Yifan Cai and Simon X. Yang
[1] D.C. Theodoridis, Y.S. Boutalis, and M.A. Christodoulou, A new adaptive neuro-fuzzy controller for trajectory tracking of robot manipulators, International Journal of Robotics and Automation, 26(1), 2011, 64–75. [2] K. Derr and M. Manic, Multi-robot, multi-target particle swarm optimization search in noisy wireless environments, in Proc. 2009 the 2nd Conf. on Human System Interactions, Catania, Italy, May 2009, 81–86. [3] L. Yang, Z. Cao, C. Zhou, L. Cheng, and M. Tan, Formation control and switching for multiple robots in uncertain environments, International Journal of Robotics and Automation, 25(3), 2010, 240–249. [4] E.U. Acar, H. Choset, Y. Zhang, and M. Schervish, Path planning for robotic demining: robust sensor-based coverage of unstructured environments and probabilistic methods, International Journal of Robotics Research, 22(7–8), 2003, 441–466. [5] F. Wang and B. Lu, The robot path planning based on PSO algorithm integrated with PID, in Proceedings of the 2009 International Conference on Information Engineering and Computer Science, Wuhan, China, December 2009, 1–4. [6] E.U. Acar, H. Choset, A.A. Rizzi, P.N. Atkar, and D. Hull, Morse decompositions for coverage tasks, International Journal of Robotics Research, 21(4), 2002, 331–344. [7] C. Luo and S.X. Yang, A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments, IEEE Transactions on Neural Networks, 19(7), 2008, 1279–1298. [8] S. Sachs, S.M.L. Valle, and S. Rajko, Visibility-based pursuit-evasion in an unknown planar environment, International Journal of Robotics Research, 23(1), 2004, 3–26. [9] W. Burgard, M. Moors, C. Stachniss, and F. Schneider, Coordinated multi-robot exploration, IEEE Transactions on Robotics, 21(3), 2005, 376–386. [10] K. Ijaz and U. Manzoor, Using vision and coordination to find unknown target in fixed and random length obstacles, WSEAS Transactions on Computers, 5(10), 2006, 2400–2405. [11] T.J. Li, G.W. Yuan, and F.J. Wang, Behavior control of multiple robots exploring unknown environment, in Proc. 2009 the 4th IEEE Conf. on Industrial Electronics and Applications, Xi’an, China, May 2009, 1877–1882. [12] Y. Yin, L. Sun, and C. Guo, A policy of conflict negotiation based on fuzzy matter element particle swarm optimization in distributed collaborative creative design, Computer Aided Design, 40(10–11), 2008, 1009–1014. [13] J. Kennedy and R.C. Eberhart, A discrete binary version of the particle swarm algorithm, in Proc. the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando, USA, October 1997, 4101–4108. [14] S. Tan, S.X. Yang, and A. Zhu, A novel GA-based fuzzy controller for mobile robots in dynamic environments with moving obstacles, International Journal of Robotics and Automation, 26(2), 2011, 212–228. [15] F. Li, An improved particle swarm optimization algorithm with synthetic update mechanism, in Proc. Third International Symp. on Intelligent Information Technology and Security Informatics, Jinggangshan, China, April 2010, 695–699. [16] S. Kiranyaz, T. Ince, A. Yildirim, and M. Gabbouj, Evolutionary artificial neural networks by multi-dimensional particle swarm optimization, Neural Networks, 22(10), 2009, 1448–1462. [17] S. Kiranyaz, J. Pulkkinen, and M. Gabbouj, Multi-dimensional particle swarm optimization for dynamic environments, in Proc. 2008 Int. Conf. on Innovations in Information Technology, Al Ain, United Arab Emirates, December 2008, 34–38. [18] H. An, Y. Qi, and Z. Cheng, A novel image fusion method based on particle swarm optimization, in Proc. 2009 Int. Conf. on Wireless Networks and Information Systems, Shanghai, China, December 2009, 527–535. [19] L.C. Lai, C.J. Wu, J.T. Jeng, C.N. Ko, and Y.Y. Fu, PSO-based potential field method for a mobile robot motion planning in an unknown environment, in Proc. 14th Int. Symp. on Artificial Life and Robotics, Oita, Japan, February 2009, 255–258. [20] K.E. Parsopoulos and M.N. Vrahatis, Particle swarm optimization and intelligence: advances and applications (Hershey, USA: Information Science Reference, January 2010). [21] Y. Xue, G. Tian, and G. Li, Global path planning for mobile robot based on improved particle swarm optimization, Journal of Huazhong University of Science and Technology (Natural Science Edition), 36(suppl.)1, 2008, 167–170. [22] S. Kiranyaz, T. Ince, A. Yildirim, and M. Gabbouj, Fractional particle swarm optimization in multidimensional search space, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40(2), 2010, 298–319. [23] E. Masehian and D. Sedighizadeh, A multi-objective PSO-based algorithm for robot path planning, in Proc. 2010 IEEE Int. Conf. on Industrial Technology, Vi a del Mar, Chile, March 2010, 465–470. [24] Y. Tang, Q. Li, L. Wang, C. Zhang, and Y. Yin, An improved PSO for path planning of mobile robots and its parameters discussion, in Proc. 2010 Int. Conf. on Intelligent Control and Information Processing, Dalian, China, August 2010, 34–38. [25] H. Chen and L. Xie, A novel artificial potential field-based reinforcement learning for mobile robotics in ambient intelligence, International Journal of Robotics and Automation, 24(3), 2009, 245–254. [26] J.R. Jang, C. Sun, and E. Mizutani, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence (New York, USA: Prentice Hall, October 1996). [27] B. Xiao, L. Yu, S. Li, and R. Chen, Research of escaping local minima strategy for artificial potential field, Journal of System Simulation, 19(19), 2007, 4495–4503.
Important Links:
Go Back