Editorial Feature

Automatic Inspection of Belt Conveyors in Mining Using a Mobile Inspection Platform Based on Autonomous UGV

Belt conveyors transport raw materials over long distances within mines (up to several dozens of km). They are usually in operation nearly 24 hours a day, often in hostile environments, such as underground mines.

Belt conveyors are obviously subjected to faster decomposition processes. They should be inspected on a regular basis according to mining law and enterprise internal regulations.

Normally, SCADA systems are used to monitor some conveyor elements (drive units) 24 hours a day. Maintenance personnel inspects the rest of the conveyor. This necessitates a visual inspection of all elements along the conveyor to look for anomalies (see Figure 1).

Conveyor belt route inspection in traditional form.

Figure 1. Conveyor belt route inspection in traditional form. Image Credit: Szrek, et al., 2022

Conveyors, on the other hand, require a lot of power and are spread out over a large area. Mining companies are looking for new ways to keep conveyors in good working order. Several research groups have already developed inspection robots for mining applications.

However, due to issues such as localization and navigation, using inspection robots in the mine is difficult. This has been tested using unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), and walking robots. Underground mines are not suitable for UAVs due to the powder/dust conditions.

Furthermore, when compared to UGVs, legged robots are far more complex. As a result, a UGV appears to be the best option for application in terms of payload, speed, battery consumption, and stability.

Researchers have proposed a mobile inspection platform based on autonomous UGVs to reduce the presence of humans in a harsh mining environment. It is equipped with a variety of sensors (RGB image, gas sensor, and sound) and can collect nearly the same data as a maintenance vehicle. They also highlight some research challenges, particularly for planned validation in underground mines.

Methodology

The goal of this research is to conduct automated inspection missions to aid in the daily maintenance of mine belt conveyors. Because field experiments in deep mines are difficult to organize, are expensive, and there is a finite amount of time to test/validate the system, a belt conveyor test rig intended for research purposes (see Figure 2) was used in the study.

Belt conveyor system in the lab.

Figure 2. Belt conveyor system in the lab. Image Credit: Szrek, et al., 2022

As a reference for experiments, a point cloud map of the lab was gathered using a high-resolution stationary geodesic LIDAR (Figure 3).

Reference point cloud of the inspected area.

Figure 3. Reference point cloud of the inspected area. Image Credit: Trybała, et al., 2021

After analyzing the requirements of the automatic inspection process, it was split into two parts: inspection planning and inspection execution.

In the first part, the inspection procedure is broken down into simple tasks for the robot, such as defining paths to be pursued and measurements to be taken at specific locations.

The study proposes an automated inspection procedure. Furthermore, a system was created to substantiate the procedure in laboratory conditions, including its components and functions. Figure 4 depicts the facility’s general layout, with the relevant areas for the inspection mission’s planning highlighted.

Inspection area layout

Figure 4. Inspection area layout. Image Credit: Szrek, et al., 2022

The UGV platform components are depicted in Figure 5.

Placement of relevant robot components

Figure 5. Placement of relevant robot components. Image Credit: Szrek, et al., 2022

The mobile platform is made up of a rigid body with wheels attached to it. Figure 6 depicts the space in which the robot will be described.

Global and local coordinates of the mobile robot

Figure 6. Global and local coordinates of the mobile robot. Image Credit: Szrek, et al., 2022

Geometric parameters, as shown in Figure 7, are required for a detailed description of the platform movement. Figure 7 depicts the instantaneous center of rotation around which the platform rotates.

Parameters of the mobile robot

Figure 7. Parameters of the mobile robot. Image Credit: Szrek, et al., 2022

The mobile platform’s kinematic model has natural input parameters, including linear and angular velocity, and is used in algorithms such as trajectory planning. Figure 8 depicts the platform control system as a block diagram.

Control system of the mobile robot

Figure 8. Control system of the mobile robot. Image Credit: Szrek, et al., 2022

In either manual or autonomous mode, the robot can be controlled. The RC transmitter is used to switch between modes remotely. The WiFi network generated by the robot’s computer can also be used to remotely view the robot’s current state (Figure 9).

The remote control panel of mobile robot

Figure 9. The remote control panel of mobile robot. Image Credit: Szrek, et al., 2022

The Robot Operating System (ROS) framework is used to control the platform’s navigation. The system’s architecture is depicted in Figure 10.

Software components of the system and data flow

Figure 10. Software components of the system and data flow. Image Credit: Szrek, et al., 2022

Mapping and localization, autonomous navigation, and motion execution are the three main logical subsystems in the architecture.

Localization and mapping tasks can be solved simultaneously or separately as a Simultaneous Localization And Mapping (SLAM) problem. The researchers decided to take a hybrid approach to this system. They used full SLAM in the planning phase. The “mapping and localization” block illustrates this difference in behavior.

Communication between functional program modules is governed by the MQTT protocol. A request is made to begin data logging when the inspection start point is reached (see Figure 11). The module in charge of registration sends a confirmation message after initializing the measurement system and saving the data. The maximum waiting time for confirmation messages has been set to secure the measurement system.

Inspection algorithm and data flow

Figure 11. Inspection algorithm and data flow. Image Credit: Szrek, et al., 2022

Results and Discussion

Figure 4 depicted a visual description of the mission, and Figure 12 illustrates the map of the area with points of interest.

Map of the area with points of interest

Figure 12. Map of the area with points of interest. Image Credit: Szrek, et al., 2022

During a remotely controlled run, the experiment area was mapped. A 2D LIDAR was used to register area boundaries and obstacles for the laboratory experiments. The image data of the conveyor was recorded to ensure proper communication with the measurement system. The experiment was run several times with two different forward velocity limits. Figure 13 depicts the view of the conveyor belt during the experiments.

An inspection robot during the experiment: (a) general photo; (b) zoom on sensor.

Figure 13. An inspection robot during the experiment: (a) general photo; (b) zoom on sensor. Image Credit: Szrek, et al., 2022

Figure 14 shows the results in time series plots for both slow and fast passes. However, in the majority of the experiments, the path did not deviate from the plan by more than ±0.02 m.

Displacement from the plan and the traveled paths in fast (left) and slow (right) passes.

Figure 14. Displacement from the plan and the traveled paths in fast (left) and slow (right) passes. Image Credit: Szrek, et al., 2022

Figure 15 shows a visual comparison of “fast” paths and the planned path.

Paths: planned and the traveled in passes 1 to 3—whole path and a zoom-in

Figure 15. Paths: planned and the traveled in passes 1 to 3—whole path and a zoom-in. Image Credit: Szrek, et al., 2022

Figure 16 depicts a similar comparison between one of the “slow” passes and one of the “fast” passes. The findings indicate that the paths are repeatable and that lowering the velocity did not improve the quality of the path following significantly.

Comparison of planned and traveled paths in a slow and a fast pass—whole path and a zoom-in

Figure 16. Comparison of planned and traveled paths in a slow and a fast pass—whole path and a zoom-in. Image Credit: Szrek, et al., 2022

The results of all tests indicate that the AMCL localization was implemented well enough for navigation. Figure 17 displays the results of all passes. The results are less repeatable than in the AMCL case.

Comparison of localization from AMCL and odometry—passes 1 to 3 (left) and fast vs. slow (right).

Figure 17. Comparison of localization from AMCL and odometry—passes 1 to 3 (left) and fast vs. slow (right). Image Credit: Szrek, et al., 2022

Using RTABMap, a 3D map was also created during the inspection drive, which is shown in Figure 18. The visualization of the robot’s path during the space experiment is shown in the figure.

3D map made during the autonomous driving with the robot’s path.

Figure 18. 3D map made during the autonomous driving with the robot’s path. Image Credit: Szrek, et al., 2022

Conclusion

The current study proposes an architecture for an automated inspection system of technical infrastructures such as belt conveyors using a mobile robot platform, as well as a procedure for performing such inspections. Experiments in a laboratory environment were used to verify the system’s correct operation. The laboratory tests were conducted to ensure that the system can complete the entire scheduled inspection route without the intervention of an operator.

The concept of the system has been proven by the results of laboratory tests. The next step in the research is to introduce the tests in real-world scenarios. That will necessitate extending the current solution, which relies on a 2D map, to include the ability to move on rough (uneven) terrain, which will necessitate the use of either a full spatial map or multi-layer 2D maps.

Journal Reference:

Szrek, J., Jakubiak, J., Zimroz, R. et al. (2022) A Mobile Robot-Based System for Automatic Inspection of Belt Conveyors in Mining Industry. Energies, 15(1), p. 327. Available Online: https://www.mdpi.com/1996-1073/15/1/327/htm.

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Laura Thomson

Written by

Laura Thomson

Laura Thomson graduated from Manchester Metropolitan University with an English and Sociology degree. During her studies, Laura worked as a Proofreader and went on to do this full time until moving on to work as a Website Editor for a leading analytics and media company. In her spare time, Laura enjoys reading a range of books and writing historical fiction. She also loves to see new places in the world and spends many weekends looking after dogs.

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