ARCHIVES
Research Article
Exploring Mobile Robot Navigation Protocols: A Detailed Summary
Abhinav Bhardwaj1
Meenakshi Arora2
1P.G. Student, Department of CSE, Sat Kabir Institute of Technology and Management, Haryana, India. 2Assistant Professor, Department of CSE, Sat Kabir Institute of Technology and Management, Haryana, India.
Published Online: May-August 2024
Pages: 202-206
Cite this article
No DOIReferences
1. K. A. A. Mustafa, “Towards continuous control for mobile robot navigation: A reinforcement learning and slam based approach.” University
of Twente, 2019.
2. X.-T. Truong and T. D. Ngo, “Toward socially aware robot navigation in dynamic and crowded environments: A proactive social motion
model,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 4, pp. 1743–1760, 2017.
3. R. S. Shokendra Dev Verma, Kirti Bhatia, Shalini Bhadola, “A Detailed Overview of Mobile NavigationProtocols,” Int. J. Innov. Res.
Comput. Commun. Eng., vol. 10, no. 6, pp. 5678–5684, 2022.
4. H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,” IEEE Robot. Autom. Mag., vol. 13, no. 2, pp. 99–110,
2006.
5. M. J. Mataric, “Behaviour-based control: Examples from navigation, learning, and group behaviour,” J. Exp. Theor. Artif. Intell., vol. 9,
no. 2–3, pp. 323–336, 1997.
6. A. Garulli, A. Giannitrapani, A. Rossi, and A. Vicino, “Mobile robot SLAM for line-based environment representation,” in Proceedings of
the 44th IEEE Conference on Decision and Control, 2005, pp. 2041–2046.
7. V. Nguyen, A. Harati, A. Martinelli, R. Siegwart, and N. Tomatis, “Orthogonal SLAM: a step toward lightweight indoor autonomous
navigation,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 5007–5012.
8. E. H. C. Harik and A. Korsaeth, “Combining hector slam and artificial potential field for autonomous navigation inside a greenhouse,”
Robotics, vol. 7, no. 2, p. 22, 2018.
9. G. Sepulveda, J. C. Niebles, and A. Soto, “A deep learning based behavioral approach to indoor autonomous navigation,” in 2018 IEEE
international conference on robotics and automation (ICRA), 2018, pp. 4646–4653.
10. Y.-H. Kim, J.-I. Jang, and S. Yun, “End-to-end deep learning for autonomous navigation of mobile robot,” in 2018 IEEE International
Conference on Consumer Electronics (ICCE), 2018, pp. 1–6.
11. J. K. Wang, X. Q. Ding, H. Xia, Y. Wang, L. Tang, and R. Xiong, “A LiDAR based end to end controller for robot navigation using deep
neural network,” in 2017 IEEE International Conference on Unmanned Systems (ICUS), 2017, pp. 614–619.
12. R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “ORB-SLAM: a versatile and accurate monocular SLAM system,” IEEE Trans. Robot.,
vol. 31, no. 5, pp. 1147–1163, 2015.
13. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to the simultaneous localization and mapping
problem,” Aaai/iaai, vol. 593598, 2002.
14. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
15. S. Gu, E. Holly, T. P. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation,” arXiv Prepr. arXiv1610.00633, vol.
1, p. 1, 2016.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process.
Syst., vol. 25, 2012.
17. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 35, no. 8, pp. 1798–1828, 2013.
18. C. Xiang et al., “Multi-sensor fusion and cooperative perception for autonomous driving: A review,” IEEE Intell. Transp. Syst. Mag., 2023.
19. S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv Prepr. arXiv1609.04747, 2016.
20. D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
21. B. Zoph et al., “Rethinking pre-training and self-training,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 3833–3845, 2020.
22. S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” J. Mach. Learn. Res., vol. 17, no. 39, pp.
1–40, 2016.
23. E. W. Dijkstra, “A note on two problems in connexion with graphs,” in Edsger Wybe Dijkstra: His Life, Work, and Legacy, 2022, pp. 287–
290.
24. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci.
Cybern., vol. 4, no. 2, pp. 100–107, 1968.
25. L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration
spaces,” IEEE Trans. Robot. Autom., vol. 12, no. 4, pp. 566–580, 1996.
26. S. M. LaValle and J. J. Kuffner, “Rapidly-exploring random trees: Progress and prospects: Steven m. lavalle, iowa state university, a james
j. kuffner, jr., university of tokyo, tokyo, japan,” Algorithmic Comput. Robot., pp. 303–307, 2001.
27. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Rob. Res., vol. 5, no. 1, pp. 90–98, 1986.
28. D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel, “Practical search techniques in path planning for autonomous driving,” Ann Arbor,
vol. 1001, no. 48105, pp. 18–80, 2008.
29. D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag., vol. 4, no. 1, pp.
23–33, 1997.
of Twente, 2019.
2. X.-T. Truong and T. D. Ngo, “Toward socially aware robot navigation in dynamic and crowded environments: A proactive social motion
model,” IEEE Trans. Autom. Sci. Eng., vol. 14, no. 4, pp. 1743–1760, 2017.
3. R. S. Shokendra Dev Verma, Kirti Bhatia, Shalini Bhadola, “A Detailed Overview of Mobile NavigationProtocols,” Int. J. Innov. Res.
Comput. Commun. Eng., vol. 10, no. 6, pp. 5678–5684, 2022.
4. H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,” IEEE Robot. Autom. Mag., vol. 13, no. 2, pp. 99–110,
2006.
5. M. J. Mataric, “Behaviour-based control: Examples from navigation, learning, and group behaviour,” J. Exp. Theor. Artif. Intell., vol. 9,
no. 2–3, pp. 323–336, 1997.
6. A. Garulli, A. Giannitrapani, A. Rossi, and A. Vicino, “Mobile robot SLAM for line-based environment representation,” in Proceedings of
the 44th IEEE Conference on Decision and Control, 2005, pp. 2041–2046.
7. V. Nguyen, A. Harati, A. Martinelli, R. Siegwart, and N. Tomatis, “Orthogonal SLAM: a step toward lightweight indoor autonomous
navigation,” in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 5007–5012.
8. E. H. C. Harik and A. Korsaeth, “Combining hector slam and artificial potential field for autonomous navigation inside a greenhouse,”
Robotics, vol. 7, no. 2, p. 22, 2018.
9. G. Sepulveda, J. C. Niebles, and A. Soto, “A deep learning based behavioral approach to indoor autonomous navigation,” in 2018 IEEE
international conference on robotics and automation (ICRA), 2018, pp. 4646–4653.
10. Y.-H. Kim, J.-I. Jang, and S. Yun, “End-to-end deep learning for autonomous navigation of mobile robot,” in 2018 IEEE International
Conference on Consumer Electronics (ICCE), 2018, pp. 1–6.
11. J. K. Wang, X. Q. Ding, H. Xia, Y. Wang, L. Tang, and R. Xiong, “A LiDAR based end to end controller for robot navigation using deep
neural network,” in 2017 IEEE International Conference on Unmanned Systems (ICUS), 2017, pp. 614–619.
12. R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “ORB-SLAM: a versatile and accurate monocular SLAM system,” IEEE Trans. Robot.,
vol. 31, no. 5, pp. 1147–1163, 2015.
13. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A factored solution to the simultaneous localization and mapping
problem,” Aaai/iaai, vol. 593598, 2002.
14. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
15. S. Gu, E. Holly, T. P. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation,” arXiv Prepr. arXiv1610.00633, vol.
1, p. 1, 2016.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process.
Syst., vol. 25, 2012.
17. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 35, no. 8, pp. 1798–1828, 2013.
18. C. Xiang et al., “Multi-sensor fusion and cooperative perception for autonomous driving: A review,” IEEE Intell. Transp. Syst. Mag., 2023.
19. S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv Prepr. arXiv1609.04747, 2016.
20. D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
21. B. Zoph et al., “Rethinking pre-training and self-training,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 3833–3845, 2020.
22. S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” J. Mach. Learn. Res., vol. 17, no. 39, pp.
1–40, 2016.
23. E. W. Dijkstra, “A note on two problems in connexion with graphs,” in Edsger Wybe Dijkstra: His Life, Work, and Legacy, 2022, pp. 287–
290.
24. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci.
Cybern., vol. 4, no. 2, pp. 100–107, 1968.
25. L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration
spaces,” IEEE Trans. Robot. Autom., vol. 12, no. 4, pp. 566–580, 1996.
26. S. M. LaValle and J. J. Kuffner, “Rapidly-exploring random trees: Progress and prospects: Steven m. lavalle, iowa state university, a james
j. kuffner, jr., university of tokyo, tokyo, japan,” Algorithmic Comput. Robot., pp. 303–307, 2001.
27. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Rob. Res., vol. 5, no. 1, pp. 90–98, 1986.
28. D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel, “Practical search techniques in path planning for autonomous driving,” Ann Arbor,
vol. 1001, no. 48105, pp. 18–80, 2008.
29. D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag., vol. 4, no. 1, pp.
23–33, 1997.
Related Articles
2024
Revolutionizing User Interfaces: Exploring the Latest Trends in Front-End Development
2024
Website Development in Computer Science: Unveiling the Digital World
2024
Review on RSA Cryptography, Steganography and Compression Techniques for Data Security
2024
Stock Price Prediction Using LSTM
2024
Comparative Analysis of Program Execution Time Required by Python, R and Julia Compiler
2024