Genetic Algorithms For Auto-tuning Mobile Robot Motion Control

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  1. Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Systems
  2. Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Devices
  3. Genetic Algorithms For Auto-tuning Mobile Robot Motion Control System
  4. Genetic Algorithms For Auto-tuning Mobile Robot Motion Control System
  5. Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Board
  1. Blickle, T., Thiele, L.A Comparison of Selection Schemes used in Genetic Algorithms, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology/ETH Zürich, Report no. 11, 1995Google Scholar
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  3. Darwin, C.On the Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life, John Murray, London, 1859Google Scholar
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  7. Hwang, Y.Object Tracking for Robotic Agent with Genetic Programming, B.E. Honours Thesis, The Univ. of Western Australia, Electrical and Computer Eng., supervised by T. Bräunl, 2002Google Scholar
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  10. Kurashige, K., Fukuda, T., Hoshino, H.Motion planning based on hierarchical knowledge for six legged locomotion robot, Proceedings of IEEE International Conference on Systems, Man and Cybernetics SMC’99, vol. 6, 1999, pp. 924–929 (6)Google Scholar
  11. Langdon, W., Poli, R.Foundations of Genetic Programming, Springer-Verlag, Heidelberg, 2002Google Scholar
  12. Lee, W., Hallam, J., Lund, H.Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots, IEEE International Conference on Evolutionary Computation (ICEC97), 1997, pp. 501–506 (6)Google Scholar
  13. Mahadevan, S., Connell, J.Automatic programming of behaviour-based robots using reinforcement learning, Proceedings of the Ninth National Conference on Artificial Intelligence, vol. 2, AAAI Press/MIT Press, Cambridge MA, 1991Google Scholar
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  15. Walker, M., Messom, C.A comparison of genetic programming and genetic algorithms for auto-tuning mobile robot motion control, Proceedings of IEEE International Workshop on Electronic Design, Test and Applications, 2002, pp. 507–509 (3)Google Scholar

Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Systems

  1. Buckle, T., Thiele, L.A Comparison of Selection Schemes used in Genetic Algorithms, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology/ETH Zürich, Report no. 11, 1995Google Scholar
  2. Brooks, R.A Robust Layered Control System for a Mobile Robot, IEEE Journal of Robotics and Automation, vol. 2, no. 1, March 1986, pp. 14–23 (10)MathSciNetCrossRefGoogle Scholar
  3. Darwin, C.On the Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life, John Murray, London, 1859Google Scholar
  4. Fernandez, J.The GP Tutorial — The Genetic Programming Notebook, http://www.geneticprogramming.com/Tutorial/, 2003Google Scholar
  5. Graham, P.ANSI Common Lisp, Prentice Hall, Englewood Cliffs NJ, 1995Google Scholar
  6. Hancock, P.An empirical comparison of selection methods in evolutionary algorithms, in T. Fogarty (Ed.), Evolutionary Computing, AISB Workshop, Lecture Notes in Computer Science, no. 865, Springer-Verlag, Berlin Heidelberg, 1994, pp. 80–94 (15)Google Scholar
  7. Hwang, Y.Object Tracking for Robotic Agent with Genetic Programming, B.E. Honours Thesis, The Univ. of Western Australia, Dept. of Electrical and Electronic Eng., supervised by T. Bräunl, 2002Google Scholar
  8. Iba, H., Nozoe, T., Ueda, K.Evolving communicating agents based on genetic programming, IEEE International Conference on Evolutionary Computation (ICEC97), 1997, pp. 297–302 (6)Google Scholar
  9. Koza, J.Genetic Programming — On the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge MA, 1992zbMATHGoogle Scholar
  10. Kurashige, K., Fukuda, T., Hoshino, H.Motion planning based on hierarchical knowledge for six legged locomotion robot, Proceedings of IEEE International Conference on Systems, Man and Cybernetics SMC’99, vol. 6, 1999, pp. 924–929 (6)Google Scholar
  11. Langdon, W., Poli, R.Foundations of Genetic Programming, Springer-Verlag, Heidelberg, 2002zbMATHCrossRefGoogle Scholar
  12. Lee, W., Hallam, J., Lund, H.Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots, IEEE International Conference on Evolutionary Computation (ICEC97), 1997, pp. 501–506 (6)Google Scholar
  13. Mahadevan, S., Connell, J.Automatic programming of behaviour-based robots using reinforcement learning, Proceedings of the Ninth National Conference on Artificial Intelligence, vol. 2, AAAI Press/MIT Press, Cambridge MA, 1991Google Scholar
  14. McCarthy, J., Abrahams, P., Edwards, D., Hart, T., Levin, M.The Lisp Programmers’ Manual, MIT Press, Cambridge MA, 1962Google Scholar
  15. Walker, M., Messom, C.A comparison of genetic programming and genetic algorithms for auto-tuning mobile robot motion control, Proceedings of IEEE International Workshop on Electronic Design, Test and Applications, 2002, pp. 507–509 (3)Google Scholar

Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Devices

Auto-tuning

Genetic Algorithms For Auto-tuning Mobile Robot Motion Control System

Genetic

Genetic Algorithms For Auto-tuning Mobile Robot Motion Control System

Walker, M., Messom, C. A comparison of genetic programming and genetic algorithms for auto-tuning mobile robot motion control, Proceedings of IEEE International Workshop on Electronic Design, Test and Applications, 2002, pp. 507-509 (3) CrossRef Google Scholar. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C), ISSN 1841-9836. IJCCC was founded in 2006, at Agora University, by Ioan DZITAC (Editor-in-Chief), Florin Gheorghe FILIP (Editor-in-Chief), and Misu-Jan MANOLESCU (Managing Editor). In both cases, genetic algorithm can be used for solving auto-tuning mobile robot control depending of path planning tasks and type of obstacles 5. Genetic algorithms are used as a path planning. This paper discusses the use of genetic programming (GP) and genetic algorithms (GA) to evolve solutions to a problem in robot control. GP is seen as an intuitive evolutionary method while GAs require an extra layer of human intervention. The infrastructures for the.

Genetic Algorithms For Auto-tuning Mobile Robot Motion Control Board

In this paper, a hybrid genetic algorithm (GA) and backstepping based tracking controller is proposed for a nonholonomic mobile robot. Backstepping is a commonly used technique for nonlinear control systems. By using a backstepping motion controller alone, there exist oscillations at the initial phase in both linear velocity and angular velocity, as well as a big initial linear velocity jump. A comparison of genetic programming and genetic algorithms for auto-tuning mobile robot motion control, Proceedings of IEEE International Workshop on Electronic Design, Test and Applications, 2002, pp. 507–509 (3) Google Scholar. Walker, M., Messom, C. A comparison of genetic programming and genetic algorithms for auto-tuning mobile robot motion control, Proceedings of IEEE International Workshop on Electronic Design, Test and Applications, 2002, pp. 507–509 (3) Google Scholar.