Title: Genetic algorithm based solution to dead-end problems in robot navigation

Authors: Xiaoming Kang; Yong Yue; Dayou Li; Carsten Maple

Addresses: China Architectural Design Institute, Beijing, China. ' Department of Computer Science and Technology, University of Bedfordshire, Park Square, Luton LU1 3JU, UK. ' Department of Computer Science and Technology, University of Bedfordshire, Park Square, Luton LU1 3JU, UK. ' Department of Computer Science and Technology, University of Bedfordshire, Park Square, Luton LU1 3JU, UK

Abstract: 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.

Keywords: dead end areas; genetic algorithms; obstacle avoidance; robot navigation; mobile robots; robotic exploration; dead-end detection; escape mechanisms; dead ends.

DOI: 10.1504/IJCAT.2011.042693

International Journal of Computer Applications in Technology, 2011 Vol.41 No.3/4, pp.177 - 184

Available online: 25 Sep 2011 *

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