Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots

In the dynamic realm of Autonomous Mobile Robots (AMRs), ensuring smooth navigation among obstacles is critical, especially as they become increasingly integral to industries such as manufacturing and transportation. Recent advances have introduced several learning models to aid in obstacle avoidanc...

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Autores:
Thakur, Abhishek
Das, Subhranil
Mishra, Sudhansu Kumar
Swain, Subrat Kumar
Tipo de recurso:
Article of journal
Fecha de publicación:
2025
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/14189
Acceso en línea:
https://doi.org/10.32397/tesea.vol6.n2.602
Palabra clave:
Autonomous Mobile Robot
Least Angle Regression
Adaptive Stochastic Gradient Descent
Machine Learning
Obstacle Avoidance
Path Planning
Rights
openAccess
License
Abhishek Thakur, Subhranil Das, Sudhansu Kumar Mishra, Subrat Kumar Swain - 2025
id UTB2_7f21e4b5d676bfd4063b1b638a861c6f
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/14189
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
dc.title.translated.spa.fl_str_mv Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
title Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
spellingShingle Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
Autonomous Mobile Robot
Least Angle Regression
Adaptive Stochastic Gradient Descent
Machine Learning
Obstacle Avoidance
Path Planning
title_short Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
title_full Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
title_fullStr Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
title_full_unstemmed Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
title_sort Adaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robots
dc.creator.fl_str_mv Thakur, Abhishek
Das, Subhranil
Mishra, Sudhansu Kumar
Swain, Subrat Kumar
dc.contributor.author.eng.fl_str_mv Thakur, Abhishek
Das, Subhranil
Mishra, Sudhansu Kumar
Swain, Subrat Kumar
dc.subject.eng.fl_str_mv Autonomous Mobile Robot
Least Angle Regression
Adaptive Stochastic Gradient Descent
Machine Learning
Obstacle Avoidance
Path Planning
topic Autonomous Mobile Robot
Least Angle Regression
Adaptive Stochastic Gradient Descent
Machine Learning
Obstacle Avoidance
Path Planning
description In the dynamic realm of Autonomous Mobile Robots (AMRs), ensuring smooth navigation among obstacles is critical, especially as they become increasingly integral to industries such as manufacturing and transportation. Recent advances have introduced several learning models to aid in obstacle avoidance, but many face computational challenges. This research introduces the Adaptive Stochastic Gradient Descent with Least Angle Regression (ASGD-LARS) algorithm, specifically designed to enhance the navigation of AMRs. By carefully considering obstacle orientations, it facilitates quicker decision-making for direction changes. When compared with well-established algorithms like KNN, XG Boost, Naive Bayes, and Logistic Regression, ASGD-LARS consistently performs better in terms of accuracy, computational efficiency, and reliability. This study lays the foundation for the deployment of smarter and more efficient AMRs across diverse industries.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-09-15 00:00:00
dc.date.available.none.fl_str_mv 2025-09-15 00:00:00
dc.date.issued.none.fl_str_mv 2025-09-15
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv Journal article
dc.type.content.eng.fl_str_mv Text
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dc.identifier.url.none.fl_str_mv https://doi.org/10.32397/tesea.vol6.n2.602
dc.identifier.doi.none.fl_str_mv 10.32397/tesea.vol6.n2.602
dc.identifier.eissn.none.fl_str_mv 2745-0120
url https://doi.org/10.32397/tesea.vol6.n2.602
identifier_str_mv 10.32397/tesea.vol6.n2.602
2745-0120
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv Yudie Hu, Yuqi Wang, Kaixiong Hu, and Weidong Li. Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing. Journal of Intelligent Manufacturing, 34(2):789–807, August 2021. [2] Francisco J. Perez-Grau, J. Ramiro Martinez-de Dios, Julio L. Paneque, J. Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R. Astorga, and Anibal Ollero. Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems, 60:312–324, July 2021. [3] Mingzhang Pan, Jing Li, Xiuze Yang, Shuo Wang, Lei Pan, Tiecheng Su, Yuke Wang, Qiye Yang, and Ke Liang. Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots. Computers amp; Industrial Engineering, 169:108275, July 2022. [4] Priyadarshi Biplab Kumar and Dayal R. Parhi. Intelligent hybridization of regression technique with genetic algorithm for navigation of humanoids in complex environments. Robotica, 38(4):565–581, June 2019. [5] Mohd. Nayab Zafar, J. C. Mohanta, and Anupam Keshari. Gwo-potential field method for mobile robot path planning and navigation control. Arabian Journal for Science and Engineering, 46(8):8087–8104, June 2021. [6] Volkan Sezer. An optimized path tracking approach considering obstacle avoidance and comfort. Journal of Intelligent amp; Robotic Systems, 105(1), May 2022. [7] Subhranil Das and Sudhansu Kumar Mishra. A machine learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments. Computers and Electrical Engineering, 103:108376, October 2022. [8] Mary B. Alatise and Gerhard P. Hancke. A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8:39830–39846, 2020. [9] Abhishek Kumar Kashyap and Dayal R. Parhi. Implementation of intelligent navigational techniques for inter-collision avoidance of multiple humanoid robots in complex environment. Applied Soft Computing, 124:109001, July 2022. [10] Chaochao Chen and Paul Richardson. Mobile robot obstacle avoidance using short memory: a dynamic recurrent neuro-fuzzy approach. Transactions of the Institute of Measurement and Control, 34(2–3):148–164, July 2010. [11] José Ricardo Sánchez-Ibáñez, Carlos J. Pérez-del Pulgar, and Alfonso García-Cerezo. Path planning for autonomous mobile robots: A review. Sensors, 21(23):7898, November 2021. [12] Zafer Duraklı and Vasif Nabiyev. A new approach based on bezier curves to solve path planning problems for mobile robots. Journal of Computational Science, 58:101540, February 2022. [13] Gongfeng Xin, Lei Shi, Guanxu Long, Weigang Pan, Yiming Li, and Jicun Xu. Mobile robot path planning with reformative bat algorithm. PLOS ONE, 17(11):e0276577, November 2022. [14] Yaonan Dai, Jiuyang Yu, Cong Zhang, Bowen Zhan, and Xiaotao Zheng. A novel whale optimization algorithm of path planning strategy for mobile robots. Applied Intelligence, 53(9):10843–10857, August 2022. [15] Abhishek Kumar Kashyap and Dayal R. Parhi. Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces. Industrial Robot: the international journal of robotics research and application, 49(2):280–288, September 2021. [16] P.B. Fernandes, R.C.L. Oliveira, and J.V. Fonseca Neto. Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity. Applied Soft Computing, 116:108108, February 2022. [17] Zhang Qing, LIU Xu, PENG Li, and Zhu Fengzeng. Path planning for mobile robots based on jps and improved a* algorithm. Journal of Frontiers of Computer Science & Technology, 15(11):2233, 2021. [18] Zhihai Liu, Hanbin Liu, Zhenguo Lu, and Qingliang Zeng. A dynamic fusion pathfinding algorithm using delaunay triangulation and improved a-star for mobile robots. IEEE Access, 9:20602–20621, 2021. [19] Hongwei Tang, Wei Sun, Anping Lin, Min Xue, and Xing Zhang. A gwo-based multi-robot cooperation method for target searching in unknown environments. Expert Systems with Applications, 186:115795, December 2021. [20] Jian Chen, Chengshuai Wu, Guoqing Yu, Deepak Narang, and Yuexuan Wang. Path following of wheeled mobile robots using online-optimization-based guidance vector field. IEEE/ASME Transactions on Mechatronics, 26(4):1737–1744, August 2021. [21] Divyendu Kumar Mishra, Aby Thomas, Jinsa Kuruvilla, P. Kalyanasundaram, K. Ramalingeswara Prasad, and Anandakumar Haldorai. Design of mobile robot navigation controller using neuro-fuzzy logic system. Computers and Electrical Engineering, 101:108044, July 2022. [22] Yao Rong, Chao Han, Christian Hellert, Antje Loyal, and Enkelejda Kasneci. Artificial intelligence methods in in-cabin use cases: A survey. IEEE Intelligent Transportation Systems Magazine, 14(3):132–145, May 2022. [23] Jing Xu, Karen Kendrick, and Alex R. Bowers. Clinical report: Experiences of a driver with vision impairment when using a tesla car. Optometry and Vision Science, 99(4):417–421, February 2022. [24] Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani. Least angle regression. The Annals of Statistics, 32(2), April 2004. [25] Seyed Matin Malakouti. Estimating the output power and wind speed with ml methods: A case study in texas. Case Studies in Chemical and Environmental Engineering, 7:100324, June 2023.
dc.relation.ispartofjournal.eng.fl_str_mv Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv 6
dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationendpage.none.fl_str_mv 26
dc.relation.bitstream.none.fl_str_mv https://revistas.utb.edu.co/tesea/article/download/602/455
dc.relation.citationedition.eng.fl_str_mv Núm. 2 , Año 2025 : (In progress) Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv 2
dc.rights.eng.fl_str_mv Abhishek Thakur, Subhranil Das, Sudhansu Kumar Mishra, Subrat Kumar Swain - 2025
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Abhishek Thakur, Subhranil Das, Sudhansu Kumar Mishra, Subrat Kumar Swain - 2025
https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv https://revistas.utb.edu.co/tesea/article/view/602
institution Universidad Tecnológica de Bolívar
repository.name.fl_str_mv Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv bdigital@metabiblioteca.com
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spelling Thakur, AbhishekDas, SubhranilMishra, Sudhansu KumarSwain, Subrat Kumar2025-09-15 00:00:002025-09-15 00:00:002025-09-15In the dynamic realm of Autonomous Mobile Robots (AMRs), ensuring smooth navigation among obstacles is critical, especially as they become increasingly integral to industries such as manufacturing and transportation. Recent advances have introduced several learning models to aid in obstacle avoidance, but many face computational challenges. This research introduces the Adaptive Stochastic Gradient Descent with Least Angle Regression (ASGD-LARS) algorithm, specifically designed to enhance the navigation of AMRs. By carefully considering obstacle orientations, it facilitates quicker decision-making for direction changes. When compared with well-established algorithms like KNN, XG Boost, Naive Bayes, and Logistic Regression, ASGD-LARS consistently performs better in terms of accuracy, computational efficiency, and reliability. This study lays the foundation for the deployment of smarter and more efficient AMRs across diverse industries.application/pdfengUniversidad Tecnológica de BolívarAbhishek Thakur, Subhranil Das, Sudhansu Kumar Mishra, Subrat Kumar Swain - 2025https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/602Autonomous Mobile RobotLeast Angle RegressionAdaptive Stochastic Gradient DescentMachine LearningObstacle AvoidancePath PlanningAdaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robotsAdaptive stochastic gradient descent with least angle regression enhanced navigation: intelligent path planning in cluttered environments for autonomous robotsArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol6.n2.60210.32397/tesea.vol6.n2.6022745-0120Yudie Hu, Yuqi Wang, Kaixiong Hu, and Weidong Li. Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing. Journal of Intelligent Manufacturing, 34(2):789–807, August 2021. [2] Francisco J. Perez-Grau, J. Ramiro Martinez-de Dios, Julio L. Paneque, J. Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R. Astorga, and Anibal Ollero. Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems, 60:312–324, July 2021. [3] Mingzhang Pan, Jing Li, Xiuze Yang, Shuo Wang, Lei Pan, Tiecheng Su, Yuke Wang, Qiye Yang, and Ke Liang. Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots. Computers amp; Industrial Engineering, 169:108275, July 2022. [4] Priyadarshi Biplab Kumar and Dayal R. Parhi. Intelligent hybridization of regression technique with genetic algorithm for navigation of humanoids in complex environments. Robotica, 38(4):565–581, June 2019. [5] Mohd. Nayab Zafar, J. C. Mohanta, and Anupam Keshari. Gwo-potential field method for mobile robot path planning and navigation control. Arabian Journal for Science and Engineering, 46(8):8087–8104, June 2021. [6] Volkan Sezer. An optimized path tracking approach considering obstacle avoidance and comfort. Journal of Intelligent amp; Robotic Systems, 105(1), May 2022. [7] Subhranil Das and Sudhansu Kumar Mishra. A machine learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments. Computers and Electrical Engineering, 103:108376, October 2022. [8] Mary B. Alatise and Gerhard P. Hancke. A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8:39830–39846, 2020. [9] Abhishek Kumar Kashyap and Dayal R. Parhi. Implementation of intelligent navigational techniques for inter-collision avoidance of multiple humanoid robots in complex environment. Applied Soft Computing, 124:109001, July 2022. [10] Chaochao Chen and Paul Richardson. Mobile robot obstacle avoidance using short memory: a dynamic recurrent neuro-fuzzy approach. Transactions of the Institute of Measurement and Control, 34(2–3):148–164, July 2010. [11] José Ricardo Sánchez-Ibáñez, Carlos J. Pérez-del Pulgar, and Alfonso García-Cerezo. Path planning for autonomous mobile robots: A review. Sensors, 21(23):7898, November 2021. [12] Zafer Duraklı and Vasif Nabiyev. A new approach based on bezier curves to solve path planning problems for mobile robots. Journal of Computational Science, 58:101540, February 2022. [13] Gongfeng Xin, Lei Shi, Guanxu Long, Weigang Pan, Yiming Li, and Jicun Xu. Mobile robot path planning with reformative bat algorithm. PLOS ONE, 17(11):e0276577, November 2022. [14] Yaonan Dai, Jiuyang Yu, Cong Zhang, Bowen Zhan, and Xiaotao Zheng. A novel whale optimization algorithm of path planning strategy for mobile robots. Applied Intelligence, 53(9):10843–10857, August 2022. [15] Abhishek Kumar Kashyap and Dayal R. Parhi. Obstacle avoidance and path planning of humanoid robot using fuzzy logic controller aided owl search algorithm in complicated workspaces. Industrial Robot: the international journal of robotics research and application, 49(2):280–288, September 2021. [16] P.B. Fernandes, R.C.L. Oliveira, and J.V. Fonseca Neto. Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity. Applied Soft Computing, 116:108108, February 2022. [17] Zhang Qing, LIU Xu, PENG Li, and Zhu Fengzeng. Path planning for mobile robots based on jps and improved a* algorithm. Journal of Frontiers of Computer Science & Technology, 15(11):2233, 2021. [18] Zhihai Liu, Hanbin Liu, Zhenguo Lu, and Qingliang Zeng. A dynamic fusion pathfinding algorithm using delaunay triangulation and improved a-star for mobile robots. IEEE Access, 9:20602–20621, 2021. [19] Hongwei Tang, Wei Sun, Anping Lin, Min Xue, and Xing Zhang. A gwo-based multi-robot cooperation method for target searching in unknown environments. Expert Systems with Applications, 186:115795, December 2021. [20] Jian Chen, Chengshuai Wu, Guoqing Yu, Deepak Narang, and Yuexuan Wang. Path following of wheeled mobile robots using online-optimization-based guidance vector field. IEEE/ASME Transactions on Mechatronics, 26(4):1737–1744, August 2021. [21] Divyendu Kumar Mishra, Aby Thomas, Jinsa Kuruvilla, P. Kalyanasundaram, K. Ramalingeswara Prasad, and Anandakumar Haldorai. Design of mobile robot navigation controller using neuro-fuzzy logic system. Computers and Electrical Engineering, 101:108044, July 2022. [22] Yao Rong, Chao Han, Christian Hellert, Antje Loyal, and Enkelejda Kasneci. Artificial intelligence methods in in-cabin use cases: A survey. IEEE Intelligent Transportation Systems Magazine, 14(3):132–145, May 2022. [23] Jing Xu, Karen Kendrick, and Alex R. Bowers. Clinical report: Experiences of a driver with vision impairment when using a tesla car. Optometry and Vision Science, 99(4):417–421, February 2022. [24] Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani. Least angle regression. The Annals of Statistics, 32(2), April 2004. [25] Seyed Matin Malakouti. Estimating the output power and wind speed with ml methods: A case study in texas. Case Studies in Chemical and Environmental Engineering, 7:100324, June 2023.Transactions on Energy Systems and Engineering Applications6126https://revistas.utb.edu.co/tesea/article/download/602/455Núm. 2 , Año 2025 : (In progress) Transactions on Energy Systems and Engineering Applications220.500.12585/14189oai:repositorio.utb.edu.co:20.500.12585/141892025-11-06 09:15:11.61https://creativecommons.org/licenses/by/4.0Abhishek Thakur, Subhranil Das, Sudhansu Kumar Mishra, Subrat Kumar Swain - 2025metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

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