Radiation-Resistant FPGA-Based Heterogeneous Architecture with Dynamic Scheduling for Real-Time Nuclear Robot Control
Abstract
A novel FPGA-based heterogeneous computing architecture is proposed, which is used to control nuclear robots in real time, and an adaptive task scheduling algorithm (DA-TSA) is proposed. It features multi-core computing layer, radiation-hardened storage with LDPC error correction, and high-speed serial/on-chip network communication layer. The DA-TSA uses the 3D weighted model to compute dynamic priorities and resource allocation. Compared with traditional algorithms (FPS, EDF, RMS), DA-TSA reduces the average task completion time by 21.9% – 31.1%, and the resource utilization rate is 26.7%. Compared with traditional systems, the real-time control system achieves 57.1% reduction in environment perception delay, 43.8% faster path planning and only 36.6% failure rates compared with traditional systems validated under simulated high radiation environment (SEU injection rate: 10 Hz/cm ²/s).References
Alkan, N., & Kahraman, C. (2022). Prioritization of Supply Chain Digital Transformation Strategies Using Multi-Expert Fermatean Fuzzy Analytic Hierarchy Process. Informatica, 34(1), 1-33. doi:10.15388/22-INFOR493
Karbauskaitė, R., Sakalauskas, L., & Dzemyda, G. (2020). Kriging Predictor for Facial Emotion Recognition Using Numerical Proximities of Human Emotions. Informatica, 31(2), 249-275. doi:10.15388/20-INFOR419
Boltürk, E., & Kahraman, C. (2022). Interval-Valued and Circular Intuitionistic Fuzzy Present Worth Analyses. Informatica, 33(4), 693-711. doi:10.15388/22-INFOR478
Robinson, N., Tidd, B., Campbell, D., Kulić, D., & Corke, P. (2023). Robotic vision for human-robot interaction and collaboration: A survey and systematic review. ACM Transactions on Human-Robot Interaction, 12(1), 1-66.https://doi.org/10.1145/3570731
Kumar, S., Kumar, D., Dangi, R., Choudhary, G., Dragoni, N., & You, I. (2024). A review of Lightweight Security and privacy for resource-constrained IoT devices. Computers, Materials and Continua, 78(1), 31-63. https://doi.org/10.32604/cmc.2023.047084
Santos, J., Wauters, T., Volckaert, B., & De Turck, F. (2021). Towards low-latency service delivery in a continuum of virtual resources: State-of-the-art and research directions. IEEE Communications Surveys & Tutorials, 23(4), 2557-2589. doi: 10.1109/COMST.2021.3095358.
Canas-Moreno, S., Piñero-Fuentes, E., Rios-Navarro, A., Cascado-Caballero, D., Perez-Peña, F., & Linares-Barranco, A. (2023). Towards neuromorphic FPGA-based infrastructures for a robotic arm. Autonomous Robots, 47(7), 947-961. https://doi.org/10.1007/s10514-023-10111-x
Yoshimoto, Y., & Tamukoh, H. (2021). FPGA implementation of a binarized dual-stream convolutional neural network for service robots. Journal of robotics and mechatronics, 33(2), 386-399. https://doi.org/10.20965/jrm.2021.p0386
Kadokawa, Y., Tsurumine, Y., & Matsubara, T. (2021). Binarized P-network: Deep reinforcement learning of robot control from raw images on FPGA. IEEE Robotics and Automation Letters, 6(4), 8545-8552. DOI: 10.1109/LRA.2021.3111416
Fekik, A., Khati, H., Azar, A. T., Hamida, M. L., Denoun, H., Hameed, I. A., & Kamal, N. A. (2024). FPGA in the loop implementation of the PUMA 560 robot based on backstepping control. IET Control Theory & Applications, 18(15), 1877-1891.https://doi.org/10.1049/cth2.12589
Lomas-Barrie, V., Silva-Flores, R., Neme, A., & Pena-Cabrera, M. (2022). A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA. Electronics, 11(5), 696. https://doi.org/10.3390/electronics11050696
Nada, A. A., & Bayoumi, M. A. (2024). Development of embedded fuzzy control using reconfigurable FPGA technology. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 65(2), 609-626. https://doi.org/10.1080/00051144.2024.2313904
Sugiura, K., & Matsutani, H. (2022). A universal LiDAR SLAM accelerator system on low-cost FPGA. IEEE Access, 10, 26931-26947. DOI: 10.1109/ACCESS.2022.3157822
Dereli, S., & Köker, R. (2023). Hardware design of FPGA-based embedded heuristic optimization technique for solving a robotic problem: IC-PSO. Arabian Journal for Science and Engineering, 48(8), 10441-10455. https://doi.org/10.1007/s13369-023-07655-6
Huang, C. H. (2021). An FPGA-based hardware/software design using binarized neural networks for agricultural applications: A case study. IEEE Access, 9, 26523-26531.DOI: 10.1109/ACCESS.2021.3058110
Saha, S., Zhai, X., Ehsan, S., Majeed, S., & McDonald-Maier, K. (2021). RASA: Reliability-aware scheduling approach for FPGA-based resilient embedded systems in extreme environments. IEEE transactions on systems, man, and cybernetics: systems, 52(6), 3885-3899. DOI: 10.1109/TSMC.2021.3077697
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