DOI QR코드

DOI QR Code

A Global Path Planning of Mobile Robot Using Modified SOFM

수정된 SOFM을 이용한 이동로봇의 전역 경로계획

  • Published : 2006.05.01

Abstract

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

Keywords

References

  1. T. Lozano-Perez and M. A. Wesley, 'An algorithm for planning collision-free paths among polyhedrial obstacles,' Commun. ACM, pp. 560-570, 1979 https://doi.org/10.1145/359156.359164
  2. H. Noborio, T. Naniwa, and S. Arimoto, 'A fast path planning algorithm by synchronizing modification and search of its path-graph,' Proc. IEEE Intern. Workshop on Artificial intelligent for Industrial Application, pp. 351-357, 1988 https://doi.org/10.1109/AIIA.1988.13317
  3. R. Brooks, 'Solving the find path problems by good representation of free space,' IEEE Trans. Syst. Man Cybern., vol. SMC-13, no. 3, pp. 190-197, 1983 https://doi.org/10.1109/TSMC.1983.6313112
  4. M. D. Adams and P. J. Probert, 'Towards a real-time navigation strategy for a mobile robot,' Proc. of the IEEE Intern Workshop on Intelligent Robots and Systems, pp. 743-748, 1990 https://doi.org/10.1109/IROS.1990.262491
  5. J. Borenstein and Y. Koren, 'The vector field histogram-fast obstacle avoidance for mobile robots,' IEEE Trans. on Robotics and Automation, no. 3, pp. 278-298, 1991 https://doi.org/10.1109/70.88137
  6. D. Qunjie, Z. Mingjun, 'Local path planning method for AUV based on fuzzy-neural network,' SHIP ENGINEERING, vol. 1, pp. 54-58, 2001
  7. Y. Y. Cha, 'Navigation of a free ranging mobile robot using heuristic local path planning algorithm,' Robotics and Computer Integrated Manufacturing, vol. 13, no. 2, pp. 145-156, 1997 https://doi.org/10.1016/S0736-5845(96)00037-3
  8. Y. Zhu, J. Chang, and S. Wang, 'A new path-planning algorithm for mobile robot based on neural network,' TENCOM '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, vol. 3, pp. 1570-1573, 2002 https://doi.org/10.1109/TENCON.2002.1182630
  9. N. G. Bourbakis, D. Goldman, R. Fematt, I. Vlachavas, and L. H. Tsoukalas, 'Path planning in a 2-D known space using neural networks and skeletonization,' Conference Proceedings : IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2001-2005, 1997 https://doi.org/10.1109/ICSMC.1997.635147
  10. N. Chaiyaratana and A. M. S. Zalzala, 'Time-optimal path planning and control using neural networks and a genetic algorithm,' International Journal of Computational Intelligence and Applications, vol. 2, no. 2, pp. 153-172, 2002 https://doi.org/10.1142/S1469026802000531
  11. Y. Y. Cha and D. G. Gweon, 'The development of a free ranging mobile robot equipped with a structured light range sensor,' Intelligent Automation and Soft Computing, vol. 4, no. 4, pp. 289-312, 1998 https://doi.org/10.1080/10798587.1998.10750739
  12. 차영엽, 강현규, 'Self-organizing feature map를 이용한 이동로봇의 전역 경로계획,' 제어.자동화.시스템 공학회지, 제11권, 제2호, pp. 137-143, 2005 https://doi.org/10.5302/J.ICROS.2005.11.2.137
  13. T. Kohonen, 'The self-organizing map,' Proc. of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990 https://doi.org/10.1109/5.58325