A navigation matching correction method based on a patrol robot

By introducing a dual time decay model and a navigation matching correction method based on a multi-robot collaborative framework, the problems of insufficient navigation accuracy and dynamic environment adaptability in existing inspection robot navigation technologies are solved, achieving efficient and accurate navigation and positioning correction, and improving the autonomous navigation capability of railway train inspection.

CN120685126BActive Publication Date: 2026-07-24CRRC HANGZHOU DIGITAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-04
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing inspection robot navigation technology is insufficient in path planning accuracy, environmental adaptability, and real-time correction capability in dynamic environments, resulting in navigation accuracy and efficiency that cannot meet the needs of railway train inspection.

Method used

A navigation matching and correction method based on inspection robots is adopted. By introducing a dual time decay model, the robot pose information is dynamically quantified and modeled. Combined with high confidence information fusion and adaptive decay parameter correction under a multi-robot collaborative framework, a closed-loop control system is established to realize dynamic management and correction of positioning information.

Benefits of technology

It significantly improves the continuity, accuracy and reliability of autonomous navigation of inspection robots in complex industrial environments, enhances the robustness of positioning and the flexibility of path planning in dynamic environments, and ensures accurate detection of key components.

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Abstract

The application discloses a navigation matching correction method based on a patrol robot, and establishes a feature database by deploying physical calibration objects and identifying natural feature objects to provide reliable positioning reference for the robot. In the positioning process, visual and laser radar data are fused to realize coarse positioning, and a dynamic trust degree evaluation mechanism is introduced to quantitatively position the reliability in real time through an exponential decay model. When the trust degree is lower than a threshold value, the system automatically triggers a compensation behavior to search for features again. In the aspect of multi-robot cooperation, secondary positioning correction is realized through trajectory matching and data fusion, high-confidence reference data is screened by using a clustering algorithm, and the group positioning accuracy is improved. For key inspection areas, multi-angle image matching is adopted to realize fine positioning, and positioning errors are dynamically corrected through a sliding window. Through closed-loop correction and adaptive optimization, the application significantly improves the continuity and accuracy of robot navigation in a complex environment, and is suitable for intelligent patrol inspection requirements of railway trains.
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Citation Information

Patent Citations

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