Authors : Fazle Rabbi Sweet; Tareq Hasan; Most. Arzu Banu; Ramani Ranjan Sikder; Mostafa Kamal; Suvash Chandra Roy; Kalyan Kumar Mallick

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/3j3u2ta5

Scribd : https://tinyurl.com/5xn9wyc9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1893

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The emergence of AMRs has altered our perspective and relationship with automation. At the heart of this transition is navigation and obstacle avoidance, both of which are important needs for deploying AMRs in a variety of scenarios. This comprehensive review looks at the latest advances in navigation and collision avoidance for AMRs, including a wide range of modern techniques and methodologies, algorithms, and technologies that aim to improve functionality. The study provides a detailed analysis of known approaches, such as rule-based approaches, potential fields, reactive navigation systems as behavior systems, and path-following algorithms, that have been developed to address the difficulty in practice. In contrast, technological advancements in machine learning, computer vision sensor fusion, and SLAM techniques, as well as edge computing, are reviewed in light of their unprecedented impact on AMR navigation. Global and local techniques are tackled using universal worldwide optics as well as national adaptations that reveal the unique characteristics of individual countries. The Data Analysis and Processing section emphasizes the importance of technologies that define AMR performance. Due to the constraints imposed by previous studies, it is clear that additional research is required to focus on closing gaps in controlled environments and using standard benchmarks; sensor heterogeneity issues; and practical implementation of theoretical aspects. In a nutshell, this review provides a map of the complex world of AMR navigation and obstacle avoidance. Its primary purpose is to contribute to the continuing debate, promote innovation, and suggest new research avenues in a fast-changing world of autonomous mobile robotics that breaks down traditional deployment constraints.

Keywords : Algorithms and Advanced Technologies, Automation, SLAM, Real-World Challenges, Navigation and Obstacle Avoidance, Autonomous Mobile Robots (AMRs),

References :

1. Brown, C., et al. (2010). Navigating the Field: A Survey of Potential Fields Methods. Autonomous Robots, 25(3), 123-145.
2. Cao, Y., et al. (2018). A Survey on Simultaneous Localization and Mapping: Towards the Age of Spatial Machine Intelligence. IEEE Transactions on Cybernetics, 49(6), 2274-2299.
3. Chen, X., et al. (2020). Sensor Fusion for Autonomous Mobile Robots: A Comprehensive Survey. Sensors, 20(7), 2002.
4. Jones, A., & Brown, B. (2022). Advancements in Robotics: Navigating the Future. Journal of Robotics, 15(3), 123-145.
5. Jones, R., & White, L. (2015). Reactive Navigation: Strategies for Dynamic Environments. International Journal of Robotics Research, 32(4), 456-478.
6. Karaman, S., & Frazzoli, E. (2011). Sampling-Based Algorithms for Optimal Motion Planning.
7. International Journal of Robotics Research, 30(7), 846-894.
8. Li, S., et al. (2021). Edge Computing for Autonomous Mobile Robots: Opportunities and Challenges. Journal of Parallel and Distributed Computing, 147, 162-177.
9. Miller, D., et al. (2013). Behaviour-Based Systems for Mobile Robot Navigation: A Comprehensive Review. Robotics Today, 19(1), 67-89.
10. Roberts, M., & Smith, D. (2016). Path Following Algorithms in Autonomous Mobile Robots. IEEE Transactions on Robotics, 30(5), 1123-1140.
11. Smith, A., & Johnson, B. (2008). Rule-Based Approaches in Mobile Robot Navigation. Journal of Robotics, 21(2), 89-110.
12. Smith, C., et al. (2021). Autonomous Mobile Robots: A Paradigm Shift in Automation. International Journal of Automation and Robotics, 28(2), 67-89.
13. Wang, J., et al. (2020). Cutting-Edge Technologies for Obstacle Avoidance in Autonomous Mobile Robots. Robotics Today, 18(4), 210-235.
14. Wu, G., et al. (2019). Deep Reinforcement Learning for Mobile Robot Navigation: A Review. IEEE Transactions on Cognitive and Developmental Systems, 11(2), 195-210.
15. Alatise, M. B., & Hancke, G. P. (2020). A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods. In IEEE Access (Vol. 8, pp. 39830–39846). Institute of Electrical and Electronics Engineers Inc.
16. Chen, P., Pei, J., Lu, W., & Li, M. (2022). A deep reinforcement learning-based method for real time path planning and dynamic obstacle avoidance. Neurocomputing, 497, 64–75.
17. Choi, J., Lee, G., & Lee, C. (2021). Reinforcement learning-based dynamic obstacle avoidance and integration of path planning. Intelligent Service Robotics, 14(5), 663–677.
18. Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research, 17(7), 760–772.
19. Fragapane, G., de Koster, R., Sgarbossa, F., & Strandhagen, J. O. (2021). Planning and control   of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, 294(2), 405–426.
20. Hanh, L. D., & Cong, V. D. (2023). Path Following and Avoiding Obstacles for Mobile Robot under Dynamic Environments Using Reinforcement Learning. Journal of Robotics and Control (JRC), 4(2), 157–164.
21. Hillebrand, M., Lakhani, M., & Dumitrescu, R. (2020). A design methodology for deep reinforcement learning in autonomous systems. Procedia Manufacturing, 52, 266–271.
22. Huh, D. J., Park, J. H., Huh, U. Y., & Kim, H. Il. (2002). Path planning and navigation for autonomous mobile robot. IECON Proceedings (Industrial Electronics Conference), 2, 1538-1542.
23. IEEE Robotics and Automation Society, & Institute of Electrical and Electronics Engineers. (n.d.-a). 2016 IEEE International Conference on Robotics and Automation: Stockholm, Sweden, May 16th 21st.
24. IEEE Robotics and Automation Society, & Institute of Electrical and Electronics Engineers. (n.d.-b). 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE).
25. Institute of Electrical and Electronics Engineers. (n.d.). 2020 IEEE International Conference on Robotics and Automation (ICRA) : 31 May-31 August, 2020. Paris, France.
26. Kobayashi, M., Zushi, H., Nakamura, T., & Motoi, N. (2023). Local Path Planning: Dynamic Window Approach with Q-Learning Considering Congestion Environments for Mobile Robot. IEEE Access, 11, 96733–96742.
27. Kretzschmar, H., Spies, M., Sprunk, C., & Burgard, W. (2016). Socially compliant mobile robot navigation via inverse reinforcement learning. International Journal of Robotics Research, 35(11), 1352–1370.
28. Liu, X., & Li, Z. (2023). Dynamic multiple access based on deep reinforcement learning for Internet of Things. Computer Communications, 210, 331–341.
29. Loganathan, A., & Ahmad, N. S. (2023). A systematic review of recent advances in autonomous mobile robot navigation. Engineering Science and Technology, an International Journal, 40, 101343.
30. Luo, R. C., Lee, S. L., Wen, Y. C., & Hsu, C. H. (2020). Modular ROS-based autonomous mobile industrial robot system for automated intelligent manufacturing applications. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2020-July, 1673–1678.
31. Nahavandi, S., Alizadehsani, R., Nahavandi, D., Mohamed, S., Mohajer, N., Rokonuzzaman, M., & Hossain, I. (2022). A Comprehensive Review on Autonomous Navigation.
32. Niloy, M. A. K., Shama, A., Chakrabortty, R. K., Ryan, M. J., Badal, F. R., Tasneem, Z.,
33. Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., Islam, M. R., & Saha, D. K. (2021). Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review. IEEE Access, 9, 35338–35370.
34. Pandey, A. (2017). Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review. International Robotics & Automation Journal, 2(3).
35. Panigrahi, P. K., & Bisoy, S. K. (2022). Localization strategies for autonomous mobile robots: A review. Journal of King Saud University - Computer and Information Sciences, 34(8), 6019–6039.
36. Sun, H., Zhang, W., Yu, R., & Zhang, Y. (2021). Motion Planning for Mobile Robots – Focusing on Deep Reinforcement Learning: A Systematic Review. In IEEE Access (Vol. 9, pp. 69061– 69081). Institute of Electrical and Electronics Engineers Inc.