Mobile Robot Path Planning Using Genetic Algorithms
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A CBR System for Autonomous Robot Navigation
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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In robot navigation, mobile robots can suffer from dead-end problems, that is, they can be stuck in areas which are surrounded by obstacles. Attempts have been reported to avoid a robot entering into such a dead-end area. However, in some applications, for example, rescue work, the dead-end areas must be explored. Therefore, it is vital for the robot to come out from the dead-end areas after exploration. This paper presents an approach which enables a robot to come out from dead-end areas. There are two main parts: a dead-end detection mechanism and a genetic algorithm (GA) based online training mechanism. When the robot realises that it is stuck in a dead-end area, it will operate the online training to produce a new best chromosome that will enable the robot to escape from the area.