Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/83795
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Type: Conference paper
Title: Evolving decision-making functions in an autonomous robotic exploration strategy using grammatical evolution
Author: Ibrahim, M.
Alexander, B.
Citation: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2013 / pp.4340-4346
Publisher: IEEE
Publisher Place: United States
Issue Date: 2013
Series/Report no.: IEEE International Conference on Intelligent Robots and Systems
ISBN: 9781467363587
ISSN: 2153-0858
2153-0866
Conference Name: International Conference on Intelligent Robots and Systems (2013 : Tokyo, Japan)
Editor: Amato, N.
Statement of
Responsibility: 
Mohd Faisal Ibrahim and Bradley James Alexander
Abstract: Customising navigational control for autonomous robotic mapping platforms is still a challenging task. Control software must simultaneously maximise the area explored whilst maintaining safety and working within the constraints of the platform. Scoring functions to assess navigational options are typically written by hand and manually refined. As navigational tasks become more complex this manual approach is unlikely to yield the best results. In this paper we explore the automatic derivation of a scoring function for a ground based exploration platform. We show that it is possible to derive the entire structure of a scoring function and that allowing structure to evolve yields significant performance advantages over the evolution of embedded constants alone.
Rights: ©2013 IEEE
DOI: 10.1109/IROS.2013.6696979
Published version: http://dx.doi.org/10.1109/iros.2013.6696979
Appears in Collections:Aurora harvest
Computer Science publications

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