Advertisement

Neural Networks for Mobile Robot Navigation: A Survey

  • An-Min Zou
  • Zeng-Guang Hou
  • Si-Yao Fu
  • Min Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Nowadays, mobile robots have attracted more and more attention from researchers due to their extensive applications. Mobile robots need to have the capabilities of autonomy and intelligence, and they pose a challenge to researchers, which is to design algorithms that allow the robots to function autonomously in unstructured, dynamic, partially observable, and uncertain environments [1]. Navigation is the key to the relative technologies of mobile robots and neural networks are widely used in the field of mobile robot navigation due to their properties such as nonlinear mapping, ability to learn from examples, good generalization performance, massively parallel processing, and capability to approximate an arbitrary function given sufficient number of neurons. This paper surveys the developments in the last few years of the neural networks with applications to mobile robot navigation.

Keywords

Neural Network Mobile Robot Path Planning Feedforward Neural Network Obstacle Avoidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sukhatme, G.S., Mataric, M.J.: Robots: Intelligence, Versatility, Adaptivity. Communications of the ACM 45(3), 30–32 (2002)CrossRefGoogle Scholar
  2. 2.
    Kortenkamp, D., Bonasso, R.P., Murphy, R. (eds.): Artificial Intelligence and Mobile Robots: Case Studies of Successful Robot Systems. AAAI press, Menlo Park (1998)Google Scholar
  3. 3.
    Kim, H.H., Ha, Y.S., Jin, G.G.: A Study on the Environmental Map Building for a Mobile Robot Using Infrared Ranger-finder Sensors. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, vol. 1, pp. 711–716 (2003)Google Scholar
  4. 4.
    Thrun, S.B.: Exploration and Model Building in Mobile Robot Domains. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 1, pp. 175–180 (1993)Google Scholar
  5. 5.
    Meng, M., Kak, A.C.: Fast Vision-Guided Mobile Robot Navigation Using Neural Networks. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 111–116 (1992)Google Scholar
  6. 6.
    Zou, A., Hou, Z.G., Zhang, L., Tan, M.: A Neural Network-based Camera Calibration Method for Mobile Robot Localization Problems. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 277–284. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Barbera, H.M., Skarmeta, A.G., Izquierdo, M.Z., Blaya, J.B.: Neural Networks for Sonar and Infrared Sensors Fusion. In: Proceedings of the Third International Conference on Information Fusion, vol. 2, pp. 4–18 (2000)Google Scholar
  8. 8.
    Lippman, R.: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine 4, 4–22 (1987)CrossRefGoogle Scholar
  9. 9.
    Hu, H.S., Gu, D.B.: Landmark-Based Navigation of Mobile Robot in Manufacturing. In: Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, vol. 1, pp. 114–121 (1999)Google Scholar
  10. 10.
    Janet, J.A., Gutierrez-Osuna, R., Chase, T.A., White, M., Luo, R.C.: Global Self-localization for Autonomous Mobile Robots Using Self-organizing Kohonen Neural Networks. In: Proceedings of the IEEE International Conference on Intelligent Robotics and Systems, vol. 3, pp. 504–509 (1995)Google Scholar
  11. 11.
    Crowley, J.L., Wallner, F., Schiele, B.: Position Estimation Using Principal Components of Range Data. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3121–3128 (1998)Google Scholar
  12. 12.
    Vlassis, N., Motomura, Y., Krose, B.: Supervised Dimension Reduction of Intrinsically Low-dimensional Data. Neural Computation 14(1), 191–215 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Nayar, S.K., Murase, H., Nene, S.A.: Learning, Positioning, and Tracking Visual Appearance. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3237–3244 (1994)Google Scholar
  14. 14.
    Artac, M., Jogan, M., Leonardis, A.: Mobile Robot Localization Using an Incremental Eigenspace Model. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 1025–1030 (2002)Google Scholar
  15. 15.
    Tamimi, H., Zell, A.: Global Visual Localization of Mobile Robots Using Kernel Principal Component Analysis. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, vol. 2, pp. 1896–1901 (2004)Google Scholar
  16. 16.
    Vapnik, V.: Statistical Learning Theory. John Willey & Sons, West Sussex (1998)MATHGoogle Scholar
  17. 17.
    Zou, A., Hou, Z.G., Tan, M.: Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3645, pp. 443–452. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Hou, Z.G.: A Hierarchical Optimization Neural Network for Large-scale Dynamic Systems. Automatica 37(12), 1931–1940 (2001)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Hou, Z.G., Wu, C.P., Bao: A Neural Network for Hierarchical Optimization of Nonlinear Large-scale Systems. International Journal of Systems Science 29(2), 159–166 (1998)CrossRefGoogle Scholar
  20. 20.
    Djekoune, O., Achour, K.: Vision-guided Mobile Robot Navigation Using Neural Network. In: Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, pp. 355–361 (2001)Google Scholar
  21. 21.
    Fujii, T., Arai, Y., Asama, H., Endo, I.: Multilayered Reinforcement Learning for Complicated Collision Avoidance Problems. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 2186–2191 (1998)Google Scholar
  22. 22.
    Silva, C., Crisostomo, M., Ribeiro, B.: MONODA: A Neural Modular Architecture for Obstacle Avoidance without Knowledge of the Environment. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 6, pp. 334–339 (2000)Google Scholar
  23. 23.
    Ishii, K., Nishida, S., Watanabe, K., Ura, T.: A Collision Avoidance System based on Self-organizing Map and its Application to an Underwater Vehicle. In: 7th International Conference on Control, Automation, Robotics and Vision, vol. 2, pp. 602–607 (2002)Google Scholar
  24. 24.
    Gaudiano, P., Chang, C.: Adaptive Obstacle Avoidance with a Neural Network for Operant Conditioning: Experiments with Real Robots. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 13–18 (1997)Google Scholar
  25. 25.
    Grossberg, S., Levine, D.S.: Neural Dynamics of Attentionally Modulated Pavlovian Conditioning: Blocking, Inter-stimulus Interval, and Secondary Reinforcement. Applied Optics 26, 5015–5030 (1987)CrossRefGoogle Scholar
  26. 26.
    Nilsson, N.J.: Principles of Artificial Intelligence. Tioga Publishing Co., Palo Alto (1980)MATHGoogle Scholar
  27. 27.
    Khatib, O.: Real-time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research 5(1), 90–98 (1986)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Prusky, A.: Robotique Mobile. La Planification de Trajectoire, Hermes (1996)Google Scholar
  29. 29.
    Kozakiewicz, C., Ejiri, M.: Neural Network Approach to Path Planning for Two Dimensional Robot Motion. In: International Workshop on Intelligent Robots and Systems, vol. 2, pp. 818–823 (1991)Google Scholar
  30. 30.
    Sfeir, J., Kanaan, H.Y., Saad, M.: A Neural Network Based Path Generation Technique for Mobile Robots. In: Proceedings of the IEEE International Conference on Mechatronics, pp. 176–181 (2004)Google Scholar
  31. 31.
    Sastry, P.S., Santharam, G., Unnikrishnan, K.P.: Memory Neuron Networks for Identification and Control of Dynamic Systems. IEEE Transactions on Neural Networks 5(2), 306–319 (1994)CrossRefGoogle Scholar
  32. 32.
    Glasius, R., Komoda, A., Gielen, S.C.A.M.: Neural Network Dynamics for Path Planning and Obstacle Avoidance. Neural Networks 8(1), 125–133 (1995)CrossRefGoogle Scholar
  33. 33.
    Fierro, R., Lewis, F.L.: Control of a Nonholonomic Mobile Robot Using Neural Networks. In: Proceedings of the IEEE International Symposium on Intelligent Control, pp. 415–421 (1995)Google Scholar
  34. 34.
    Fierro, R., Lewis, F.L.: Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks. In: Proceedings of the 35th IEEE on Decision and Control, vol. 2, pp. 1722–1727 (1996)Google Scholar
  35. 35.
    Fierro, R., Lewis, F.L.: Control of a Nonholonomic Mobile Robot Using Neural Networks. IEEE Transactions on Neural Networks 9(4), 589–600 (1998)CrossRefGoogle Scholar
  36. 36.
    Yang, S.X., Meng, M.: An Efficient Neural Network Approach to Dynamic Robot Motion Planning. Neural Networks 13(2), 143–148 (2000)CrossRefGoogle Scholar
  37. 37.
    Yang, S.X., Meng, M.: An Efficient Neural Network Method for Real-time Motion Planning with Safety Consideration. Robotics and Autonomous Systems 32(2-3), 115–128 (2000)CrossRefGoogle Scholar
  38. 38.
    Yang, S.X., Meng, M.Q.-H.: Real-time Collision-free Motion Planning of a Mobile Robot Using a Neural Dynamic-based Approach. IEEE Transactions on Neural Networks 14(6), 1541–1552 (2003)CrossRefGoogle Scholar
  39. 39.
    Hodgkin, A.L., Huxley, A.F.: A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve. Journey of Physiology (London) 117, 500–544 (1952)Google Scholar
  40. 40.
    Grossberg, S.: Nonlinear Neural Networks: Principal, Mechanisms, and Architecture. Neural Networks 1, 17–61 (1988)CrossRefGoogle Scholar
  41. 41.
    Huang, S. H.: Artificial Neural Networks and its Manufacturing Application: Part 1. Online slides are available at, www.min.uc.edu/icams/resourses/ANN/ANNMaul.ppt
  42. 42.
    Jang, J.-S.R.: ANFIS: Adaptive-network-based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  43. 43.
    Godjevac, J., Steele, N.: Neuro-fuzzy Control of a Mobile Robot. Neurocomputing 28, 127–143 (1999)CrossRefGoogle Scholar
  44. 44.
    Marichal, G.N., Acosta, L., Moreno, L., Mendez, J.A., Rodrigo, J.J., Sigut, M.: Obstacle Avoidance for a Mobile Robot: A Neuro-fuzzy Approach. Fuzzy Sets and Systems 124(2), 171–179 (2001)MATHCrossRefMathSciNetGoogle Scholar
  45. 45.
    Er, M.J., Deng, C.: Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach. IEEE Transactions on Industrial Electronics 52(3), 898–905 (2005)CrossRefGoogle Scholar
  46. 46.
    Wu, S., Er, M.J., Gao, Y.: A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks. IEEE Transactions on Fuzzy Systems 9(4), 578–594 (2001)CrossRefGoogle Scholar
  47. 47.
    Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4, 759–771 (1991)CrossRefGoogle Scholar
  48. 48.
    Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps. IEEE Transactions on Neural Networks 3, 698–713 (1992)CrossRefGoogle Scholar
  49. 49.
    Araujo, R., de Almeida, A.T.: Learning Sensor-based Navigation of a Real Mobile Robot in Unknown Worlds. IEEE Transactions on Systems, Man and Cybernetics, Part B 29(2), 164–178 (1999)CrossRefGoogle Scholar
  50. 50.
    Araujo, R., Lourenco, D., Ferreira, G.: Integrating Laser and Infrared Sensors for Learning Dynamic Self-organizing World Maps. In: International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 293–298 (2001)Google Scholar
  51. 51.
    Streilein, W.W., Gaudiano, P., Carpenter, G.A.: A Neural Network for Object Recognition through Sonar on a Mobile Robot. In: Proceedings of the IEEE ISIC/CIRA/ISAS Joint Conference, pp. 271–276 (1998)Google Scholar
  52. 52.
    Azouaoui, O., Ouaaz, M., Chohra, A., Farah, A., Achour, K.: Fuzzy ARTMap Neural Network Based Collision Avoidance Approach for Autonomous Robots Systems. In: Proceedings of the Second International Workshop on Robot Motion and Control, pp. 285–290 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • An-Min Zou
    • 1
  • Zeng-Guang Hou
    • 1
  • Si-Yao Fu
    • 1
  • Min Tan
    • 1
  1. 1.Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationThe Chinese Academy of SciencesBeijingChina

Personalised recommendations