Growing topological map for SLAM of mobile robots

This paper proposes a simultaneous localization and mapping method in unknown environments of multiple mobile robots. According to the measured distance by laser range finder, a shared map is updated sequentially cooperatively. When the difference between the measured distance and its corresponding map data is large, the robot updates the self-location by using the steady-state genetic algorithm, and updates the map by using topological approach. We propose a map building method using a growing topological map. Finally we discuss the effectiveness of the proposed methods through several experimental results and comparison results.

[1]  Nobuyuki Kita,et al.  3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Wolfram Burgard,et al.  Map building with mobile robots in populated environments , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  R. Brooks.  Planning Collision- Free Motions for Pick-and-Place Operations , 1983 .

[4]  Frank Wolter,et al.  Exploring Artificial Intelligence in the New Millenium , 2002 .

[5]  S SukhatmeGaurav,et al.  Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments , 2005 .

[6]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[7]  Kikuo Fujimura,et al.  Map making by cooperating mobile robots , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[8]  I. Khemapech,et al.  A Survey of Wireless Sensor Networks Technology , 2005 .

[9]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[10]  Wolfram Burgard,et al.  Mapping and localization with RFID technology , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[11]  FritzkeBernd.  Growing cell structuresa self-organizing network for unsupervised and supervised learning , 1994 .

[12]  Nak Young Chong,et al.  A distributed knowledge network for real world robot applications , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[13]  Gaurav S. Sukhatme,et al.  Multirobot Simultaneous Localization and Mapping Using Manifold Representations , 2006, Proceedings of the IEEE.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Charles E. Thorpe,et al.  Simultaneous localization and mapping with detection and tracking of moving objects , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[16]  Masahiro Tomono,et al.  Mobile robot localization based on an inaccurate map , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[17]  Thomas Oliver Smailus,et al.  Mapping by Cooperative Mobile Robots. , 2001 .

[18]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .