongress on Intelligent Control and Automation, 2010, 2168–2173. doi:10.1109/WCICA.2010.5554341.
  30. [30] D. Zhu, H. Huang, and S.X. Yang, Dynamic task assign-ment and path planning of multi-auv system based on animproved self-organizing map and velocity synthesis method inthree-dimensional underwater workspace, IEEE Transactionson Cybernetics, 43(2), 2013, 504–514. doi:10.1109/TSMCB.2012.2210212.
  31. [31] H. Li, S.X. Yang, and M.L. Seto, Neural-network-basedpath planning for a multirobot system with moving obsta-cles, IEEE Transactions on Systems, Man, and Cybernetics,Part C (Applications and Reviews), 39(4), 2009, 410–419.doi:10.1109/TSMCC.2009.2020789.
  32. [32] C. Luo, S.X. Yang, X. Li, and M.Q. Meng, Neural-dynamics-driven complete area coverage navigation through coopera-tion of multiple mobile robots, IEEE Transactions on In-dustrial Electronics, 64(1), 2017, 750–760. doi:10.1109/TIE.2016.2609838.

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