Fuzzy Motion Planning of Mobile Robots in Unknown Environments

A fuzzy algorithm is proposed to navigate a mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm will determine the priorities of thirteen possible heading directions. Then the robot is driven to an intermediate configuration along the heading direction that has the highest priority. The navigation procedure will be iterated until a collision-free path between the initial and the final configurations is found. To show the feasibility of the proposed method, in addition to computer simulation, experimental results will be also given.

[1]  Sauro Longhi,et al.  Motion planning for unicycle and car-like robots , 1996, Int. J. Syst. Sci..

[2]  Elmer G. Gilbert,et al.  Distance functions and their application to robot path planning in the presence of obstacles , 1985, IEEE J. Robotics Autom..

[3]  D. Lane,et al.  Subsea vehicle path planning using nonlinear programming and constructive solid geometry , 1997 .

[4]  Lydia E. Kavraki,et al.  Analysis of probabilistic roadmaps for path planning , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[5]  Ching-Chih Tsai.  A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[6]  Chia-Ju Wu,et al.  A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules , 2000, J. Intell. Robotic Syst..

[7]  Daniel E. Koditschek,et al.  Exact robot navigation using artificial potential functions , 1992, IEEE Trans. Robotics Autom..

[8]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[9]  Liqiang Feng,et al.  Measurement and correction of systematic odometry errors in mobile robots , 1996, IEEE Trans. Robotics Autom..

[10]  Chia-Ju Wu,et al.  Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information , 2001, J. Intell. Robotic Syst..

[11]  Shuzhi Sam Ge,et al.  New potential functions for mobile robot path planning , 2000, IEEE Trans. Robotics Autom..

[12]  Hong Xu,et al.  LiAS: a reflexive navigation architecture for an intelligent mobile robot system , 1996, IEEE Trans. Ind. Electron..

[13]  Vijay Kumar,et al.  A potential field based approach to multi-robot manipulation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[14]  Fernando Figueroa,et al.  A Robust Navigation System for Autonomous Vehicles using Ultrasonics , 1993 .

[15]  Kimon P. Valavanis,et al.  Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic , 2001, IEEE Trans. Robotics Autom..

[16]  Sadayuki Tsugawa,et al.  Vision-based vehicles in Japan: machine vision systems and driving control systems , 1994, IEEE Trans. Ind. Electron..

[17]  Narendra Ahuja,et al.  Path planning using a potential field representation , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[18]  Eric T. Baumgartner,et al.  An autonomous vision-based mobile robot , 1994, IEEE Trans. Autom. Control..

[19]  Wei Li,et al.  A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments , 1997, Fuzzy Sets Syst..

[20]  Chia-Ju Wu.  A learning fuzzy algorithm for motion planning of mobile robots , 1994, J. Intell. Robotic Syst..