In the realm of subsea tunnel infrastructure, the complexity and diversity of facilities present significant challenges for maintenance and inspection. Traditional manual monitoring methods often fall short due to human error, leading to inaccurate assessments of equipment conditions. To address these issues, our research team has embarked on a project to explore the integration of AI robots into automatic monitoring systems. This study focuses on developing a robust model for AI robot deployment, leveraging fusion recognition technologies to enhance data accuracy and operational efficiency. We aim to demonstrate how AI robots can autonomously adapt to the dynamic environment of subsea tunnels, providing real-time, reliable monitoring solutions. The following sections detail the construction of our automatic monitoring model, analyze the performance of AI robots in practical scenarios, and discuss the implications of our findings for future applications.
The development of an automatic monitoring model for AI robots involves several critical components, each designed to ensure seamless operation in subsea tunnel environments. Firstly, the sensor system is integral for collecting real-time data on facility parameters such as temperature, humidity, vibration, and light intensity. This data is then processed by a data acquisition system, which converts raw inputs into actionable insights. Communication systems, utilizing fiber-optic networks and wireless technologies, enable rapid data transmission and sharing, ensuring timely monitoring responses. Additionally, a display system presents the operational status of tunnel facilities through visualizations like 3D models, value maps, and trend graphs, thereby improving monitoring efficacy. To facilitate autonomous adjustments in speed and posture based on environmental changes, we designed a track-mounted platform for the AI robot. This platform supports the robot’s control mechanisms, allowing it to navigate straight, curved, and sloped tracks with precision. Key considerations during model optimization included cost-effectiveness,智能化程度, response speed, real-time monitoring capabilities, and protective adjustment mechanisms. Below, we outline the mathematical foundations of our model, which govern the AI robot’s motion and power dynamics.
One of the core aspects of our model is balancing the forces acting on the AI robot during operation. The equilibrium between driving force and motion resistance is crucial for maintaining stable movement and accurate data collection. This relationship is expressed by the following equation, which ensures that the AI robot can adjust its trajectory based on sensor feedback:
$$ \frac{T_i \eta}{r} = mgf \cos \beta + \frac{C_D A \mu}{21.25} + \frac{\delta m g}{g} + mg \sin \beta $$
Here, \( T \) represents the motor torque of the AI robot, \( i \) denotes the minimum unit in the \( i \)-th row of the inspection area’s coordinate system, \( \eta \) is the number of minimum units in the inspection area, \( r \) is the wheel radius, \( m \) is the mass of the AI robot, \( g \) is gravitational acceleration, \( f \) is the friction coefficient, \( \beta \) is the wheel rolling coefficient, \( C_D \) is the air resistance coefficient, \( A \) is the frontal area, \( \mu \) is the operational speed, and \( \delta \) is the rotational mass coefficient. This equation allows the AI robot to dynamically control its driving force, ensuring reliable monitoring in varying tunnel conditions.
Next, we focus on the power balance between motor output and motion resistance, which is essential for optimizing the AI robot’s energy consumption and extending its operational duration. The following equation defines this balance, enabling precise control over the driving force:
$$ P \eta = \left( \frac{mgf \cos \beta}{3,600} + \frac{C_D A \mu^2}{76,140} + \frac{mg \sin \beta}{3,600} + \frac{\delta m g}{3,600 g} \mu \right) $$
In this context, \( P \) represents the power output, and the other variables retain their previous meanings. By applying this equation, we can fine-tune the AI robot’s control strategies to minimize power usage while maintaining performance.
To assess the motor power during operation, we utilize the maximum climb indicator, as described by the following equation:
$$ P_i = \frac{1}{\eta} \left( \frac{mgf \mu}{3,600} + \frac{mg i \mu}{3,600} \right) $$
Here, \( P_i \) denotes the motor power during monitoring, and \( i \) refers to the slope gradient. This calculation helps in evaluating the AI robot’s ability to handle inclined tracks commonly found in subsea tunnels.
For monitoring the drive motor power at maximum speed, we employ the following equation:
$$ P_\upsilon = \frac{1}{\eta} \left( \frac{mgf \mu}{3,600} \right) $$
Where \( P_\upsilon \) represents the drive motor power. This ensures that the AI robot operates within safe power limits during high-speed monitoring tasks.
Additionally, the maximum motor power during acceleration is determined using the equation below:
$$ P_a = \frac{1}{3,600 \times \eta} \times \left( mgf + \frac{C_D A \mu^2}{21.15} + \delta m a \right) \mu $$
Here, \( P_a \) is the acceleration-driven maximum power, and \( a \) is the acceleration value. This allows us to verify that the peak power remains within safe thresholds, as per the following inequality:
$$ P_{\text{max}} \geq \max(P_i, P_\upsilon, P_a) $$
To enhance the AI robot’s capabilities, we incorporated a high-performance recognition processing module. This module utilizes deep learning and artificial intelligence technologies to rapidly analyze captured images, improving the visualization of monitoring results. For instance, by installing specialized cameras on the AI robot, we can capture images of tunnel targets and extract data features through computer system computations. Given the unique environment of subsea tunnels, we applied image morphology techniques to diagnose facility anomalies such as expansion, cracks, corrosion, and deformation. This approach addresses common issues like data distortion, blurring, and local deviations in conventional monitoring. Our AI robot’s monitoring process, supported by fusion recognition technology, involves visual servoing, focus servoing, and exposure servoing to ensure data accuracy and algorithmic reliability. The following table summarizes the key parameters and their roles in the AI robot’s automatic monitoring system:
| Parameter | Description | Role in AI Robot Operation |
|---|---|---|
| \( T \) | Motor Torque | Controls driving force for movement |
| \( \mu \) | Operational Speed | Determines motion velocity and response time |
| \( \beta \) | Wheel Rolling Coefficient | Influences traction on varied surfaces |
| \( C_D \) | Air Resistance Coefficient | Affects energy efficiency in tunnel environments |
| \( \delta \) | Rotational Mass Coefficient | Impacts acceleration and deceleration |
To validate the feasibility of using AI robots with fusion recognition technology for automatic monitoring, we conducted experiments focused on electrical facilities in subsea tunnels. We deployed a functional prototype of the AI robot on a track-mounted platform and evaluated its performance under various conditions. Speed and position data were monitored using precision instruments like velocimeters and vernier calipers to detect any deviations in control. We also tested the AI robot’s turning capabilities on curved tracks with radii of 1,000 mm, 1,200 mm, and 1,500 mm, as well as its climbing performance on slopes of 10°, 15°, and 20°. The results demonstrated the AI robot’s ability to navigate these challenges without issues such as jamming, interference, delays, or gear disengagement. The following sections present detailed analyses of the AI robot’s horizontal movement, turning, and climbing performance, supported by data tables and equations.
In assessing the AI robot’s horizontal movement reliability, we tested it at different speeds and conducted repeated positioning tests. The data below illustrates the average error rates and positioning errors observed during these trials. For instance, at a speed of 0.1 m/s, the average error was 3.28%, while at 1.0 m/s, it increased to 5.03%. Despite this variation, all error rates remained within acceptable limits for subsea tunnel monitoring. The repeated positioning errors averaged between 2.3 mm and 6.1 mm across speeds from 0.1 m/s to 1.5 m/s, never exceeding 10 mm, which meets design requirements. The following table compiles the horizontal movement speed test data:
| Speed (m/s) | Average Error Rate (%) | Description |
|---|---|---|
| 0.1 | 3.28 | Low speed with minimal error |
| 0.5 | 4.12 | Moderate speed showing slight increase |
| 1.0 | 5.03 | High speed within acceptable range |
The repeated positioning error data is summarized in the table below:
| Speed (m/s) | Average Positioning Error (mm) | Implication for AI Robot |
|---|---|---|
| 0.1 | 2.3 | High precision for detailed inspections |
| 0.5 | 3.9 | Balanced speed and accuracy |
| 1.5 | 6.1 | Effective for rapid surveys |
These findings indicate that the AI robot can maintain precise control over its movement, adapting to the tunnel’s environmental changes without compromising data integrity.
For turning performance, we evaluated the AI robot on tracks with different radii. The results showed that the AI robot smoothly navigated all tested radii, including the minimum of 1,000 mm, without any control abnormalities. This highlights the effectiveness of the drive system in handling curved paths, which are common in subsea tunnels. Similarly, climbing tests on slopes of 10°, 15°, and 20° confirmed that the AI robot could ascend without slipping, thanks to the sufficient driving force provided by its motor. The data below encapsulates the turning and climbing performance:
| Turning Radius (mm) | Performance Outcome | Remarks on AI Robot |
|---|---|---|
| 1,000 | Stable passage | No delays or disengagements |
| 1,200 | Smooth operation | Consistent with design specs |
| 1,500 | Efficient navigation | Ideal for wide curves |
Climbing performance data:
| Slope Angle (degrees) | Performance Outcome | AI Robot Capability |
|---|---|---|
| 10 | Stable ascent | Adequate for gentle inclines |
| 15 | Consistent climb | Handles moderate slopes |
| 20 | No slippage | Suitable for steep sections |
Throughout our experiments, the AI robot demonstrated exceptional adaptability, driven by the fusion recognition technology that enabled real-time data processing and decision-making. For example, the AI robot could quickly identify anomalies in electrical facilities, such as overheating or structural wear, using its integrated sensors and deep learning algorithms. This capability is crucial for preventive maintenance in subsea tunnels, where early detection of issues can avert major failures. The following equation exemplifies how the AI robot’s power management aligns with its operational demands, ensuring efficiency during monitoring tasks:
$$ P_{\text{total}} = \frac{1}{\eta} \left( \frac{mgf \mu}{3,600} + \frac{C_D A \mu^3}{76,140} + \frac{mg \sin \beta \mu}{3,600} \right) $$
Here, \( P_{\text{total}} \) represents the total power consumption, which is optimized to extend the AI robot’s battery life during prolonged inspections.
In conclusion, our research underscores the transformative potential of AI robots in automating the monitoring of subsea tunnel facilities. By integrating fusion recognition technologies, we have developed a model that enhances the AI robot’s speed control, trajectory accuracy, turning agility, and climbing prowess. The experimental data confirm that the AI robot meets the diverse demands of tunnel environments, providing reliable, real-time data for maintenance decisions. Although our study relied on functional prototype tests rather than field trials, the results offer a solid foundation for future implementations. We recommend further research involving real-world deployments to validate these findings and refine the AI robot’s capabilities. Ultimately, the adoption of AI robots in subsea tunnel monitoring can significantly reduce operational costs, improve safety, and ensure the longevity of critical infrastructure. As technology advances, we anticipate that AI robots will become indispensable tools in the realm of automated facility management, driven by continuous innovations in artificial intelligence and machine learning.