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

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