Autonomous Navigation For TurtleBot3 Robots in Gazebo Simulation Environment

Conference paper

Theeng Tamang, M., Maheriya, D., Sharif, S. and Sutharssan, T. 2024. Autonomous Navigation For TurtleBot3 Robots in Gazebo Simulation Environment. 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies.
AuthorsTheeng Tamang, M., Maheriya, D., Sharif, S. and Sutharssan, T.
TypeConference paper
Abstract

The fast-paced growth in the field of robotics has driven the creation of autonomous navigation systems that are necessary for robots to work autonomously in diverse environments. This research targets TurtleBot3, an immensely popular robotic platform that is known for its affordability and adaptability. The objective of TurtleBot3 is to improve the capabilities within the Gazebo simulation environment, an open-source robotic simulator that provides a realistic virtual environment for testing and developing navigation algorithms for robotics. The combination of intricate sensor technologies, leading-edge control systems, and innovative artificial intelligence strategies is the core of this research, enabling autonomous navigation in complicated surroundings. The initial focus of the study is the application and utilization of path planning algorithms, such as A* (A Star) and Rapidly exploring Random Tree (RRT) and using Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations demonstrate that the A* algorithm achieved a path accuracy rate of over 95% in static environments, while the RRT algorithm proved effective in dynamic, multi-dimensional spaces. The aim of this analysis is to enhance autonomous navigation algorithms using the Gazebo simulation environment. Conducting tests in a virtual setting minimizes the risks and costs compared to real-time testing. The outcomes of this research are anticipated to advance the field of robotics and hold significant value for applications in autonomous systems, including engineering, urban mobility, and domestic assistance.

Year2024
Conference2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies
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CompletedNov 2024
Accepted02 Nov 2024
Deposited20 Dec 2024
Copyright holder© 2024 The Authors
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Fasanmade, A., Aliyu, S., He, Y., Al-Bayatti, A. H., Sharif, S. and Alfakeeh, A. S. 2019. Context-Aware Driver Distraction Severity Classification using LSTM Network. IEEE International Conference on Computing, Electronics & Communications Engineering 2019 (IEEE iCCECE '19) . London Metropolitan University, London, UK 22 - 23 Aug 2019 IEEE. pp. 147-152 https://doi.org/10.1109/iCCECE46942.2019.8941966
Predicting the Standard and Deviant Patterns In EEG Signals Based On Deep Learning Model
Sharif, S., Al-Bayatti, A. H. and Alfakeeh, A. S. 2019. Predicting the Standard and Deviant Patterns In EEG Signals Based On Deep Learning Model. IEEE International Conference on Computing, Electronics & Communications Engineering 2019 (IEEE iCCECE '19) . London Metropolitan University, London, UK 22 - 23 Aug 2019 IEEE. https://doi.org/10.1109/iCCECE46942.2019.8941730
Effect of PET Image Reconstruction Techniques on Unexpected Aorta Uptake
Hirji, H., Sullivan, K., Lasker, I., Sharif, S., Nunes, A., Shepherd, C., Wong, W. and Sanghera, B. 2019. Effect of PET Image Reconstruction Techniques on Unexpected Aorta Uptake. Molecular Imaging and Radionuclide Therapy. 28 (1), pp. 1-7. https://doi.org/10.4274/mirt.galenos.2018.88528
Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data
Ebenuwa, S., Sharif, M., Alazab, Mamoun and Al-Nemrat, A. 2019. Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data. IEEE Access. 7, pp. 24649-24666. https://doi.org/10.1109/ACCESS.2019.2899578
Medical data analysis based on Nao robot: An automated approach towards robotic real-time interaction with human body
Sharif, M. and Alsibai, Mohammed Hayyan 2018. Medical data analysis based on Nao robot: An automated approach towards robotic real-time interaction with human body. in: 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) IEEE. pp. 91-96
A Proposed Machine Learning Based Collective Disease Model to Enable Predictive Diagnostics in Necrotising Enterocolitis
van Druten, Jacqueline, Sharif, M., Khashu, Minesh and Abdalla, H. 2019. A Proposed Machine Learning Based Collective Disease Model to Enable Predictive Diagnostics in Necrotising Enterocolitis. in: Miraz, Mahdi H., Exce, Peter S., Jones, Andrew, Soomro, Safeeullah and Ali, Maaruf (ed.) Proceedings 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE) IEEE. pp. 101-106
A Machine Learning Techniques to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser
Alsallal, Muna, Sharif, M., Al-Ghzawi, Baydaa and al Mutoki, Sabah Mohammed Mlkat 2019. A Machine Learning Techniques to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser. in: Miraz, Mahdi H., Excell, Peter S., Jones, Andrew, Soomro, Safeeullah and Ali, Maaruf (ed.) Proceedings 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE) IEEE. pp. 118-122
An Innovative EPW Design Using Add-on Features to Meet Malaysian Requirements
Alsibai, Mohammed Hayyan, Sharif, M., Yaakub, Salma and Hamran, Nurul Nadia Nor 2018. An Innovative EPW Design Using Add-on Features to Meet Malaysian Requirements. in: Proceedings of the 7th IEEE International Conference on Control Systems, Computing and Engineering (ICCSCE 2017) IEEE. pp. 180-185
An Effective TeleHealth Assistive System to Support Senior Citizen at Home or Care-Homes
Sharif, M., Alsallal, Muna and Herghelegiu, Lucian 2018. An Effective TeleHealth Assistive System to Support Senior Citizen at Home or Care-Homes. IEEE International Conference on Computing, Electronics & Communications Engineering 2018 (iCCECE '18). Southend, UK 16 - 17 Aug 2018 IEEE. pp. 113-117 https://doi.org/10.1109/iCCECOME.2018.8658877
Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network
Sharif, M., Naeem, U., Islam, S. and Karami, A. 2018. Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_34
The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain
Okoye, Kingsley, Islam, S., Naeem, U., Sharif, M., Azam, Muhammad Awais and Karami, A. 2018. The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_96
Taskification – Gamification of Tasks
Naeem, U., Islam, S., Sharif, M., Sudakov, Sergey and Azam, Awais 2017. Taskification – Gamification of Tasks. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 631-634
SignalSense - Towards Quality Service
Islam, S., Sharif, M., Naeem, U. and Geehan, James 2017. SignalSense - Towards Quality Service. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 627-630
CrimeSafe - Helping you stay safe
Islam, S., Naeem, U., Sharif, M. and Dovnarovic, Arnold 2017. CrimeSafe - Helping you stay safe. in: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers Association for Computing Machinery (ACM). pp. 642-645
The Future of Enterprise Security with Regards to Mobile Technology and Applications
Tagoe, F. T. and Sharif, M. 2017. The Future of Enterprise Security with Regards to Mobile Technology and Applications. in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad (ed.) Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017 Proceedings Springer International Publishing.
Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles
Seuwou, Patrice, Banissi, Ebad, Ubakanma, George, Sharif, M. and Healey, Ann 2017. Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles. in: Jahankhani, Hamid, Carlile, Alex, Emm, David, Hosseinian-Far, Amin, Brown, Guy, Sexton, Graham and Jamal, Arshad (ed.) Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017 Proceedings Springer International Publishing.
An efficient system for preprocessing confocal corneal images for subsequent analysis
Sharif, M., Qahwaji, Rami, Hayajneh, Sofyan, Ipson, Stanley, Alzubaidi, Rania and Brahma, Arun 2014. An efficient system for preprocessing confocal corneal images for subsequent analysis. in: 2014 14th UK Workshop on Computational Intelligence (UKCI) IEEE.
Artificial Neural Network-Based System for PET Volume Segmentation
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2010. Artificial Neural Network-Based System for PET Volume Segmentation. International Journal of Biomedical Imaging. 2010 (105610). https://doi.org/10.1155/2010/105610
Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2012. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification. Advances in Fuzzy Systems. 2012 (327861). https://doi.org/10.1155/2012/327861
Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study
Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal 2013. Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study. British Journal of Applied Science & Technology. 4 (4), pp. 634-649. https://doi.org/10.9734/bjast/2014/5084
Preparation of 2D sequences of corneal images for 3D model building
Elbita, Abdulhakim, Qahwaji, Rami, Ipson, Stanley, Sharif, M. and Ghanchi, Faruque 2015. Preparation of 2D sequences of corneal images for 3D model building. Computer Methods and Programs in Biomedicine. 114 (2), pp. 194-205. https://doi.org/10.1016/j.cmpb.2014.01.009
Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images
Sharif, M., Qahwaji, R., Ipson, S. and Brahma, A. 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Applied Soft Computing. 36 (Nov.), pp. 269-282. https://doi.org/10.1016/j.asoc.2015.07.019
An efficient intelligent analysis system for confocal corneal endothelium images
Sharif, M., Qahwaji, R., Shahamatnia, E., Alzubaidi, R., Ipson, S. and Brahma, A. 2015. An efficient intelligent analysis system for confocal corneal endothelium images. Computer Methods and Programs in Biomedicine. 122 (3), pp. 421-436. https://doi.org/10.1016/j.cmpb.2015.09.003
In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study
Alzubaidi, Rania, Sharif, M., Qahwaji, Rami, Ipson, Stanley and Brahma, Arun 2015. In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study. British Journal of Ophthalmology. 100 (1), pp. 41-55. https://doi.org/10.1136/bjophthalmol-2015-306934
A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing
Pizzamiglio, S., Naeem, U., ur Réhman, Shafiq, Sharif, M., Abdalla, H. and Turner, D. 2017. A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing. Procedia Computer Science. 113, pp. 89-96. https://doi.org/10.1016/j.procs.2017.08.297