Bassel Zeidan

Group(s): Neural Control and Robotics
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    Journal / Proceedings / Book
    Zeidan, B. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P. (2014).
    Adaptive Landmark-Based Navigation System Using Learning Techniques. From Animals to Animats 13, 121-131, 8575. DOI: 10.1007/978-3-319-08864-8_12.
    BibTeX:
    @inproceedings{zeidandasguptawoergoetter2014,
      author = {Zeidan, B. and Dasgupta, S. and Wörgötter, F. and Manoonpong, P.},
      title = {Adaptive Landmark-Based Navigation System Using Learning Techniques},
      pages = {121-131},
      booktitle = {From Animals to Animats 13},
      year = {2014},
      volume= {8575},
      editor = {del Pobil, AngelP. and Chinellato, Eris and Martinez-Martin, Ester and Hallam, John and Cervera, Enric and Morales, Antonio},
      language = {English},
      month = {July},
      publisher = {Springer International Publishing},
      series = {Lecture Notes in Computer Science},
      url = http://dx.doi.org/10.1007/978-3-319-08864-8_12},
      doi = 10.1007/978-3-319-08864-8_12},
      abstract = The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.}}
    		
    Abstract: The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.
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