NDLI: A Fast Genetic SLAM Approach for Mobile Robots

Content Provider	IEEE Xplore Digital Library
Author	Chih-Hung Wu Yi-Han Chen Yao-Yu Lee Chiung-Hui Tsai
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan (Chih-Hung Wu; Yi-Han Chen; Yao-Yu Lee; Chiung-Hui Tsai)
Abstract	This paper presents a new SLAM (simultaneous localization and mapping) method using genetic algorithm (GA) for mobile robots. A laser range finder (LRF) is installed on a mobile robot for collecting point-distance information about the surroundings. From the LRF points, several important ones are extracted for describing the main features of the surroundings. A new form of chromosomes for representing the changes of feature LRF points that are caused by the robot's movement is designed. The matching of current LRF features and the robot's possible movement is done by a fast genetic algorithm. A restart mechanism that re-initializes all chromosomes for increasing the diversity of solutions is developed and works with the matching process. Some constrains are developed for filtering out irrational chromosomes after the operation of crossover and mutation. With these mechanisms and constrains, our proposed method generates feasible solutions in several hundreds of GA iterations. Experiments are conducted on a real mobile robot. The experimental results show that our proposed method is efficient and effective for SLAM.
Sponsorship	IEEE Comput. Soc.
Starting Page	563
Ending Page	568
File Size	618207
Page Count	6
File Format	PDF
ISBN	9780769550053
DOI	10.1109/SNPD.2013.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	constrained evolution genetic algorithm Simultaneous localization and mapping simultaneous localization and mapping laser range finder Feature extraction Genetics Mobile robots Biological cells Genetic algorithms
Content Type	Text
Resource Type	Article

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