Abstract
In recent years, urban search and rescue (USAR) tasks in unknown environments have arisen widespread attention in the domain of Smart City. However, the complexity and unpredictability of unknown environments pose great difficulties for decision-making in search and rescue phases, ultimately resulting in serious consequences. To address the decision-making problems, this paper proposes a hierarchical swarm robots digital twin framework, which includes a physical layer, virtual layer, twin data layer, parallel decision layer, and service layer, to provide efficient decision support for USAR tasks in large static unknown indoor environments. Besides, considering the requirements of reality, we specifically design two modules: (1) real-time twin model modeling module, solving the issue of poor real-time performance in the offline construction of twin models. (2) parallel decision path planning module based on deep reinforcement learning, solving the difficulty users face in obtaining direct decision support. In our experiment, we implement the proposed framework using Unity3D and the Robot Operating System (ROS). The results show that the framework is capable of improving the decision efficiency of USAR tasks. Additionally, the update frequency of the proposed framework is kept in less than 1 s while providing optimal path planning.
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References
Surmann, H., Kaiser, T., Leinweber, A., Senkowski, G., Slomma, D., Thurow, M.: Small commercial uavs for indoor search and rescue missions. In: 2021 7th International Conference on Automation, Robotics and Applications (ICARA), pp. 106–113. IEEE (2021)
Feng, F., Li, D., Zhao, J., Sun, H.: Research of collaborative search and rescue system for photovoltaic mobile robot based on edge computing framework. In: 2020 Chinese Control And Decision Conference (CCDC), pp. 2337–2341 (2020)
Guo, Z.-Z., Wang, K., Qin, J.-Y., Li, X.-B., Ran, D.-C., Shen, Y.: An intelligent maritime scene frame prediction based on digital twins technology. In: 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 25–28. IEEE (2021)
Mihai, S., et al.: Digital twins: a survey on enabling technologies, challenges, trends and future prospects. IEEE Commun. Surv. Tutor. 24, 2255–2291 (2022)
Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T.H., Liu, Y.: A survey on digital twins: architecture, enabling technologies, security and privacy, and future prospects. IEEE Internet Things J. 10, 14965–14987 (2023)
Li, L., Lei, B., Mao, C.: Digital twin in smart manufacturing. J. Ind. Inf. Integr. 26, 100289 (2022)
Li, C., Mahadevan, S., Ling, Y., Choze, S., Wang, L.: Dynamic bayesian network for aircraft wing health monitoring digital twin. AIAA J. 55, 930–941 (2017)
Lim, K.Y.H., Zheng, P., Chen, C.-H., Huang, L.: A digital twin-enhanced system for engineering product family design and optimization. J. Manuf. Syst. 57, 82–93 (2020)
Li, J., Liu, M., Wang, W., Hu, C.: Inspection robot based on offline digital twin synchronization architecture. IEEE J. Radio Freq. Identificat. 6, 943–947 (2022)
Ji, G., Hao, J., Gao, J., Lu, C.: Digital twin modeling method for individual combat quadrotor UAV. In: 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 1–4. IEEE (2021)
Ren, J., Nalpantidis, L., Andersen, N.: Building digital twin of mobile robotics testbed using centralized localization system. In: 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA), pp. 139–145 IEEE (2023)
Kattepur, A.: Robotic tele-operation performance analysis via digital twin simulations. In: 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 415–417. IEEE (2022)
Birais, H., Rafikova, E.: Augmented reality system for immersive mobile robot simulation and trajectory estimation. In: 2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR) and 2023 Workshop on Robotics in Education (WRE), pp. 17–22. IEEE (2023)
Kang, Y., Kim, D., Kim, K.: URDF generator for manipulator robot. In: 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 483–487. IEEE (2019)
Ai, Y., Li, C.: Design of an indoor surveying and mapping robot based on SLAM technology. In: 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA), pp. 848–852 (2021)
Kang, B., Liang, D., Ding, W., Zhou, H., Zhu, W.-P.: Grayscale-thermal tracking via inverse sparse representation-based collaborative encoding. IEEE Trans. Image Process. 29, 3401–3415 (2020)
Wang, H., Hao, J., Wu, W., Jiang, A., Mao, K., Xia, Y.: A new AGV path planning method based on PPO algorithm. In: 2023 42nd Chinese Control Conference (CCC), pp. 3760–3765 (2023)
Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2432–2437. IEEE (2005)
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Guo, L., Cai, G., Zhu, K., Qiu, H., Liu, C. (2024). A Parallel Intelligent Search and Rescue System for Swarm Robots Based on Digital Twin. In: Huang, DS., Zhang, X., Zhang, C. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14879. Springer, Singapore. https://doi.org/10.1007/978-981-97-5675-9_1
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DOI: https://doi.org/10.1007/978-981-97-5675-9_1
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