A live working auxiliary maintenance robot control system

Abstract

The utility model discloses a live working auxiliary overhaul robot control system, including mechanical arm control module, operation platform control module, main control processing unit and communication module. The utility model discloses have higher precision's positioning and operating ability, can carry out the task accurately according to the prearranged program, improve work efficiency and quality.

Description

Technical Field

[0001] This utility model relates to the field of power maintenance equipment technology, and in particular to a control system for a live-line auxiliary maintenance robot. Background Technology

[0002] The existing maintenance robot control system has the following shortcomings:

[0003] Insufficient control precision: Traditional single-sensor positioning errors reach ±5cm, which cannot meet the requirements of millimeter-level operation;

[0004] Low operational safety: The near-electric operation mode exposes operators to the electric field radiation area and lacks a real-time equipment status early warning mechanism;

[0005] Improper operation: Workers may engage in improper operations during the handling of high-voltage cables, which could easily lead to personal injury and property damage.

[0006] 4. Lack of operational data collection: Traditional maintenance methods are limited to equipment maintenance and lack data collection on equipment and operational conditions during the maintenance process, which cannot provide support for subsequent cable maintenance. Utility Model Content

[0007] To address the aforementioned problems, this utility model provides a live-line working auxiliary maintenance robot control system, which solves the problems of low safety, poor control accuracy, and inconvenient maintenance in the prior art.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by this utility model is: a control system for a live-line working auxiliary maintenance robot, characterized in that: it includes a robotic arm control module, a work platform control module, a main control processor, and a communication module;

[0009] The robotic arm control module includes a multi-sensor fusion unit and an intelligent diagnostic unit. The multi-sensor fusion unit includes a lidar, an IMU sensor, and a vision camera, while the intelligent diagnostic unit includes an insulation resistance sensor and a temperature sensor.

[0010] The communication module includes a 5G communication module and a LoRa communication module, which are connected to the remote control terminal and the LoRa repeater, respectively. The 5G communication module and the LoRa communication module are dynamically switched through a dual-mode communication protocol stack.

[0011] The work platform control module includes a motion planning unit, a drive control module, and an execution module. The execution module includes an insulated work robot driven by a robotic arm drive motor and an insulated work platform driven by a work platform adjustment mechanism.

[0012] It also includes a power supply module that powers the sensor module, main control processor, and drive control module. The main control processor also includes a data storage module and a human-machine interface.

[0013] The communication module establishes signal connections between the robotic arm control module and the main control processor. The robotic arm control module feeds back the collected data to the main control processor, which then processes the data and issues action commands to the work platform control module.

[0014] The multi-sensor fusion unit, combined with the calibration board, performs spatial coordinate system transformation; and Kalman filtering is used to assist in the localization of feature points.

[0015] The insulation resistance sensor and temperature sensor are combined to build redundant acquisition channels, and edge computing nodes are deployed to detect and model resistance changes and set up corresponding early warning mechanisms.

[0016] As can be seen from the above description of the structure of this utility model, compared with the prior art, this utility model has the following advantages:

[0017] It has monitoring and early warning functions, and can detect potential problems in power transmission lines and send early warning information to relevant personnel.

[0018] It possesses high-precision positioning and operational capabilities, enabling it to accurately execute detection tasks according to preset procedures, thereby improving work efficiency and quality. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an undue limitation of the present invention. In the drawings:

[0020] Figure 1 This is a schematic diagram of the control system structure of this utility model;

[0021] Figure 2 This is a schematic diagram of a partial power supply structure of this utility model. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this utility model clearer, the present utility model will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present utility model and are not intended to limit the present utility model.

[0023] Example

[0024] refer to Figure 1 and Figure 2A control system for a live-line working auxiliary maintenance robot includes a robotic arm control module, a work platform control module 1, a main control processor 2, and a communication module 3.

[0025] The robotic arm control module includes a multi-sensor fusion unit 4 and an intelligent diagnostic unit 5. The multi-sensor fusion unit includes a lidar, an IMU sensor, and a vision camera, while the intelligent diagnostic unit includes an insulation resistance sensor and a temperature sensor.

[0026] Communication module 3 includes a 5G communication module and a LoRa communication module, which are connected to the remote control terminal and the LoRa repeater, respectively. The 5G communication module and the LoRa communication module are dynamically switched through a dual-mode communication protocol stack.

[0027] The work platform control module 1 includes a motion planning unit, a drive control module, and an execution module. The execution module includes an insulated work robot driven by a robot arm drive motor and an insulated work platform driven by a work platform adjustment mechanism.

[0028] The communication module 3 establishes a signal connection between the robotic arm control module and the main control processor 2. The robotic arm control module feeds back the collected data to the main control processor 2, and the main control processor processes the data and issues action commands to the work platform control module.

[0029] The multi-sensor fusion unit 4, combined with the calibration plate, performs spatial coordinate system transformation; and uses Kalman filtering to assist in the localization of feature points.

[0030] It is also equipped with a power supply module, which provides power to the multi-sensor fusion unit 4, the main control processor 2 and the drive control module.

[0031] The main control processor 2 is also equipped with a data storage module and a human-machine interface.

[0032] The data from the insulation resistance sensor and the temperature sensor are combined to build a redundant acquisition channel, and edge computing nodes are deployed to detect sudden changes in resistance and to set up a corresponding early warning mechanism.

[0033] When using it, it needs to be combined with relevant algorithms, as follows:

[0034] The fusion of the multi-sensor fusion unit 4: adopts a hardware synchronous triggering mechanism to unify the time reference of the lidar, IMU sensor and visual camera, and completes the spatial coordinate system transformation in combination with the calibration board; deploys a dynamic compensation algorithm based on Kalman filtering to eliminate the influence of the cumulative error of the IMU sensor on the point cloud registration, realize feature point assisted localization, and identify the three-dimensional spatial position of live wires, insulators and surrounding obstacles.

[0035] Data Acquisition by the Intelligent Diagnostic Unit: A four-wire insulation resistance measurement circuit is designed, and redundant acquisition channels are constructed in conjunction with a PT100 temperature sensor, with a sampling frequency ≥1kHz. Edge computing nodes are deployed, and wavelet transform is used to reduce noise in the temperature signal. Algorithms are used to detect resistance mutation events. For example, an LSTM time-series prediction model is constructed to reconstruct the acquired data into a three-dimensional model of the work scenario, marking the surface contours and safety boundaries of energized objects: conductors and insulators, providing a basis for path planning. Historical temperature, resistance data, and operating parameters are input, and the equipment health index is output. A graded alarm mechanism is developed, and a cloud-based expert system performs root cause analysis.

[0036] The data collected by the multi-sensor fusion unit 4 and the intelligent diagnostic unit is fed back to the main control processor through the communication module. After processing the data, the main control processor displays the data digitally and performs data reasoning and suggestions on the human-machine interaction platform. The suggestions include judging the fault point and the standardization of maintenance personnel's operation. The main control processor will also issue instructions to the operation platform control module 1 to allow the execution module to perform some basic checks and maintenance, such as using a camera or lidar to lock the wire to be processed, and calculating the wire position, angle and insulation layer thickness through algorithms.

[0037] The robotic arm uses a collaborative positioning system. Driven by a motor, the robotic arm approaches the conductor and adjusts the clamping force via a force feedback sensor to ensure conductor stability and prevent damage to the insulation. It then operates on an insulated work platform driven by an adjustment mechanism. A bidirectional lead screw and left / right turning nuts are used to simultaneously clamp the conductor. A centering motor adjusts the clamping plate spacing to accommodate different wire diameters, ensuring the conductor remains straight.

[0038] In summary, the advanced image recognition algorithm can be used for live detection, accurately analyze and determine single-point fault modes on the line, predict and suggest the work scenarios and postures of operators in advance, and determine whether the operators' work is standardized, whether their posture is safe, and whether their safety protection equipment is complete. Once an abnormality is detected, an early warning signal will be issued immediately to ensure the safety of the operators.

Claims

1. A control system for a live-line working auxiliary maintenance robot, characterized in that: It includes a robotic arm control module, a work platform control module (1), a main control processor (2), and a communication module (3); The robotic arm control module includes a multi-sensor fusion unit (4) and an intelligent diagnostic unit (5). The multi-sensor fusion unit includes a lidar, an IMU sensor, and a vision camera. The intelligent diagnostic unit includes an insulation resistance sensor and a temperature sensor. The communication module (3) includes a 5G communication module and a LoRa communication module, which are respectively connected to the remote control terminal and the LoRa repeater. The 5G communication module and the LoRa communication module are dynamically switched through a dual-mode communication protocol stack. The work platform control module (1) includes a motion planning unit, a drive control module and an execution module. The execution module includes an insulated work robot driven by a robot arm drive motor and an insulated work platform driven by a work platform adjustment mechanism. The communication module (3) establishes signal connection between the robotic arm control module and the main control processor (2). The robotic arm control module feeds back the collected data to the main control processor (2), and the main control processor issues action commands to the work platform control module after data processing.

2. The control system for the live-line working auxiliary maintenance robot according to claim 1, characterized in that: The multi-sensor fusion unit (4) combines the calibration plate to transform the spatial coordinate system; and uses Kalman filtering to assist in the positioning of feature points.

3. The control system for the live-line working auxiliary maintenance robot according to claim 1, characterized in that: It is also equipped with a power supply module, which provides power to the multi-sensor fusion unit (4), the main control processor (2) and the drive control module.

4. The control system for the live-line working auxiliary maintenance robot according to claim 1, characterized in that: The main control processor (2) is also equipped with a data storage module and a human-computer interaction interface.

5. The control system for live-line working auxiliary maintenance robot according to claim 1, characterized in that: The data from the insulation resistance sensor and the temperature sensor are combined to construct a redundant acquisition channel, and edge computing nodes are deployed to detect and model resistance changes, with corresponding early warning mechanisms.

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