Open Access
Issue
E3S Web Conf.
Volume 552, 2024
16th International Conference on Materials Processing and Characterization (ICMPC 2024)
Article Number 01080
Number of page(s) 15
DOI https://doi.org/10.1051/e3sconf/202455201080
Published online 23 July 2024
  1. Mohan, Y., & Ponnambalam, S.G. (2009, December). An extensive review of research in swarm robotics. In 2009 world congress on nature & biologically inspired computing (nabic) (pp. 140–145). IEEE. [CrossRef] [Google Scholar]
  2. Navarro, I., & Matía, F. (2013). An introduction to swarm robotics. Isrn robotics, 2013, 1–10. [CrossRef] [Google Scholar]
  3. Osaba, E., Del Ser, J., Iglesias, A., Yang, X.S. Soft Computing for Swarm Robotics: New Trends and Applications; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
  4. Yang, H.A., Cao, S., Bai, L., Zhang, Z., Kong, J. A distributed and parallel self-assembly approach for swarm robotics. Robot. Auton. Syst. 2019, 118, 80–92. [CrossRef] [Google Scholar]
  5. Singh, P.K., Singh, R., Nandi, S.K., Ghafoor, K.Z., Rawat, D.B., Nandi, S. An efficient blockchain-based approach for cooperative decision making in swarm robotics. Internet Technol. Lett. 2020, 3, e140 [CrossRef] [Google Scholar]
  6. Ali, S., Khan, Z., Din, A., Hassan, M.U. Investigation on communication aspects of multiple swarm networked robotics. Turk. J. Electr. Eng. Comput. Sci. 2019, 27, 2010–2020. [CrossRef] [Google Scholar]
  7. Singh, P.K., Singh, R., Nandi, S.K., Ghafoor, K.Z., Rawat, D.B., Nandi, S. An efficient blockchain-based approach for cooperative decision making in swarm robotics. Internet Technol. Lett. 2020, 3, e140 [CrossRef] [Google Scholar]
  8. Barca, J.C., & Sekercioglu, Y.A. (2013). Swarm robotics reviewed. Robotica, 31(3), 345–359 [CrossRef] [Google Scholar]
  9. Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015). A survey of research on cloud robotics and automation. IEEE Transactions on automation science and engineering, 12(2), 398–409. [CrossRef] [Google Scholar]
  10. Chen, D., Dou, W., Wang, X., & Chen, J. (2014). A big data architecture design for smart grid flexibly integrating various applications. IEEE Transactions on Industrial Informatics, 10(2), 1840–1847. [Google Scholar]
  11. Şahin, E., Girgin, S., Bayindir, L., & Turgut, A.E. (2008). Swarm robotics (pp. 87–100). Springer Berlin Heidelberg. [Google Scholar]
  12. Vivaldini, K.C.T., Galdames, J.P.M., Pasqual, T.B., Sobral, R.M., Araújo, R.C., Becker, M., & Caurin, G.A.P. (2010). Automatic routing system for intelligent warehouses. In IEEE International Conference on Robotics and Automation (Vol. 1, pp. 1–6). [Google Scholar]
  13. Draganjac, I., Miklić, D., Kovačić, Z., Vasiljević, G., & Bogdan, S. (2016). Decentralized control of multi-AGV systems in autonomous warehousing applications. IEEE Transactions on Automation Science and Engineering, 13(4), 1433–1447. [CrossRef] [Google Scholar]
  14. Ben-Ari, M., Mondada, F., Ben-Ari, M., & Mondada, F. (2018). Swarm robotics. Elements of robotics, 251–265. [CrossRef] [Google Scholar]
  15. Yang, H.a.; Cao, S.; Bai, L.; Zhang, Z.; Kong, J. A distributed and parallel self-assembly approach for swarm robotics. Robot. Auton. Syst. 2019, 118, 80–92.. [CrossRef] [Google Scholar]
  16. Navarro, I., & Matía, F. (2013). An introduction to swarm robotics. Isrn robotics, 2013,1–10. [CrossRef] [Google Scholar]
  17. J.C. Barca, Y.A. Sekercioglu Swarm robotics reviewed Robotica, 31 (2014), pp. 345–359, 10.1017/S026357471200032X. [Google Scholar]
  18. Finkenzeller, K. (2010). RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication. John Wiley & Sons. [Google Scholar]
  19. Bjerknes, J.D., & Winfield, A.F. (2014). On fault tolerance and scalability of swarm robotic systems. In Distributed Autonomous Robotic Systems: The 10th International Symposium (pp. 431–444). Springer Berlin Heidelberg. [Google Scholar]
  20. Purwin, O., D’Andrea, R., & Lee, J.W. (2008). Theory and implementation of path planning by negotiation for decentralized agents. Robotics and Autonomous Systems, 56(5), 422–436. [CrossRef] [Google Scholar]
  21. Li, J., Tan, Y. A probabilistic finite state machine-based strategy for multi-target search using swarm robotics. Appl. Soft Comput. 2019, 77, 467–483. [CrossRef] [Google Scholar]
  22. Barcis, A., Barcis, M., and Bettstetter, C. (2019). “Robots that sync and swarm: a proof of concept in ROS 2,” in Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (New Brunswick, NJ: IEEE), 98–104. [Google Scholar]
  23. Dorigo, M., Floreano, D., Gambardella, L.M., Mondada, F., Nolfi, S., Baaboura, T., et al. (2013). Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20, 60–71. doi: 10.1109/MRA.2013.2252996 [CrossRef] [Google Scholar]
  24. Majid, M.H.A., Arshad, M.R., & Mokhtar, R.M. (2022). Swarm robotics behaviors and tasks: a technical review. Control Engineering in Robotics and Industrial Automation: Malaysian Society for Automatic Control Engineers (MACE) Technical Series 2018, 99–167. [CrossRef] [Google Scholar]
  25. Daniel, H. Stolfi, M.B., Lennox, B., & Arvin, F. (2021, February). Self-organised swarm flocking with deep reinforcement learning. In 2021 7th International Conference on Automation, Robotics and Applications (ICARA) (pp. 226–230). IEEE. [Google Scholar]
  26. Ben-Ari, M., Mondada, F., Ben-Ari, M., & Mondada, F. (2018). Swarm robotics. Elements of robotics, 251–265. [CrossRef] [Google Scholar]
  27. Schillinger, P., Bürger, M., Dimarogonas, D.V. Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems Int. J. Robot. Res., 37 (7) (2018), pp. 818–838 [CrossRef] [Google Scholar]
  28. Nedjah, N., & Junior, L.S. (2019). Review of methodologies and tasks in swarm robotics towards standardization. Swarm and Evolutionary Computation, 50, 100565. [CrossRef] [Google Scholar]
  29. Mannone, M., Seidita, V., & Chella, A. (2023). Modeling and designing a robotic swarm: A quantum computing approach. Swarm and Evolutionary Computation, 79, 101297. [CrossRef] [Google Scholar]
  30. Sarma, S.E., Want, R., & Want, R. (2000). Networked RFID systems and lightweight cryptography. In Proceedings of the 2000 ACM workshop on Security and privacy in digital rights management (pp. 47–61). [Google Scholar]
  31. Koscher, K., Czeskis, A., Roesner, F., Patel, S., Kohno, T., Checkoway, S., & Savage, S. (2010). Experimental security analysis of a modern automobile. In IEEE Symposium on Security and Privacy (SP) (pp. 447–462). [Google Scholar]
  32. Kaplan, E.D., & Hegarty, C.J. (2005). Understanding GPS: Principles and Applications. Artech House. [Google Scholar]
  33. Khaldi, B., & Cherif, F. (2015). An overview of swarm robotics: Swarm intelligence applied to multi-robotics. International Journal of Computer Applications, 126(2), 31–37.. [CrossRef] [Google Scholar]
  34. Bjerknes, J.D., & Winfield, A.F. (2014). On fault tolerance and scalability of swarm robotic systems. In Distributed Autonomous Robotic Systems: The 10th International Symposium (pp. 431–444). Springer Berlin Heidelberg. [Google Scholar]
  35. Li, R., & Cheng, Y. (2020). An intelligent warehouse management system based on the internet of things and big data analysis. Sensors, 20(12), 3605. [CrossRef] [PubMed] [Google Scholar]
  36. Abuzneid, A., Al-Smadi, M., Shaalan, K., Al-Ayyoub, M., Al-Khasawneh, A., & Alzoubi, D. (2017). Internet of Things (IoT) Operating Systems Support: Motivation, Survey, and Open Challenges. Journal of King Saud University - Computer and Information Sciences. [Google Scholar]
  37. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. [CrossRef] [Google Scholar]
  38. Zhang, Y., Wang, X., Tian, Y., Zhang, X., & Yang, D. (2019). A Review on the Telematics Technologies in the Application of Intelligent Transportation. IEEE Access, 7, 45675–45692. [Google Scholar]

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