Tunnel invert detection robot navigation positioning system and method based on multi-technology fusion

Abstract

This invention relates to a navigation and positioning system and method for a tunnel invert arch inspection robot based on multi-technology fusion, belonging to the fields of tunnel engineering inspection technology and navigation and positioning technology. The system includes a sensor module, a data processing module, a path planning module, a control module, and an execution module. The sensor module collects robot motion parameters and tunnel environment information; the data processing module fuses data from lidar and inertial measurement unit to achieve high-precision positioning; the path planning module generates a full-coverage square wave detection path based on the tunnel structure; the control module uses a model predictive control algorithm combined with real-time positioning information to generate control commands; and the execution module drives the robot to move along the path to complete the invert arch inspection. This invention effectively solves the problems of missing GPS signals and large environmental interference in tunnels, achieving high-precision navigation and positioning with a trajectory tracking error within ±0.06m and a heading angle error within ±1°, thus improving inspection efficiency and comprehensiveness.

Description

Technical Field

[0001] This invention relates to the fields of tunnel engineering inspection technology and navigation and positioning technology, and in particular to a navigation and positioning system and method for a tunnel invert inspection robot based on multi-technology fusion. It is specifically designed for tunnel invert inspection robots and can achieve high-precision navigation and positioning in complex tunnel environments, ensuring efficient and accurate inspection operations. Background Technology

[0002] As a crucial component of the tunnel structure, the construction quality of the tunnel invert directly affects the overall stability and operational safety of the tunnel. A tunnel invert inspection robot is a key piece of equipment for achieving automated invert quality inspection, and accurate navigation and positioning are prerequisites for the robot to complete comprehensive and detailed inspections according to a preset trajectory. However, the unique characteristics of the tunnel environment present numerous challenges to navigation and positioning: the presence of numerous steel bars, pipelines, and other metal structures within the tunnel can severely interfere with or even block GPS signals, rendering traditional GPS-based positioning methods ineffective; the narrow, dimly lit interior space of the tunnel, along with potential water accumulation and dust, can affect the sensor's accuracy in perceiving the environment; and the uneven surface of the invert causes the robot to experience bumps during movement, leading to increased errors in position and attitude measurement.

[0003] Currently, navigation and positioning technologies used in tunnel invert inspection robots suffer from numerous shortcomings. Some methods rely on single sensors for positioning, such as inertial measurement units (IMUs), which lead to a sharp decline in positioning accuracy over time due to accumulated errors. Some path planning methods fail to adequately consider the elongated structure of tunnel inverts and the specific inspection requirements, resulting in paths with either blind spots or low efficiency. Furthermore, data processing struggles to effectively integrate data from multiple sensors to eliminate noise and errors, leading to poor robot positioning stability. These problems severely restrict the inspection accuracy and operational efficiency of tunnel invert inspection robots, failing to meet the high-quality inspection requirements of tunnel engineering.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a navigation and positioning system and method for a tunnel invert inspection robot based on multi-technology fusion. Addressing the unique environment and requirements of tunnel invert inspection, this invention integrates model predictive control (MPC), square wave path design, and Kalman filtering techniques to solve problems such as signal interference, unreasonable path planning, and low positioning accuracy faced by the tunnel invert inspection robot during navigation and positioning. This achieves high-precision and stable navigation and positioning of the robot in the tunnel invert area, ensuring the comprehensiveness and accuracy of the inspection operation.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A navigation and positioning system for a tunnel arch inspection robot based on multi-technology fusion, comprising: The sensor module is used to collect the robot's motion parameters and real-time environmental information around the tunnel; The data processing module is used to receive data from the lidar and inertial measurement unit, and output high-precision positioning information in real time using data fusion algorithms; The path planning module is used to generate the optimal full-coverage square wave detection path based on the actual tunnel scenario and using square wave path parameters suitable for tunnel invert arch detection. The control module is used to employ model predictive control algorithms, based on the planned path and real-time positioning information, to calculate control in real time using the control algorithm, and to send speed information to the execution module via commands. The execution module is used to drive the robot to move along the planned path according to the control instructions sent by the control module, thereby achieving precise trajectory tracking.

[0008] Optionally, the sensor module includes at least one of an inertial measurement unit, a wheel speed encoder, a lidar, and a vision sensor; the motion parameter information includes position, velocity, acceleration, angular acceleration, and angle.

[0009] Optionally, the data processing module uses a motion model based on constant acceleration and angular acceleration to perform point-by-point motion correction, and achieves data fusion between the lidar and the inertial measurement unit through a registration method that scans to the map.

[0010] Optionally, the path planning module adopts a parametric square wave path design, and the path parameters include the distance x along the tunnel direction, the tunnel width y, the distance between acquisition points m, and the distance between acquisition lines n.

[0011] Optionally, the model predictive control algorithm of the control module uses the robot's state variables. Input from robots Based on this, the optimal control sequence is generated by optimizing the objective function; The state-space equation of the robot is: .

[0012] A navigation and positioning method for a tunnel arch inspection robot based on multi-technology fusion includes the following steps: The robot's motion status and tunnel environment information are collected through sensor modules; The data processing module fuses the data from the lidar and the inertial measurement unit to obtain the robot's real-time positioning information; A square wave detection path is generated based on the tunnel arch structure; The model predictive control algorithm is used to generate control commands by combining real-time positioning information and planned paths; The robot is driven to move along the path by the execution module to complete the arch detection.

[0013] Optionally, the data processing steps include: Constructing continuous-time trajectories for motion correction; A nonlinear geometric observer is used to generate a complete state estimate of the robot. Perform scan-to-map registration to reduce computational load.

[0014] Optionally, in the path generation step, the state of the path points is represented as follows: θ is set according to the tunnel direction to ensure that the detection sensor is directly facing the surface of the invert arch; when generating the path, the linear parametric equation is used.

[0015] Determine the coordinates of the collection points on the straight line segment. For path inflection points, use a differential chassis so that the robot can rotate in place and pass through the path inflection points.

[0016] Optionally, the objective function of the model predictive control algorithm is:

[0017] in, This is the terminal state. Let Q be the reference state at time k, Q be the state weight matrix, R be the control weight matrix, and S be the terminal weight matrix.

[0018] Optionally, during actual operation, the robot's trajectory tracking error is controlled within ±0.06m, and the heading angle error is controlled within ±1°.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention addresses the unique environment of tunnel invert arch inspection by organically integrating MPC, square wave path design, and laser-inertial navigation technology. This effectively solves problems such as GPS signal loss and significant environmental interference within tunnels, enabling high-precision navigation and positioning of the tunnel invert arch inspection robot. The positioning error can be controlled within a small range, meeting the accuracy requirements for invert arch inspection.

[0020] 2. The square wave path design is suitable for tunnel inverts, fully considering the narrow structure and inspection requirements of tunnel inverts. It ensures that the robot can perform comprehensive inspection of the invert surface without blind spots, and the reasonable path planning improves the efficiency of the inspection operation. 3. Laser-inertial navigation technology successfully integrates data from multiple sensors, effectively suppressing noise and interference in the tunnel environment, improving the stability and accuracy of robot state estimation, and providing a reliable state basis for the robot's precise navigation. 4. The system architecture is specifically designed for tunnel arch inspection robots. Each module has a clear division of labor and works together efficiently. It can adjust the robot's motion state in real time according to changes in the tunnel environment, ensuring that the robot can safely and stably complete inspection tasks in complex tunnel environments. It has strong practicality and promotional value. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the navigation and positioning system for a tunnel arch detection robot based on multi-technology fusion, provided in an embodiment of the present invention.

[0023] Figure 2 A flowchart of a navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion, provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The purpose of this invention is to provide a navigation and positioning system and method for a tunnel invert inspection robot based on multi-technology fusion. Addressing the unique environment and requirements of tunnel invert inspection, this invention integrates model predictive control (MPC), square wave path design, and Kalman filtering techniques to solve problems such as signal interference, unreasonable path planning, and low positioning accuracy faced by the tunnel invert inspection robot during navigation and positioning. This achieves high-precision and stable navigation and positioning of the robot in the tunnel invert area, ensuring the comprehensiveness and accuracy of the inspection operation.

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Example 1: Application of navigation and positioning robot for tunnel arch inspection: Sensor Configuration and Data Acquisition: The tunnel invert inspection robot is equipped with a high-precision inertial measurement unit (IMU, sampling frequency 100Hz), wheel speed encoder (resolution 0.01m), 3D LiDAR (scanning range 0.2m-100m, angular resolution 0.18°@10Hz), and an industrial camera (resolution 640x480@20FPS). During tunnel inspection operations, the IMU measures the robot's acceleration and angular velocity in real time, the wheel speed encoder records the robot's travel distance to calculate its speed, the LiDAR scans the tunnel walls and invert surface to acquire environmental contour information, and the industrial camera captures images of the invert surface for auxiliary positioning and inspection. All sensor data is transmitted via a data bus. Data processing and state estimation: The data processing module fuses sensor data, and the robot's positioning accuracy can reach ±0.06m, with a heading angle error of ±1°.

[0028] Path planning parameters and generation: For a tunnel arch section with a length of 100m and a width of 8m, the path planning module is set with the following parameters: x=100, y=8, m=0.5, n=1.

[0029] Control and Execution Process: The control module employs the MPC algorithm for trajectory tracking control, setting the prediction interval N=10, the state weight matrix, and the control weight matrix. Based on the reference path generated by the path planning module and the robot state output by the data processing module, the MPC algorithm calculates the optimal control variables (linear velocity and angular velocity) by solving a quadratic programming problem. The execution module drives the robot's drive motors and steering mechanism according to the control commands, enabling the robot to move along the planned path. In actual operation, the robot's trajectory tracking error can be controlled within ±0.06m, accurately detecting the tunnel invert arch according to the preset path and comprehensively acquiring the quality information of the invert arch surface.

[0030] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0031] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A navigation and positioning system for a tunnel arch inspection robot based on multi-technology fusion, characterized in that, include: The sensor module is used to collect the robot's motion parameters and real-time environmental information around the tunnel; The data processing module is used to receive data from the lidar and inertial measurement unit, and output high-precision positioning information in real time using data fusion algorithms; The path planning module is used to generate the optimal full-coverage square wave detection path based on the actual tunnel scenario and using square wave path parameters suitable for tunnel invert arch detection. The control module is used to employ model predictive control algorithms, based on the planned path and real-time positioning information, to calculate control in real time using the control algorithm, and to send speed information to the execution module via commands. The execution module is used to drive the robot to move along the planned path according to the control instructions sent by the control module, thereby achieving precise trajectory tracking.

2. The tunnel arch inspection robot navigation and positioning system based on multi-technology fusion as described in claim 1, characterized in that, The sensor module includes at least one of an inertial measurement unit, a wheel speed encoder, a lidar, and a vision sensor; the motion parameter information includes position, velocity, acceleration, angular acceleration, and angle.

3. The tunnel arch inspection robot navigation and positioning system based on multi-technology fusion according to claim 1, characterized in that, The data processing module uses a motion model based on constant acceleration and angular acceleration to perform point-by-point motion correction, and achieves data fusion between the lidar and the inertial measurement unit through a registration method that scans to the map.

4. The tunnel arch inspection robot navigation and positioning system based on multi-technology fusion according to claim 1, characterized in that, The path planning module adopts a parametric square wave path design, and the path parameters include the distance x along the tunnel direction, the tunnel width y, the distance between acquisition points m, and the distance between acquisition lines n.

5. The tunnel arch inspection robot navigation and positioning system based on multi-technology fusion according to claim 1, characterized in that, The model predictive control algorithm of the control module uses the robot's state variables. Input from robots Based on this, the optimal control sequence is generated by optimizing the objective function; The state-space equation of the robot is: 。 6. A navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion, characterized in that, Includes the following steps: The robot's motion status and tunnel environment information are collected through sensor modules; The data processing module fuses the data from the lidar and the inertial measurement unit to obtain the robot's real-time positioning information; A square wave detection path is generated based on the tunnel arch structure; The model predictive control algorithm is used to generate control commands by combining real-time positioning information and planned paths; The robot is driven to move along the path by the execution module to complete the arch detection.

7. The navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion according to claim 6, characterized in that, The steps involved in data processing include: Constructing continuous-time trajectories for motion correction; A nonlinear geometric observer is used to generate a complete state estimate of the robot. Perform scan-to-map registration to reduce computational load.

8. The navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion according to claim 6, characterized in that, In the path generation process, the state of the path points is represented as follows: θ is set according to the tunnel direction to ensure that the detection sensor is directly facing the surface of the invert arch; when generating the path, the linear parametric equation is used. Determine the coordinates of the collection points on the straight line segment. For path inflection points, use a differential chassis so that the robot can rotate in place and pass through the path inflection points.

9. The navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion according to claim 6, characterized in that, The objective function of the model predictive control algorithm is: in, This is the terminal state. Let Q be the reference state at time k, Q be the state weight matrix, R be the control weight matrix, and S be the terminal weight matrix.

10. The navigation and positioning method for a tunnel arch detection robot based on multi-technology fusion according to claim 6, characterized in that, In actual operation, the robot's trajectory tracking error is controlled within ±0.06m, and the heading angle error is controlled within ±1°.

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