<title>Advances in learning for intelligent mobile robots</title>

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.