Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot

Autonomous cooperating mobile robots is one of technological advancement in enabling autonomy in search and rescue task (SaR). While, simultaneous localization and mapping (SLAM) algorithm is one of a key element to enable autonomous navigation. Many researchers chose to adopt high-end sensors to so...

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Main Author:	Jamaludin, Amirul
Format:	Thesis
Language:	English English
Published:	2024
Online Access:	http://eprints.utem.edu.my/id/eprint/28261/1/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf http://eprints.utem.edu.my/id/eprint/28261/2/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf
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spelling	my-utem-ep.282612024-12-13T11:16:55Z Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot 2024 Jamaludin, Amirul Autonomous cooperating mobile robots is one of technological advancement in enabling autonomy in search and rescue task (SaR). While, simultaneous localization and mapping (SLAM) algorithm is one of a key element to enable autonomous navigation. Many researchers chose to adopt high-end sensors to solve Cooperative SLAM or CSLAM. But this will cause staggering cost for multiple robot system. Thus, the alternative is to implement low-cost sensors with limited sensing to perform CSLAM. However, this approach introduces challenges such as inaccurate robots’ sensors measurements and low accuracy cooperative mapping were reported. In this research, an Artificial Neural Network (ANN) is proposed to improve the accuracy of the CSLAM algorithm with lowcost sensors and is evaluated using real robots. Here, the selected methodologies are divided into three important stages to support three objectives defined to solve the stated problem statement. Firstly, the ANN configurations is established to reduce the nonlinearity error of the low-cost sensor measurements for building high accuracy environmental map. By training the ANN using sensor measurements, it learns to model the data and reduce the error or uncertainties present in the measurements obtained from the low-cost sensors. Secondly, a framework of CSLAM algorithm integrated with ANN using Rao-Blackwellized particle filter (RBPF) algorithm for single SLAM robot, and the map merging using random sample consensus (RANSAC) algorithm, is designed and developed. Lastly, the performance of the CSLAM algorithm with ANN is evaluated and validated using measurements from real robot platforms, and compared to that without ANN. From the real-world experiment, CSLAM with ANN has increased the performance of resulting maps by 61.09% compared to without ANN. It shows that, CSLAM integrated with ANN have improved the performance of CSLAM significantly. Moreover, CSLAM integrated with ANN have achieved 3 closed loop condition out of 10 trials for 600 particles compared to without ANN that does not achieve closed loop map out of 10 trials even though, the number of particles is increased. From the results, it can be concluded that the development of CSLAM algorithm integrated with ANN able to improve the performance of CSLAM for mobile robot using low-cost sensor. 2024 Thesis http://eprints.utem.edu.my/id/eprint/28261/ http://eprints.utem.edu.my/id/eprint/28261/1/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf text en public http://eprints.utem.edu.my/id/eprint/28261/2/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124170 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Technology and Engineering Mohamad Yatim, Norhidayah
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Mohamad Yatim, Norhidayah
description	Autonomous cooperating mobile robots is one of technological advancement in enabling autonomy in search and rescue task (SaR). While, simultaneous localization and mapping (SLAM) algorithm is one of a key element to enable autonomous navigation. Many researchers chose to adopt high-end sensors to solve Cooperative SLAM or CSLAM. But this will cause staggering cost for multiple robot system. Thus, the alternative is to implement low-cost sensors with limited sensing to perform CSLAM. However, this approach introduces challenges such as inaccurate robots’ sensors measurements and low accuracy cooperative mapping were reported. In this research, an Artificial Neural Network (ANN) is proposed to improve the accuracy of the CSLAM algorithm with lowcost sensors and is evaluated using real robots. Here, the selected methodologies are divided into three important stages to support three objectives defined to solve the stated problem statement. Firstly, the ANN configurations is established to reduce the nonlinearity error of the low-cost sensor measurements for building high accuracy environmental map. By training the ANN using sensor measurements, it learns to model the data and reduce the error or uncertainties present in the measurements obtained from the low-cost sensors. Secondly, a framework of CSLAM algorithm integrated with ANN using Rao-Blackwellized particle filter (RBPF) algorithm for single SLAM robot, and the map merging using random sample consensus (RANSAC) algorithm, is designed and developed. Lastly, the performance of the CSLAM algorithm with ANN is evaluated and validated using measurements from real robot platforms, and compared to that without ANN. From the real-world experiment, CSLAM with ANN has increased the performance of resulting maps by 61.09% compared to without ANN. It shows that, CSLAM integrated with ANN have improved the performance of CSLAM significantly. Moreover, CSLAM integrated with ANN have achieved 3 closed loop condition out of 10 trials for 600 particles compared to without ANN that does not achieve closed loop map out of 10 trials even though, the number of particles is increased. From the results, it can be concluded that the development of CSLAM algorithm integrated with ANN able to improve the performance of CSLAM for mobile robot using low-cost sensor.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Jamaludin, Amirul
spellingShingle	Jamaludin, Amirul Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
author_facet	Jamaludin, Amirul
author_sort	Jamaludin, Amirul
title	Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
title_short	Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
title_full	Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
title_fullStr	Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
title_full_unstemmed	Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
title_sort	design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronics and Computer Technology and Engineering
publishDate	2024
url	http://eprints.utem.edu.my/id/eprint/28261/1/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf http://eprints.utem.edu.my/id/eprint/28261/2/Design%20and%20development%20of%20cooperative%20simultaneous%20localization%20and%20mapping%20integrated%20with%20neural%20network%20for%20mobile%20robot.pdf
_version_	1818612059004207104

Design and development of cooperative simultaneous localization and mapping integrated with neural network for mobile robot

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