ISSN:
1573-0409
Keywords:
behaviour decomposition
;
behaviour learning
;
intelligent navigation
;
decision tress
;
ITI
;
machine learning
;
robotics
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract This paper presents a new approach to the intelligent navigation of a mobile robot. The hybrid control architecture described combines properties of purely reactive and behaviour-based systems, providing the ability both to learn automatically behaviours from inception, and to capture these in a distributed hierarchy of decision tree networks. The robot is first trained in the simplest world which has no obstacles, and is then trained in successively more complex worlds, using the knowledge acquired in the previous worlds. Each world representing the perceptual space is thus directly mapped on a unique rule layer which represents in turn the robot action space encoded in a distinct decision tree. A major advantage of the current implementation, compared with the previous work, is that the generated rules are easily understood by human users. The paper demonstrates that the proposed behavioural decomposition approach provides efficient management of complex knowledge, and that the learning mechanism is able to cope with noise and uncertainty in sensory data.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1008012519312
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