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Design and Development of Intelligent Autonomous Robots

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Intelligent Autonomous Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 275))

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Abstract

The present chapter deals with the issues related to design and development of autonomous mobile robots. One of the major issues in developing an intelligent and autonomous robot is to design an appropriate scheme for planning its motion without any human intervention. Both conventional potential field method as well as soft computing-based approaches have been developed for the said purpose. Initially, the performances of all the approaches have been studied through computer simulations. Thereafter, real experiments are conducted to test the effectiveness of the said approaches. A camera-based vision system has been used to collect information of the environment, while carrying out the real experiments.

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References

  1. Abdel-Aziz, I.I., Karara, H.M.: Direct linear transformation into object space coordinates in close-range photogrammetry. In: Proceedings Symposium Close-Range Photogrammetry, University of Illinois at Urbana Champaign, Urbana, pp. 1–18 (1971)

    Google Scholar 

  2. Akbarzadeh, M., Kumbla, K., Tunstel, E., Jamshidi, M.: Soft computing for autonomous robotic systems. Computers and Electrical Engineering 26, 5–32 (2000)

    Article  Google Scholar 

  3. Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for autonomy. Special issue on: Integrated architectures for robot control and programming 17(4) (1998)

    Google Scholar 

  4. Asada, M., Kitano, H., Noda, I., Veloso, M.: Robocup: today and tomorrow - what we have learned. Artificial Intelligence 110, 193–214 (1999)

    Article  MATH  Google Scholar 

  5. Berenji, H.R.: Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks 3, 724–740 (1992)

    Article  Google Scholar 

  6. Borenstein, J., Koren, Y.: Real time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems Man and Cybernetics 19(5), 1179–1187 (1989)

    Article  Google Scholar 

  7. Borenstein, J., Koren, Y.: The vector field histogram – fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation 7(3), 278–288 (1991)

    Article  Google Scholar 

  8. Casillas, J., Cordon, O., Herrera, F.: Learning fuzzy rules using ant colony optimization algorithms. In: Proceedings of 2nd International Workshop on Ant Algorithms, Brussels, Belgium, pp. 13–21 (2000)

    Google Scholar 

  9. Cokal, E., Erden, A.: Development of an image processing system for a special purpose mobile robot navigation. In: Proceedings of Fourth Annual Conference on Mechatronics and Machine Vision Practice, Toowoomba, Australia, pp. 246–252 (1997)

    Google Scholar 

  10. Denna, M., Mauri, G., Zanaboni, A.M.: Learning fuzzy rules with tabu search - an application to control. IEEE Transactions on Fuzzy Systems 7(2), 295–318 (1999)

    Article  Google Scholar 

  11. Faig, W.: Calibration of close-range photogrammetry systems: mathematical formulation. Photogrammetric Engineering and Remote Sensing 41, 1479–1486 (1975)

    Google Scholar 

  12. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using the relative velocity paradigm. In: Proceedings of IEEE Conference on Robotics and Automation, pp. 560–565 (1993)

    Google Scholar 

  13. Fraichard, T., Garnier, P.: Fuzzy control to drive car-like vehicles. Robotics and Autonomous Systems 34, 1–22 (2001)

    Article  Google Scholar 

  14. Fujimura, K., Samet, H.: Accessibility: a new approach to path planning among moving obstacles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Ann Arbor, MI, pp. 803–807 (1988)

    Google Scholar 

  15. Gu, D., Hu, H.: Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems 39, 73–86 (2002)

    Article  Google Scholar 

  16. Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  17. Hui, N.B.: Design and development of adaptive robot motion planner for wheeled robots. Ph.D. thesis, Indian Institute of Technology, Kharagpur (2008)

    Google Scholar 

  18. Kant, K., Zucker, S.W.: Towards efficient planning: the path velocity decomposition. International Journal of Robotics Research 5(1), 72–89 (1986)

    Google Scholar 

  19. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research 5(1), 90–98 (1986)

    Article  MathSciNet  Google Scholar 

  20. Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  21. Lamadrid, J.G., Gini, M.L.: Path tracking through uncharted moving obstacles. IEEE Transactions on Systems, Man and Cybernetics 20(6), 1408–1422 (1990)

    Article  Google Scholar 

  22. Latombe, J.C.: Robot motion planning. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  23. Lee, P.S., Shen, Y.E.: Model-based location of automated guided vehicles in the navigation sessions by 3d computer vision. Journal of Robotic Systems 11(3), 181–195 (1994)

    Article  MATH  Google Scholar 

  24. Leven, D., Sharir, M.: Planning a purely translational motion for a convex object in two-dimensional space using generalized voronoi diagrams. Discrete Computational Geometry 2, 9–31 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  25. Liu, Y.H., Arimoto, S.: Path planning using a tangent graph for mobile robots among polynomial and curved obstacles. International Journal of Robotics Research 11(4), 376–382 (1992)

    Article  Google Scholar 

  26. Mackworth, A.: On seeing robots. In: Computer Vision: Systems, Theory and Applications, pp. 1–13. World Scientific Press, Singapore (1993)

    Google Scholar 

  27. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7, 1–13 (1975)

    Article  MATH  Google Scholar 

  28. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state claculations by fast computing machines. Journal of Chemical Physics 21, 1087–1092 (1953)

    Article  Google Scholar 

  29. Mondada, F., Floreano, D.: Evolution of neural control structures: some experiments on mobile robots. Robotics and Autonomous Systems 16, 183–195 (1995)

    Article  Google Scholar 

  30. Nilsson, N.J.: A mobile automation: an application of artificial intelligence techniques. In: Proceedings of the 1st International Joint Conference on Artificial Intelligence (IJCAI), Washington, DC, pp. 509–520 (1969)

    Google Scholar 

  31. Nolfi, S., Parsi, D.: Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior 5(1), 75–98 (1997)

    Article  Google Scholar 

  32. Nomura, H., Hayashi, I., Wakami, W.: A learning method of fuzzy inference rules by descent method. In: Proceedings of IEEE International Conference Fuzzy Systems, San Diego, CA (1992)

    Google Scholar 

  33. Pham, T.D., Valliappan, S.: A least squares model for fuzzy rules of inference. Fuzzy Sets and Systems 64, 207–212 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  34. Pratihar, D.K.: Algorithmic and soft computing approaches to robot motion planning. Machine Intelligence and Robotic Control 5(1), 1–16 (2003)

    Google Scholar 

  35. Pratihar, D.K.: Evolutionary robotics - a review. Sadhana 28(6), 999–1003 (2003)

    Article  Google Scholar 

  36. Pratihar, D.K.: Soft computing. Narosa Publishing House, New Delhi (2008)

    Google Scholar 

  37. Pratihar, D.K., Bibel, W.: Near-optimal, collision-free path generation for multiple robots working in the same workspace using a genetic-fuzzy systems. Machine Intelligence and Robotic Control 5(2), 45–58 (2003)

    Google Scholar 

  38. Slack, M.: Navigation template: mediating qualitative guidance control in mobile robots. IEEE Transactions on Systems, Man, Cybernetics 23(2), 452–466 (1993)

    Article  MathSciNet  Google Scholar 

  39. Svestka, P., Overmars, M.: Motion planning for car-like robots using a probabilistic learning approach. International Journal of Robotics Research 16(2), 119–143 (1997)

    Article  Google Scholar 

  40. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15, 116–132 (1985)

    MATH  Google Scholar 

  41. Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 2d machine vision meteorology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation 4, 323–344 (1987)

    Article  Google Scholar 

  42. Tsukiyama, T., Huang, T.: Motion stereo for navigation of autonomous vehicle in man-made environments. Pattern Recognition 20(1), 105–113 (1987)

    Article  Google Scholar 

  43. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man and Cybernetics 22(6), 1414–1422 (1992)

    Article  MathSciNet  Google Scholar 

  44. Weng, J., Cohen, P., Herniou, M.: Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(10), 965–980 (1992)

    Article  Google Scholar 

  45. Yamada, S.: Evolutionary behavior learning for action based environment modeling by a mobile robot. Applied Soft Computing 5, 245–257 (2005)

    Article  Google Scholar 

  46. Yang, S.X., Meng, M.: An efficient neural network approach to dynamic robot motion planning. Neural Networks 13(2), 143–148 (2000)

    Article  Google Scholar 

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Hui, N.B., Pratihar, D.K. (2010). Design and Development of Intelligent Autonomous Robots. In: Pratihar, D.K., Jain, L.C. (eds) Intelligent Autonomous Systems. Studies in Computational Intelligence, vol 275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11676-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-11676-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11675-9

  • Online ISBN: 978-3-642-11676-6

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