AI-Driven Navigation for Autonomous Inspection Robots

  • Authors

    • Andrey Ershov Professor, Siberian Division Academy of Sciences, Russia Author
    • Alexey Lyapunov Professor, Moscow State University, Russia Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2023PII8S8A

    Published 07-04-2023

  • Autonomous Inspection Robots, Deep Reinforcement Learning, Sensor Fusion, Visual-Inertial Odometry, Path Planning, Intelligent Automation

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    A. Ershov and A. Lyapunov, “AI-Driven Navigation for Autonomous Inspection Robots”, IJIARE, vol. 6, no. 2, pp. 01–08, Jul. 2023, doi: 10.67228/30715725/IJIARE-2023PII8S8A.

  • Abstract

    We focus on autonomous inspection robots operating in complex, dynamic, and GNSS-denied industrial environments dealing with critical problems related to real-time trajectory optimization, feature tracking and precise spatial localization. Remember that traditional control algorithms tend to not adapt well to difficult visual occlusions, non-Gaussian sensor noise, or unforeseen structural impediments. We propose a unified AI pipeline that fuses deep reinforcement learning with sensor data—by combining Light Detection and Ranging (LiDAR), Visual-Inertial Odometry (VIO), and thermal images—to discover flexible navigation strategies for autonomous inspection ground vehicles. In this work, we form a prior method based on Deep Deterministic Policy Gradient controller and a adaptive unscented Kalman filter which continuously providing constantly estimating robot states and improving the motion primitives in hazardous operating conditions. A series of experimental evaluations conducted on both simulated industrial plants and a physical mock-up facility show that the AI-based method achieves up to 51% relative reduction in localization error compared to traditional Simultaneous Localization and Mapping methods. The results show that the path deviation decrease by 38.15%, collision avoidance timeliness is significantly improved, and the accuracy of anomalies detection can reach more than 98%. These results validate that end-to-end AI navigation architectures deliver the robust performance needed for next-gen automated industrial monitoring and non-destructive evaluation at scale.

  • References

    [1] Edwards, S., Anderson, S., & Aitken, J. (2023). Visual Localisation for Pipe Inspection Robots using Prior Information of the Environment (Master's thesis). Department of Automatic Control and Systems Engineering, University of Sheffield.

    [2] Gelhaus, F. E., & Roman, H. T. (1990). Robot applications in nuclear power plants. Progress in Nuclear Energy, 23(1), 1–33.

    [3] Penington, J., & Shakeri, S. (2006). A human factors approach to effective maintenance. Proceedings of the Annual Conference of the Canadian Nuclear Society, Toronto, Canada.

    [4] Tong, X., Wang, Y., & Zhao, C. (2023). Multi-modal sensor fusion for autonomous mobile robots in GPS-denied environments. Journal of Field Robotics, 40(5), 601–618.

    [5] Zhang, H., & Wang, J. (2022). Deep learning-based visual inspection and seam tracking for mobile industrial platforms. Robotics and Computer-Integrated Manufacturing, 78, 102389.

    [6] Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/TRO.2016.2624754

    [7] Macaulay, M. O., & Shafiee, M. (2022). Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure. Autonomous Intelligent Systems, 2(8), 1–27. https://doi.org/10.1007/s43684-022-00025-3

    [8] Aitken, J., Evans, M., Worley, R., et al. (2021). Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: A review. IEEE Access, 9, 140173–140198. https://doi.org/10.1109/ACCESS.2021.3119968

    [9] Siciliano, B., & Khatib, O. (Eds.). (2016). Springer handbook of robotics (2nd ed.). Springer.

    [10] Corke, P. (2017). Robotics, vision and control: Fundamental algorithms in MATLAB (2nd ed.). Springer.

    [11] Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press.

    [12] Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots (2nd ed.). MIT Press.

    [13] Howard, A. G., Zhu, M., Chen, B., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

    [14] Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.

    [15] Ortiz, A., Antich, J., & Bonnin-Pascual, F. (2020). Navigation for inspection. In Encyclopedia of Robotics. Springer.

  • Downloads

Similar Articles

11-20 of 84

You may also start an advanced similarity search for this article.