LavalleS.M., Planning Algorithms, Cambridge University Press, 2006.
2.
DengM.InoueA.SekiguchiK., Parking control of a two wheeled mobile robot, Proc. of IEEE Int. Conf. Mechatronics and Automation, pp. 539–544, 2007.
3.
DengM.InoueA.SekiguchiK., Lyapunov function base obstacle avoidance scheme for a two wheeled mobile robot, J. of Control Theory and Applications, Vol. 6, No. 4, pp. 399–404, 2008.
4.
JiangL.DengM.InoueA., Obstacle avoidance and motion control of a two wheeled mobile robot using SVR technique, Int. J. of Innovative Computing, Information and Control, Vol. 5, No. 2, pp. 253–262, 2009.
5.
RimonE.KoditschekD.E., Exact robot navigation using artificial potential functions, IEEE Trans. Robotics and Automation, Vol. 8, No. 5, pp. 501–518, 1992.
6.
OkumaK.UrakudoT.TadaY., Lyapunov control of a two wheeled mobile robot in the presence of obstacles, Proc. of Japan-USA Symp. Flexible Automation, CD-ROM (JS-026), 2004.
7.
LoizouS.TannerH.G.KumarV.KyriakopoulosK.J., Closed loop navigation for mobile agents in dynamic environments, Proc. of IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 2003.
8.
GeS.S.FuaC.H.LimK.W., Multi-robot formations: queues and artificial potential trenches, Proc. of IEEE Int. Conf. Robotics and Automation, pp. 3345–3350, 2004.
9.
GeS.S.CuiY.J., Dynamic motion planning for mobile robots using potential field method, Autotomous Robots, Vol. 13, No. 3, pp. 207–222, 2002.
10.
KimJ OKhoslaP K. Real-Time Obstacle Avoidance Using Harmonic Potential Functions. IEEE Trans. Robotics and Automation, Vol. 8, No. 3, pp. 338–349, 1992.
11.
GeS.S.CuiY.J.. New potential functions for mobile robot path planning. IEEE Trans. Robotics and Automation, Vol. 16, No. 5, pp. 615–620, 2000.
12.
DengM.SaijoN.GomiH.InoreA., A robust real time method for estimating human multijoint arm viscoelasticity, Int. J. of Innovative Computing, Information and Control, Vol. 2, No. 4, pp. 705–721, 2006.
13.
CristianiniN.Shawe-TalorJ., An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, London, 2005.
14.
SchölkopfB.SmolaA.J., Learning with kernels-support vector machines, regularization, optimization, and beyond, The MIT Press, London, 2005.
15.
SmolaA.J.SchölkopfB., A tutorial on support vector regression, NeuroCOLT2 Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK, 1998.