Titulo:
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior
.
Sumario:
Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) duri... Ver más
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Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior Navegación autónoma reactiva de un robot móvil basada en aprendizaje profundo y comportamientos difusos Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) during testing tracks, attributed to the Raspberry Pi's limited computational capacity. Conclusions: By combining Deep Learning and fuzzy behaviors, the study demonstrated the effectiveness of the control architecture in enhancing the robot's autonomous navigation capabilities. The integration of pre-trained models and fuzzy logic provided adaptability and responsiveness to dynamic traffic scenarios. Future research could focus on optimizing system parameters and exploring applications in more complex environments to further advance autonomous robotics and artificial intelligence technologies. Objetivo: este estudio tuvo como objetivo desarrollar una arquitectura de control para la navegación autónoma reactiva de un robot móvil mediante la integración de técnicas de Deep Learning y comportamientos difusos basados en el reconocimiento de señales de tráfico. Materiales: la investigación utilizó transfer learning con la red Inception V3 como base para entrenar una red neuronal en la identificación de señales de tráfico. Los experimentos se llevaron a cabo utilizando un Donkey-Car, un robot móvil de código abierto tipo Ackermann, con limitaciones computacionales inherentes. Resultados: la implementación de la técnica de transfer learning arrojó un resultado satisfactorio, logrando una alta precisión del 96.2% en la identificación de señales de tráfico. No obstante, se encontraron desafíos debido a retrasos en los cuadros por segundo (FPS) durante las pruebas, atribuidos a la capacidad computacional limitada de la Raspberry Pi. Conclusiones: al combinar Deep Learning y comportamientos difusos, el estudio demostró la efectividad de la arquitectura de control en mejorar las capacidades de navegación autónoma del robot. La integración de modelos pre-entrenados y lógica difusa proporcionó adaptabilidad y capacidad de respuesta a escenarios de tráfico dinámicos. Investigaciones futuras podrían centrarse en optimizar los parámetros del sistema y explorar aplicaciones en entornos más complejos para avanzar aún más en las tecnologías de robótica autónoma e inteligencia artificial. López-Velásquez, Julián Acosta-Amaya, Gustavo Alonso Jimenez-Builes, Jovani Alberto navegación autónoma aprendizaje profundo comportamientos difusos arquitectura de control redes neuronales inteligencia artificial Autonomous Navigation Deep Learning Fuzzy Behaviors Control Architecture Neural Networks Artificial Intelligence 21 42 Núm. 42 , Año 2024 : Tabla de contenido Revista EIA No. 42 Artículo de revista Journal article 2024-07-01 00:00:00 2024-07-01 00:00:00 2024-07-01 application/pdf Fondo Editorial EIA - Universidad EIA Revista EIA 1794-1237 2463-0950 https://revistas.eia.edu.co/index.php/reveia/article/view/1764 10.24050/reia.v21i42.1764 https://doi.org/10.24050/reia.v21i42.1764 spa https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2024 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. 4229 pp. 1 14 Afif, M., Ayachi, R., Said, Y., Pissaloux, E., & Atri, M. (2020). Indoor image recognition and classification via deep convolutional neural network. In Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), vol. 1, pp. 364-371. Cham, Switzerland: Springer International Publishing. Bachute, M., & Subhedar, J. (2021). Autonomous driving architectures: Insights of machine learning and deep learning algorithms. Machine Learning with Applications, vol. 6, 100164. Available at: https://doi.org/10.1016/j.mlwa.2021.100164. Bengio, Y. (2016). Machines who learn. Scientific American Magazine, vol. 314(6), pp. 46–51. Available at: https://doi.org/10.1038/scientificamerican0616-46. Bjelonic, M. (2024). Yolo v2 for ROS: Real-time object detection for ROS. Available online: https://github.com/leggedrobotics/darknet_ros/tree/feature/ros_separation (accessed on 17 May 2024). Blacklock, P. (1986). Standards for programming practices: An alvey project investigates quality certification. Data Processing, vol. 28(10), pp. 522–528. Available at: https://doi.org/10.1016/0011-684X(86)90069-9. Dahirou, Z., & Zheng, M. (2021). Motion detection and object detection: Yolo (You Only Look Once). In 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 250-257. New York, USA: IEEE. DonkeyCar. (2024). How to build a Donkey. Available online: http://docs.donkeycar.com/guide/build_hardware/ (accessed on 17 May 2024). Itsuka, T., Song, M., & Kawamura, A. (2022). Development of ROS2-TMS: New software platform for informationally structured environment. Robomech J., vol. 9(1). Available at: https://doi.org/10.1186/s40648-021-00216-2. Kahraman, C., Deveci, M., Boltürk, E., & Türk, S. (2020). Fuzzy controlled humanoid robots: A literature review. Robotics and Autonomous Systems, vol. 134, p. 103643. Available at: https://doi.org/10.1016/j.robot.2020.103643. Lighthill, J. (1973). Artificial intelligence: A general survey. The Lighthill Report. Available at: http://dx.doi.org/10.1016/0004-3702(74)90016-2. Lin, H., Han, Y., Cai, W., & Jin, B. (2022). Traffic signal optimization based on fuzzy control and differential evolution algorithm. IEEE Transactions on Intelligent Transportation Systems, vol. 1(4). Available at: https://doi.org/10.59890/ijetr.v1i4.1138. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, vol. 27(4), p. 12. Available at: https://doi.org/10.1609/aimag.v27i4.1904. Mengoli, D., Tazzari, R., & Marconi, L. (2020). Autonomous robotic platform for precision orchard management: Architecture and software perspective. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor, pp. 303-308. New York, USA: IEEE. Newell, A., Simon, H. A., & Shaw, J. C. (1958). Report on a general problem-solving program. Pittsburgh, Pennsylvania: Carnegie Institute of Technology, pp. 1-27. Available at: http://dx.doi.org/10.1016/0004-3702(74)90016-2. OTL. (2024). ROS inception v3. GitHub, Inc. Available online: https://github.com/OTL/rostensorflow (accessed on 17 May 2024). Qian, J., Zhang, L., Huang, Q., Liu, X., Xing, X., & Li, X. (2024). A self-driving solution for resource-constrained autonomous vehicles in parked areas. High-Confidence Computing, vol. 4(1), 100182. Available at: https://doi.org/10.1016/j.hcc.2023.100182. Redmon, J., Santosh, D., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788. Available at: https://doi.org/10.1109/CVPR.2016.91. ROS. (2024). Ros rqt_graph. Open Robotics. Available online: http://wiki.ros.org/rqt_graph (accessed on 17 May 2024). Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, vol. 10(07), pp. 3897-3904. Available at: https://doi.org/10.4028/www.scientific.net/JERA.24.124. Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, vol. 3, pp. 54-70. Available at: https://doi.org/10.1016/j.cogr.2023.04.001. Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, vol. 32, pp. 323-332. Available at: https://doi.org/10.1016/j.neunet.2012.02.016. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2818–2826. Available at: https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.308. Transfer learning. (2024). Transfer learning. Available online: https://paperswithcode.com/task/transfer-learning (accessed on 17 May 2024). Treleaven, P., & Lima, I. (1982). Japan’s fifth generation computer systems. Computer, vol. 15(08), pp. 79–88. Available at: https://doi.org/10.1109/MC.1982.1654113. Vinolia, A., Kanya, N., & Rajavarman, V. N. (2023). Machine learning and deep learning based intrusion detection in cloud environment: A review. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, pp. 952-960. Available at: 10.1109/ICSSIT55814.2023.10060868. https://revistas.eia.edu.co/index.php/reveia/article/download/1764/1628 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 http://purl.org/redcol/resource_type/ART info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD EIA |
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https://nuevo.metarevistas.org/UNIVERSIDADEIA/logo.png |
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Colombia |
| collection |
Revista EIA |
| title |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| spellingShingle |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior López-Velásquez, Julián Acosta-Amaya, Gustavo Alonso Jimenez-Builes, Jovani Alberto navegación autónoma aprendizaje profundo comportamientos difusos arquitectura de control redes neuronales inteligencia artificial Autonomous Navigation Deep Learning Fuzzy Behaviors Control Architecture Neural Networks Artificial Intelligence |
| title_short |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| title_full |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| title_fullStr |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| title_full_unstemmed |
Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| title_sort |
enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior |
| title_eng |
Navegación autónoma reactiva de un robot móvil basada en aprendizaje profundo y comportamientos difusos |
| description |
Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) during testing tracks, attributed to the Raspberry Pi's limited computational capacity. Conclusions: By combining Deep Learning and fuzzy behaviors, the study demonstrated the effectiveness of the control architecture in enhancing the robot's autonomous navigation capabilities. The integration of pre-trained models and fuzzy logic provided adaptability and responsiveness to dynamic traffic scenarios. Future research could focus on optimizing system parameters and exploring applications in more complex environments to further advance autonomous robotics and artificial intelligence technologies.
|
| description_eng |
Objetivo: este estudio tuvo como objetivo desarrollar una arquitectura de control para la navegación autónoma reactiva de un robot móvil mediante la integración de técnicas de Deep Learning y comportamientos difusos basados en el reconocimiento de señales de tráfico. Materiales: la investigación utilizó transfer learning con la red Inception V3 como base para entrenar una red neuronal en la identificación de señales de tráfico. Los experimentos se llevaron a cabo utilizando un Donkey-Car, un robot móvil de código abierto tipo Ackermann, con limitaciones computacionales inherentes. Resultados: la implementación de la técnica de transfer learning arrojó un resultado satisfactorio, logrando una alta precisión del 96.2% en la identificación de señales de tráfico. No obstante, se encontraron desafíos debido a retrasos en los cuadros por segundo (FPS) durante las pruebas, atribuidos a la capacidad computacional limitada de la Raspberry Pi. Conclusiones: al combinar Deep Learning y comportamientos difusos, el estudio demostró la efectividad de la arquitectura de control en mejorar las capacidades de navegación autónoma del robot. La integración de modelos pre-entrenados y lógica difusa proporcionó adaptabilidad y capacidad de respuesta a escenarios de tráfico dinámicos. Investigaciones futuras podrían centrarse en optimizar los parámetros del sistema y explorar aplicaciones en entornos más complejos para avanzar aún más en las tecnologías de robótica autónoma e inteligencia artificial.
|
| author |
López-Velásquez, Julián Acosta-Amaya, Gustavo Alonso Jimenez-Builes, Jovani Alberto |
| author_facet |
López-Velásquez, Julián Acosta-Amaya, Gustavo Alonso Jimenez-Builes, Jovani Alberto |
| topic |
navegación autónoma aprendizaje profundo comportamientos difusos arquitectura de control redes neuronales inteligencia artificial Autonomous Navigation Deep Learning Fuzzy Behaviors Control Architecture Neural Networks Artificial Intelligence |
| topic_facet |
navegación autónoma aprendizaje profundo comportamientos difusos arquitectura de control redes neuronales inteligencia artificial Autonomous Navigation Deep Learning Fuzzy Behaviors Control Architecture Neural Networks Artificial Intelligence |
| topicspa_str_mv |
Autonomous Navigation Deep Learning Fuzzy Behaviors Control Architecture Neural Networks Artificial Intelligence |
| citationvolume |
21 |
| citationissue |
42 |
| citationedition |
Núm. 42 , Año 2024 : Tabla de contenido Revista EIA No. 42 |
| publisher |
Fondo Editorial EIA - Universidad EIA |
| ispartofjournal |
Revista EIA |
| source |
https://revistas.eia.edu.co/index.php/reveia/article/view/1764 |
| language |
spa |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2024 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references |
Afif, M., Ayachi, R., Said, Y., Pissaloux, E., & Atri, M. (2020). Indoor image recognition and classification via deep convolutional neural network. In Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), vol. 1, pp. 364-371. Cham, Switzerland: Springer International Publishing. Bachute, M., & Subhedar, J. (2021). Autonomous driving architectures: Insights of machine learning and deep learning algorithms. Machine Learning with Applications, vol. 6, 100164. Available at: https://doi.org/10.1016/j.mlwa.2021.100164. Bengio, Y. (2016). Machines who learn. Scientific American Magazine, vol. 314(6), pp. 46–51. Available at: https://doi.org/10.1038/scientificamerican0616-46. Bjelonic, M. (2024). Yolo v2 for ROS: Real-time object detection for ROS. Available online: https://github.com/leggedrobotics/darknet_ros/tree/feature/ros_separation (accessed on 17 May 2024). Blacklock, P. (1986). Standards for programming practices: An alvey project investigates quality certification. Data Processing, vol. 28(10), pp. 522–528. Available at: https://doi.org/10.1016/0011-684X(86)90069-9. Dahirou, Z., & Zheng, M. (2021). Motion detection and object detection: Yolo (You Only Look Once). In 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 250-257. New York, USA: IEEE. DonkeyCar. (2024). How to build a Donkey. Available online: http://docs.donkeycar.com/guide/build_hardware/ (accessed on 17 May 2024). Itsuka, T., Song, M., & Kawamura, A. (2022). Development of ROS2-TMS: New software platform for informationally structured environment. Robomech J., vol. 9(1). Available at: https://doi.org/10.1186/s40648-021-00216-2. Kahraman, C., Deveci, M., Boltürk, E., & Türk, S. (2020). Fuzzy controlled humanoid robots: A literature review. Robotics and Autonomous Systems, vol. 134, p. 103643. Available at: https://doi.org/10.1016/j.robot.2020.103643. Lighthill, J. (1973). Artificial intelligence: A general survey. The Lighthill Report. Available at: http://dx.doi.org/10.1016/0004-3702(74)90016-2. Lin, H., Han, Y., Cai, W., & Jin, B. (2022). Traffic signal optimization based on fuzzy control and differential evolution algorithm. IEEE Transactions on Intelligent Transportation Systems, vol. 1(4). Available at: https://doi.org/10.59890/ijetr.v1i4.1138. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, vol. 27(4), p. 12. Available at: https://doi.org/10.1609/aimag.v27i4.1904. Mengoli, D., Tazzari, R., & Marconi, L. (2020). Autonomous robotic platform for precision orchard management: Architecture and software perspective. In 2020 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor, pp. 303-308. New York, USA: IEEE. Newell, A., Simon, H. A., & Shaw, J. C. (1958). Report on a general problem-solving program. Pittsburgh, Pennsylvania: Carnegie Institute of Technology, pp. 1-27. Available at: http://dx.doi.org/10.1016/0004-3702(74)90016-2. OTL. (2024). ROS inception v3. GitHub, Inc. Available online: https://github.com/OTL/rostensorflow (accessed on 17 May 2024). Qian, J., Zhang, L., Huang, Q., Liu, X., Xing, X., & Li, X. (2024). A self-driving solution for resource-constrained autonomous vehicles in parked areas. High-Confidence Computing, vol. 4(1), 100182. Available at: https://doi.org/10.1016/j.hcc.2023.100182. Redmon, J., Santosh, D., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788. Available at: https://doi.org/10.1109/CVPR.2016.91. ROS. (2024). Ros rqt_graph. Open Robotics. Available online: http://wiki.ros.org/rqt_graph (accessed on 17 May 2024). Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, vol. 10(07), pp. 3897-3904. Available at: https://doi.org/10.4028/www.scientific.net/JERA.24.124. Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, vol. 3, pp. 54-70. Available at: https://doi.org/10.1016/j.cogr.2023.04.001. Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, vol. 32, pp. 323-332. Available at: https://doi.org/10.1016/j.neunet.2012.02.016. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2818–2826. Available at: https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.308. Transfer learning. (2024). Transfer learning. Available online: https://paperswithcode.com/task/transfer-learning (accessed on 17 May 2024). Treleaven, P., & Lima, I. (1982). Japan’s fifth generation computer systems. Computer, vol. 15(08), pp. 79–88. Available at: https://doi.org/10.1109/MC.1982.1654113. Vinolia, A., Kanya, N., & Rajavarman, V. N. (2023). Machine learning and deep learning based intrusion detection in cloud environment: A review. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, pp. 952-960. Available at: 10.1109/ICSSIT55814.2023.10060868. |
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