In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings.
| Published in | Engineering Science (Volume 9, Issue 1) |
| DOI | 10.11648/j.es.20240901.13 |
| Page(s) | 12-20 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Transfer Function, PID Controller, Artificial Neural Network (ANN), Route Tracking, Localization, Over-Shoots, Performance Improvements
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APA Style
Chukwubueze, O. E., Ifeanyichukwu, E. I., Chigozie, E. P. (2024). Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance. Engineering Science, 9(1), 12-20. https://doi.org/10.11648/j.es.20240901.13
ACS Style
Chukwubueze, O. E.; Ifeanyichukwu, E. I.; Chigozie, E. P. Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance. Eng. Sci. 2024, 9(1), 12-20. doi: 10.11648/j.es.20240901.13
AMA Style
Chukwubueze OE, Ifeanyichukwu EI, Chigozie EP. Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance. Eng Sci. 2024;9(1):12-20. doi: 10.11648/j.es.20240901.13
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Download@article{10.11648/j.es.20240901.13,
author = {Obasi Emmanuel Chukwubueze and Eneh Innocent Ifeanyichukwu and Ene Princewill Chigozie},
title = {Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance},
journal = {Engineering Science},
volume = {9},
number = {1},
pages = {12-20},
doi = {10.11648/j.es.20240901.13},
url = {https://doi.org/10.11648/j.es.20240901.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.es.20240901.13},
abstract = {In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings.
},
year = {2024}
}
TY - JOUR T1 - Improving the Control of Autonomous Navigation of a Robot with Artificial Neural Network for Optimum Performance AU - Obasi Emmanuel Chukwubueze AU - Eneh Innocent Ifeanyichukwu AU - Ene Princewill Chigozie Y1 - 2024/01/08 PY - 2024 N1 - https://doi.org/10.11648/j.es.20240901.13 DO - 10.11648/j.es.20240901.13 T2 - Engineering Science JF - Engineering Science JO - Engineering Science SP - 12 EP - 20 PB - Science Publishing Group SN - 2578-9279 UR - https://doi.org/10.11648/j.es.20240901.13 AB - In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings. VL - 9 IS - 1 ER -
Electrical and Electronic Engineering Department, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria
Electrical and Electronic Engineering Department, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria
Electrical and Electronic Engineering Department, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria
