Research on Dead Reckoning Algorithm for Self-Propelled Pipeline Robots in Three-Dimensional Complex Pipelines

2025-12-18

AI Summary

To address challenges in complex 3D curved pipes—including entanglement of tethered robots, localization drift in visual/lidar SLAM due to sparse features and lighting sensitivity, and the trade-off between mobility and positioning accuracy—this paper proposes a self-propelled pipe inspection robot employing tightly coupled inertial measurement unit (IMU) and wheel odometry for dead reckoning. We innovatively design an extended Kalman filter (EKF) framework that jointly optimizes attitude estimation and wheel-based distance measurement, achieving, for the first time in pipe robots, deep kinematic–inertial fusion. A mechanically adaptive clamping mechanism is integrated to enhance obstacle negotiation capability. Experimental validation in a rectangular toroidal pipe demonstrates a positioning error of less than 0.8%, significantly outperforming vision- or lidar-only approaches. The system enables cable-free autonomous mapping and high-precision trajectory tracking.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the field of gas pipeline location, existing pipeline location methods mostly rely on pipeline location instruments. However, when faced with complex and curved pipeline scenarios, these methods often fail due to problems such as cable entanglement and insufficient equipment flexibility. To address this pain point, we designed a self-propelled pipeline robot. This robot can autonomously complete the location work of complex and curved pipelines in complex pipe networks without external dragging. In terms of pipeline mapping technology, traditional visual mapping and laser mapping methods are easily affected by lighting conditions and insufficient features in the confined space of pipelines, resulting in mapping drift and divergence problems. In contrast, the pipeline location method that integrates inertial navigation and wheel odometers is less affected by pipeline environmental factors. Based on this, this paper proposes a pipeline robot location method based on extended Kalman filtering (EKF). Firstly, the body attitude angle is initially obtained through an inertial measurement unit (IMU). Then, the extended Kalman filtering algorithm is used to improve the accuracy of attitude angle estimation. Finally, high-precision pipeline location is achieved by combining wheel odometers. During the testing phase, the roll wheels of the pipeline robot needed to fit tightly against the pipe wall to reduce slippage. However, excessive tightness would reduce the flexibility of motion control due to excessive friction. Therefore, a balance needed to be struck between the robot's motion capability and positioning accuracy. Experiments were conducted using the self-propelled pipeline robot in a rectangular loop pipeline, and the results verified the effectiveness of the proposed dead reckoning algorithm.

Problem

• Develops a self-propelled robot for mapping complex 3D pipelines without external dragging.
• Proposes an EKF-based method combining IMU and wheel odometers to improve positioning accuracy.
• Balances robot motion control and positioning by managing wheel friction against pipe walls.

Innovation

• Uses extended Kalman filter to fuse IMU and wheel odometer data
• Implements self-propelled robot for autonomous navigation in complex pipelines
• Balances wheel friction to optimize motion control and positioning accuracy