BIOINSPIRED NEURAL NETWORK-BASED Q-LEARNING APPROACH FOR ROBOT PATH PLANNING IN UNKNOWN ENVIRONMENTS

Jianjun Ni, Xinyun Li, Mingang Hua and Simon X. Yang

References

  1. [1] M.T. Mason, Creation myths: The beginnings of robotics research, IEEE Robotics and Automation Magazine, 19(2), 2012, 72–77.
  2. [2] S. Piao, W. Zhao, Q. Zhong, and Y. Liu, Research of vision-based global localization for mobile robot, Journal of Computational Information Systems, 7(1), 2011, 144–151.
  3. [3] E. Galceran and M. Carreras, A survey on coverage path planning for robotics, Robotics and Autonomous Systems, 61(12), 2013, 1258–1276.
  4. [4] T.-K. Lee, S.-H. Baek, Y.-H. Choi, and S.-Y. Oh, Smooth coverage path planning and control of mobile robots based on high-resolution grid map representation, Robotics and Autonomous Systems, 59(10), 2011, 801–812.
  5. [5] T.-K. Wang, Q. Dang, and P.-Y. Pan, Path planning approach in unknown environment, International Journal of Automation and Computing, 7(3), 2010, 310–316.
  6. [6] J. Ni, W. Wu, J. Shen, and X. Fan, An improved VFF approach for robot path planning in unknown and dynamic environments, Mathematical Problems in Engineering, Volume 2014, Article ID 461237, 2014, p. 10.
  7. [7] M.-C. Tsou and C.-K. Hsueh, The study of ship collision avoidance route planning by ant colony algorithm, Journal of Marine Science and Technology, 18(5), 2010, 746–756.
  8. [8] F. Zhou, G. Wang, G. Tian, Y. Li, and X. Yuan, A fast navigation method for service robots in the family environment, Journal of Information and Computational Science, 9(12), 2012, 3501–3508.
  9. [9] F. Duchon, D. Hunady, M. Dekan, and A. Babinec, Optimal navigation for mobile robot in known environment, Applied Mechanics and Materials, 282, 2013, 33–38. DOI: 10.4028/www.scientific.net/AMM.282.33.
  10. [10] T.H. Kim, K. Goto, H. Igarashi, K. Kon, N. Sato, and F. Matsuno, Path planning for an autonomous mobile robot considering a region with a velocity constraint in a real environment, Artificial Life and Robotics, 16(4), 2012, 514–518.
  11. [11] D. Zhu, H. Huang, and S. Yang, 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, Cybernetics, IEEE Transactions on, 43(2), 2013, 504–514.
  12. [12] J. Ni and S. X. Yang, A fuzzy-logic based chaos GA for cooperative foraging of multi-robots in unknown environments, International Journal of Robotics and Automation, 27(1), 2012, 15–30.
  13. [13] Y. Gao, S.-D. Sun, D.-W. Hu, and L.-J. Wang, An online path planning approach of mobile robot based on particle filter, Industrial Robot, 40(4), 2013, 305–319.
  14. [14] M.A.K. Jaradat, M.H. Garibeh, and E.A. Feilat, Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field, Soft Computing, 16(1), 2012, 153–164.
  15. [15] Y. Huang, Intelligent technique for robot path planning using artificial neural network and adaptive ant colony optimization, Journal of Convergence Information Technology, 7(9), 2012, 246–252.
  16. [16] B. Lei and W. Li, A fuzzy behaviours fusion algorithm for mobile robot real-time path planning in unknown environment, in 2007 IEEE International Conference on Integration Technology (Shenzhen, China, 2007) 173–178.
  17. [17] J. Ni, X. Li, J. Shen, and X. Fan, A dynamic risk level based bioinspired neural network approach for robot path planning, International Journal of Complex Systems – Computing, Sensing and Control, 1(1–2), 2013, 55–64.
  18. [18] M. Yan, D. Zhu, and S.X. Yang, A novel 3-D bio-inspired neural network model for the path planning of an AUV in underwater environments, Intelligent Automation & Soft Computing, 19(4), 2013, 555–566.
  19. [19] S. Saravanakumar and T. Asokan, Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space, Intelligent Service Robotics, 6(4), 2013, 211–224.
  20. [20] G. Antonelli, S. Chiaverini, and G. Fusco, A fuzzy-logic-based approach for mobile robot path tracking, IEEE Transactions on Fuzzy Systems, 15(2), 2007, 211–221.
  21. [21] J.A. Villacorta-Atienza, M.G. Velarde, and V.A. Makarov, Compact internal representation of dynamic situations: Neural network implementing the causality principle, Biological Cybernetics, 103(4), 2010, 285–297.
  22. [22] T. Kitamura and D. Nishino, Training of a leaning agent for navigation – Inspired by brain-machine interface, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(2), 2006, 353–365.
  23. [23] H.-H. Viet, S.-Y. Choi, and T.-C. Chung, Dyna-QUF: Dyna-Q based univector field navigation for autonomous mobile robots in unknown environments, Journal of Central South University, 20(5), pp. 1178 – 1188, 2013.
  24. [24] B. Liu and Z. Lu, AUV path planning under ocean current based on reinforcement learning in electronic chart, Proc. – 2013 International Conference on Computational and Information Sciences, ICCIS 2013, Shiyan, Hubei, China, 2013, 1939–1942.
  25. [25] X. Ma, Y. Xu, G.-Q. Sun, L.-X. Deng, and Y.-B. Li, State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots, Journal of Zhejiang University: Science C, 14(3), 2013, 167–178.
  26. [26] X. Xu, D. Hu, and X. Lu, Kernel-based least squares policy iteration for reinforcement learning, IEEE Transactions on Neural Networks, 18(4), 2007, 973–992.
  27. [27] L. Khriji, F. Touati, K. Benhmed, and A. Al-Yahmedi, Mobile robot navigation based on Q-learning technique, International Journal of Advanced Robotic Systems, 8(1), 2011, 45–51.
  28. [28] J. Ni, M. Liu, L. Ren, and S. Yang, A multiagent Q-learning-based optimal allocation approach for urban water resource management system, IEEE Transactions on Automation Science and Engineering, 11(1), 2014, 204–214.
  29. [29] A. Bonarini, A. Lazaric, F. Montrone, and M. Restelli, “Reinforcement distribution in fuzzy Q-learning, Fuzzy Sets and Systems, 160(10), 2009, 1420–1443.
  30. [30] A. Konar, I.G. Chakraborty, S.J. Singh, L.C. Jain, and A.K. Nagar, A deterministic improved Q-learning for path planning of a mobile robot, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(5), 2013, 1141–1153.
  31. [31] M. Nagayoshi, H. Murao, and H. Tamaki, Adaptive coconstruction of state and action spaces in reinforcement learning, Artificial Life and Robotics, 16(1), 2011, 48–52.
  32. [32] K.-S. Hwang, Y.-J. Chen, W.-C. Jiang, and T.-F. Lin, Continuous action generation of Q-learning in multi-agent cooperation, Asian Journal of Control, 15(4), 2013, 1011–1020.
  33. [33] B. Kiumarsi, F.L. Lewis, H. Modares, A. Karimpour, and M.-B. Naghibi-Sistani, Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics, Automatica, 50(4), 2014, 1167–1175.
  34. [34] X. Xu, C. Liu, S. X. Yang, and D. Hu, Hierarchical approximate policy iteration with binary-tree state space decomposition, IEEE Transactions on Neural Networks, 22(12, Part 1), 2011, 1863–1877.
  35. [35] J. Ni and S.X. Yang, Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments, IEEE Transactions on Neural Networks, 22(12), 2011, 2062–2077.
  36. [36] D. Zhu, Y. Zhao, and M. Yan, A bio-inspired neurodynamics based back stepping path-following control of an AUV with ocean current, International Journal of Robotics and Automation, 27(3), 2012, 298–307.
  37. [37] H. Qu, S.X. Yang, A.R. Willms, and Z. Yi, Real-time robot path planning based on a modified pulse-coupled neural network model, IEEE Transactions on Neural Networks, 20(11), 2009, 1724–1739.
  38. [38] S.X. Yang and M. Meng, Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach, IEEE Transactions on Neural Networks, 14(6), 2003, 1541–1552.
  39. [39] H. Miao and Y.-C. Tian, Dynamic robot path planning using an enhanced simulated annealing approach, Applied Mathematics and Computation, 222, 2013, 420–437.
  40. [40] E. Masehian and M.R. Amin-Naseri, Sensor-based robot motion planning – A tabu search approach, IEEE Robotics and Automation Magazine, 15(2), 2008, 48–57.

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