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25 October 2004 Advances in learning for intelligent mobile robots
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Abstract
Intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths to accomplish a variety of tasks. Such machines have many potential useful applications in medicine, defense, industry and even the home so that the design of such machines is a challenge with great potential rewards. Even though intelligent systems may have symbiotic closure that permits them to make a decision or take an action without external inputs, sensors such as vision permit sensing of the environment and permit precise adaptation to changes. Sensing and adaptation define a reactive system. However, in many applications some form of learning is also desirable or perhaps even required. A further level of intelligence called understanding may involve not only sensing, adaptation and learning but also creative, perceptual solutions involving models of not only the eyes and brain but also the mind. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots with examples of adaptive, creative and perceptual learning. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to important beneficial applications.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ernest L. Hall, Masoud Ghaffari, Xiaoqun Sherry Liao, and Souma M. Alhaj Ali "Advances in learning for intelligent mobile robots", Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); https://doi.org/10.1117/12.571195
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