Mobile robot path planning using Ant Colony Optimization
Ant colony optimization (ACO) technique is proposed to solve the mobile robot path planning (MRPP) problem. In order to demonstrate the effectiveness of ACO in solving the MRPP problem, several maps of varying complexity used by an earlier researcher is used for evaluation. Each map consists of stat...
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Institute of Electrical and Electronics Engineers Inc.
2017
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my.utp.eprints.201552018-04-22T14:43:39Z Mobile robot path planning using Ant Colony Optimization Rashid, R. Perumal, N. Elamvazuthi, I. Tageldeen, M.K. Khan, M.K.A.A. Parasuraman, S. Ant colony optimization (ACO) technique is proposed to solve the mobile robot path planning (MRPP) problem. In order to demonstrate the effectiveness of ACO in solving the MRPP problem, several maps of varying complexity used by an earlier researcher is used for evaluation. Each map consists of static obstacles in different arrangements. Besides that, each map has a grid representation with an equal number of rows and columns. The performance of the proposed ACO is tested on a given set of maps. Overall, the results demonstrate the effectiveness of the proposed approach for path planning. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015962268&doi=10.1109%2fROMA.2016.7847836&partnerID=40&md5=bda25be6f6d2162eb028ffe7718cf325 Rashid, R. and Perumal, N. and Elamvazuthi, I. and Tageldeen, M.K. and Khan, M.K.A.A. and Parasuraman, S. (2017) Mobile robot path planning using Ant Colony Optimization. 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation, ROMA 2016 . http://eprints.utp.edu.my/20155/ |
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Ant colony optimization (ACO) technique is proposed to solve the mobile robot path planning (MRPP) problem. In order to demonstrate the effectiveness of ACO in solving the MRPP problem, several maps of varying complexity used by an earlier researcher is used for evaluation. Each map consists of static obstacles in different arrangements. Besides that, each map has a grid representation with an equal number of rows and columns. The performance of the proposed ACO is tested on a given set of maps. Overall, the results demonstrate the effectiveness of the proposed approach for path planning. © 2016 IEEE. |
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Article |
author |
Rashid, R. Perumal, N. Elamvazuthi, I. Tageldeen, M.K. Khan, M.K.A.A. Parasuraman, S. |
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Rashid, R. Perumal, N. Elamvazuthi, I. Tageldeen, M.K. Khan, M.K.A.A. Parasuraman, S. Mobile robot path planning using Ant Colony Optimization |
author_facet |
Rashid, R. Perumal, N. Elamvazuthi, I. Tageldeen, M.K. Khan, M.K.A.A. Parasuraman, S. |
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Rashid, R. |
title |
Mobile robot path planning using Ant Colony Optimization |
title_short |
Mobile robot path planning using Ant Colony Optimization |
title_full |
Mobile robot path planning using Ant Colony Optimization |
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Mobile robot path planning using Ant Colony Optimization |
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Mobile robot path planning using Ant Colony Optimization |
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mobile robot path planning using ant colony optimization |
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Institute of Electrical and Electronics Engineers Inc. |
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2017 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015962268&doi=10.1109%2fROMA.2016.7847836&partnerID=40&md5=bda25be6f6d2162eb028ffe7718cf325 http://eprints.utp.edu.my/20155/ |
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