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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 01011 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 01011
Number of page(s) 18
Section Robotics Design and Control
DOI https://doi.org/10.1051/epjconf/202636701011
Published online 29 April 2026
  1. X. Ren, K. Qu, J. Guo, H. Yin, B. Huang, Research on accurate fire source localization and seconds-level autonomous fire extinguishing technology, Sci. Rep. 15(1), 17135 (2025). https://doi.org/10.1038/s41598-025-01830-5 [Google Scholar]
  2. C. Liu, D. Zhang, W. Liu, X. Sui, Y. Huang, X. Ma, X. Yang, X. Wang, Enhancing autonomous exploration for robotics via real time map optimization and improved frontier costs, Sci. Rep. 15(1), 12261 (2025). https://doi.org/10.1038/s41598-025-97231-9 [Google Scholar]
  3. Y. Chang, Y. Cheng, U. Manzoor, J. Murray, A review of UAV autonomous navigation in GPS-denied environments, Robot. Auton. Syst. 170, 104533 (2023). https://doi.org/10.1016/j.robot.2023.104533 [Google Scholar]
  4. Mordor Intelligence, Search and Rescue Robots Market Size and Share Analysis: Growth Trends and Forecasts (2025–2030) (2025) [Google Scholar]
  5. F. Bu, M. S. Gharajeh, Intelligent and vision-based fire detection systems: A survey, Image Vis. Comput. 91, 103803 (2019). https://doi.org/10.1016/j.imavis.2019.08.007 [Google Scholar]
  6. L. Kong, J. Li, S. Guo, X. Zhou, D. Wu, Computer vision based early fire-detection and firefight- ing mobile robots oriented for onsite construction, J. Civ. Eng. Manag. 30(8), 720–737 (2024). https://doi.org/10.3846/jcem.2024.21360 [Google Scholar]
  7. R. Jadeja, T. Trivedi, J. Surve, Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms, Sci. Rep. 14(1), 25047 (2024). https://doi.org/10.1038/s41598-024-75156-z [Google Scholar]
  8. A. Tourani, H. Bavle, J. L. Sanchez-Lopez, H. Voos, Visual SLAM: What are the current trends and what to expect?, Sensors 22(23), 9297 (2022). https://doi.org/10.3390/s22239297 [Google Scholar]
  9. R. Wang, J. Zhang, M. Lyu, C. Yan, Y. Chen, An improved frontier-based robot exploration strategy combined with deep reinforcement learning, Robot. Auton. Syst. 181, 104783 (2024). https://doi.org/10.1016/j.robot.2024.104783 [Google Scholar]
  10. H. Zhao, Y. Guo, Y. Liu, J. Jin, Multirobot unknown environment exploration and obstacle avoid- ance based on a Voronoi diagram and reinforcement learning, Expert Syst. Appl. 264, 125900 (2025). https://doi.org/10.1016/j.eswa.2024.125900 [Google Scholar]
  11. B. Eslam, A. Samir, M. Osama, M. Moustafa, M. Ali, The evolution of firefighting robots: Bridg- ing technology and safety: A comprehensive review, Adv. Sci. Technol. J. (2025) [Google Scholar]
  12. L. Šiktar, B. C´ aran, B. Šekoranja, M. Švaco, Autonomous UAV navigation for search and rescue missions using computer vision and convolutional neural networks, arXiv:2507.18160 [cs.RO] (2025). https://doi.org/10.48550/arXiv.2507.18160 [Google Scholar]
  13. Y. Zhou, M. Sun, A visual SLAM loop closure detection method based on lightweight siamese capsule network, Sci. Rep. 15(1), 7644 (2025). https://doi.org/10.1038/s41598-025-90511-4 [Google Scholar]
  14. L. Zhao, T. Chen, P. Yuan, X. Li, B. Chen, Review of deep learning-based visual SLAM: Types, approaches, and future work, Ind. Robot (2025). https://doi.org/10.1108/IR-04-2025-0137 [Google Scholar]
  15. A. A. Adil, S. Sakhrieh, J. Mounsef, N. Maalouf, A multi-robot collaborative manipulation framework for dynamic and obstacle-dense environments: Integration of deep learning for real- time task execution, Front. Robot. AI 12, 1585544 (2025). https://doi.org/10.3389/frobt.2025.1585544 [Google Scholar]
  16. V. M. Tuck, H. Parwana, P.-W. Chen, G. Fainekos, B. Hoxha, H. Okamoto, S. S. Sastry, S. A. Seshia, MRTA-Sim: A modular simulator for multi-robot allocation, planning, and con- trol in open-world environments, arXiv:2504.15418 [cs.RO] (2025). https://doi.org/10.48550/arXiv.2504.15418 [Google Scholar]
  17. S. Nahavandi, R. Alizadehsani, D. Nahavandi, S. Mohamed, N. Mohajer, M. Rokonuzzaman, I. Hossain, A comprehensive review on autonomous navigation, ACM Comput. Surv. 57(9), 234 (2025). https://doi.org/10.1145/3727642 [Google Scholar]
  18. W. Chen, W. Chi, S. Ji, H. Ye, J. Liu, Y. Jia, J. Yu, J. Cheng, A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration, Biomim. Intell. Robot. 5(2), 100203 (2025). https://doi.org/10.1016/j.birob.2024.100203 [Google Scholar]
  19. H. Chen, L. Hou, G. Zhang, S. Moon, Development of BIM, IoT and AR/VR technologies for fire safety and upskilling, Autom. Constr. 125, 103631 (2021). https://doi.org/10.1016/j.autcon.2021.103631 [Google Scholar]
  20. C. C. Ulloa, J. Álvarez, J. del Cerro, A. Barrientos, Vision-based collaborative robots for explo- ration in uneven terrains, Mechatronics 100, 103184 (2024). https://doi.org/10.1016/j.mechatronics.2024.103184 [Google Scholar]
  21. S. Jiang, S. Wang, Z. Yi, M. Zhang, X. Lv, Autonomous navigation system of greenhouse mobile robot based on 3D LiDAR and 2D LiDAR SLAM, Front. Plant Sci. 13, 815218 (2022). https://doi.org/10.3389/fpls.2022.815218 [Google Scholar]
  22. A. Koval, S. Karlsson, G. Nikolakopoulos, Experimental evaluation of autonomous map-based Spot navigation in confined environments, Biomim. Intell. Robot. 2(1), 100035 (2022). https://doi.org/10.1016/j.birob.2022.100035 [Google Scholar]
  23. S. Li, Z. Ren, J. I. Kim, A structured review of SLAM generation in the AEC industry: Technical framework, site-specific challenges, and adaptive strategies, KSCE J. Civ. Eng. 30(3), 100408 (2026). https://doi.org/10.1016/j.kscej.2025.100408 [Google Scholar]

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