Path planning approach in unknown environment

This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning: advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A* path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.

[1]  Takashi Kimura,et al.  Fuzzy Path Planning System for Autonomous Vehicle , 1993 .

[2]  Yoram Koren,et al.  Obstacle avoidance with ultrasonic sensors , 1988, IEEE J. Robotics Autom..

[3]  Yasushi Yagi,et al.  Map-based navigation for a mobile robot with omnidirectional image sensor COPIS , 1995, IEEE Trans. Robotics Autom..

[4]  Mark H. Overmars,et al.  Adding variation to path planning , 2008 .

[5]  N. Bourbaki Topological Vector Spaces , 1987 .

[6]  Norman E. Gough,et al.  A Genetic Algorithm for Path Planning , 1997 .

[7]  David G. Ward,et al.  A Hybrid A*/Automaton Approach to On-Line Path Planning with Obstacle Avoidance , 2004 .

[8]  Rae A. Earnshaw,et al.  Real-time path planning for navigation in unknown environment , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..

[9]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[10]  Qingjin Peng,et al.  Effective virtual reality based building navigation using dynamic loading and path optimization , 2009, Int. J. Autom. Comput..

[11]  N. Bourbaki,et al.  Topological Vector Spaces: Chapters 1–5 , 1987 .

[12]  L. Råde,et al.  Mathematics handbook for science and engineering , 1995 .

[13]  Yoram Koren,et al.  Real-time obstacle avoidance for fact mobile robots , 1989, IEEE Trans. Syst. Man Cybern..

[14]  N. Bourbaki Topological Vector Spaces , 1987 .

[15]  Mark H. Overmars,et al.  Indicative routes for path planning and crowd simulation , 2009, FDG.

[16]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[17]  Masahiro Fujita,et al.  A Floor and Obstacle Height Map for 3D Navigation of a Humanoid Robot , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[18]  Ying Zhang,et al.  A rough set GA-based hybrid method for robot path planning , 2006, Int. J. Autom. Comput..