Distance detection and turning path design method for monorail crane equipment
By installing sensors and data processing units on the monorail lifting equipment, generating three-dimensional models and designing turning paths, the problem of difficult path selection in complex environments is solved, and safe and efficient path planning is achieved.
Patent Information
- Application Number
- CN202510078354.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-17
- Publication Date
- 2025-02-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When monorail crane equipment operates in a narrow or obstacle-rich environment, it has collision risks and difficulty in path selection. The existing technology lacks automated and intelligent path planning.
By installing sensors on a single-rail lifting equipment, the distance between obstacles around the front of the vehicle is detected in real time, a three-dimensional model is generated using a data processing unit to design a reliable turning path, and adjust the speed of the two wheels to make the torque consistent, ensuring the stability of the turning process. reliable.
It realizes safe and efficient path planning of single-rail crane equipment in complex environments, reduces collision risks, and improves transportation efficiency and safety.
Smart Images
Figure CN119461058A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of path planning for monorail crane equipment, and more specifically, to a distance detection and turning path design method for monorail crane equipment. Background Art
[0002] Monorail crane equipment has the advantages of high reliability, strong transportation capacity, low cost, easy installation, small turning radius, etc. It can achieve direct transportation and reduce the number of transfers. It is widely used in industrial production, logistics transportation, coal mine underground equipment and material transportation and other fields. However, when it operates in a narrow environment or an environment with many obstacles, there is a risk of collision and difficulty in path selection. Existing solutions mostly rely on the operator's experience and lack automated and intelligent path planning.
[0003] Some mining areas have begun to try out the monorail crane remote control system, marking that underground auxiliary transportation has officially entered the "driverless" era. Automation and intelligent requirements have opened up new paths to improve transportation efficiency and safety.
[0004] In summary, the current status of monorail crane equipment in underground mines shows a positive development trend, especially in automation, intelligence, battery technology and information transmission. However, it also faces the challenge of further improving efficiency and safety.
[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention
[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a distance detection and turning path design method for monorail crane equipment, which uses sensors to identify the distance of obstacles around the monorail crane head, calculates the real-time distance in real time, designs a reliable turning radius and speed requirements, and at the same time actually matches the dual-wheel speed ratio to ensure stability and reliability during the turning process, thereby solving the problems raised in the above-mentioned background technology.
[0007] To achieve the above object, the present invention provides the following technical solutions: A distance detection and turning path design method for a monorail crane device comprises the following steps: Step S1, installing sensors on the monorail crane and transmitting data collected by the sensors to a data processing unit in real time, wherein the data processing unit identifies obstacles and calculates distances; Step S2, the data processing unit uses data fusion technology to process and generate a three-dimensional model of the surrounding environment of the monorail crane equipment; Step S3, establishing a turning path model according to the three-dimensional model, and outputting an optimal turning path; Step S4, by sensing the turning position and turning angle in advance, the rotation speeds of the two wheels are controlled so that the output torques of the two wheels are consistent; The formula of the optimal turning path model is: ; Where: is the total cost function, which evaluates the total “cost” that the monorail crane needs to pay during the turning process. is the distance that the monorail crane equipment needs to travel during the turning process, wherein: For all the key discrete points of the monorail crane during the turning process, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, is the energy consumed by the monorail crane during the turning process, wherein: For the monorail crane equipment The speed of a discrete point, For the monorail crane equipment The acceleration of a discrete point, To adjust the energy consumed to the total cost function The coefficient of influence, is the turning radius of the monorail crane during the turning process, satisfy , To adjust the turning radius to the total cost function The coefficient of influence.
[0008] As a further solution of the present invention, a sensor is installed on the monorail crane to achieve 360-degree all-round distance detection, and the collected data is transmitted to the data processing unit in real time, including the following specific contents: In the step S1, a sensor is installed on the monorail crane, and the collected data is transmitted to the data processing unit in real time, and the data processing unit identifies obstacles and calculates distances, including: through the sensor installed on the monorail crane, 360-degree real-time obstacle detection around the monorail crane is achieved. In order to optimize the recognition effect of obstacles around the monorail crane head, multiple binocular vision cameras are installed on the monorail crane to achieve 360-degree all-round real-time monitoring through multi-view coverage, and the collected image data is transmitted to the data processing unit in real time. A high-definition wide-angle binocular vision camera is installed at the front center of the monorail crane to capture the panoramic view in front of the equipment and the image of obstacles at a long distance. The installation height of the binocular vision camera is higher than the front structure of the monorail crane to avoid visual blind spots. In addition, a set of wide-angle binocular vision cameras are installed on the left and right sides of the monorail crane respectively, which are suitable for capturing environmental changes on the left and right sides at close range, and improving the ability to identify potential obstacles on the side of the monorail crane. The detection unit judges and monitors the detection of obstacles and turning safety during the operation of the monorail crane. The formula for the judgment of the detection unit is: ,in, It is the safety detection score, which is used to judge the safety of the current operating status of the monorail crane. , indicating that there is a potential danger in the path, and it is necessary to update the optimal turning path model and recalculate the optimal turning path. , the monorail crane equipment operates safely in the current state, is the distance between the monorail crane and the obstacle, In order to adjust the influence of the distance between the monorail crane and the obstacle on the safety detection score, To adjust the impact of turning angle and turning radius on safety detection scores, is the relative speed between the monorail crane and the obstacle, To adjust the impact of the relative speed between the device and the obstacle on the safety detection score, It is the minimum safe distance that needs to be maintained between the monorail and obstacles during operation. It is used to adjust the influence of the ratio of the minimum safety distance to the obstacle distance on the safety detection score. At the same time, a short-range infrared auxiliary camera is arranged at the front, rear and left and right corners of the monorail crane to enhance the perception of close-range environments in low light or special conditions, especially for the recognition of close-range obstacles when turning and reversing.
[0009] The data processing unit uses the improved YOLOv5 model to identify obstacles in the image acquired by the binocular vision camera. The specific improvement includes the following steps: Step Z1, replace the C3 module in YOLOv5 with a C3NN module that integrates different soft attention mechanisms, including CBAM attention mechanism, SE attention mechanism, ECA attention mechanism and CA attention mechanism. The CBAM attention mechanism includes channel attention and spatial attention, which are used to enhance the network's ability to capture significant features of the image; the SE attention mechanism adjusts the channel weight of the feature map by capturing the relationship between different channels; the ECA attention mechanism uses one-dimensional convolution to capture local cross-channel dependencies to avoid dimensionality reduction; the CA attention mechanism divides attention into two parallel processes on the height and width axes; In step Z2, the PANet in the Neck part of YOLOv5 is replaced with the BiFPN architecture. The BiFPN efficiently fuses features from different layers through bidirectional feature fusion and weight learning, thereby improving the recognition accuracy.
[0010] The binocular vision depth estimation algorithm is used to calculate the distance between the binocular vision camera and the obstacle in real time. The calculation formula is: ,in, is the distance between the obstacle and the binocular vision camera, is the focal length of the binocular vision camera, The baseline distance between the two lenses of the binocular vision camera, is the horizontal pixel coordinate of the obstacle in the left lens of the binocular vision camera, is the horizontal pixel coordinate of the obstacle in the right lens of the binocular vision camera.
[0011] As a further solution of the present invention, the data processing unit uses data fusion technology to process and generate a three-dimensional model of the environment around the monorail crane equipment, including the following specific contents: the data processing unit uses data fusion technology to fuse the data of the binocular vision camera and the short-range infrared auxiliary camera to generate accurate distance and obstacle information, and assigns different weights to the data of different sensors according to the detection accuracy of the sensors and environmental conditions. The result of data fusion is used to generate a three-dimensional model around the monorail crane equipment. The three-dimensional model not only includes the distance information of the surrounding obstacles, but also includes their orientation and relative position. The generation of the three-dimensional model includes: establishing a unified coordinate system with the monorail crane equipment as the center, converting the data of the sensor to the coordinate system, and ensuring that the three-dimensional model The location information of all obstacles is accurate, and the data obtained by the binocular vision camera can directly generate point cloud data, which is used as the basis of the three-dimensional model and supplemented by the data of the short-range infrared auxiliary camera to achieve a more complete three-dimensional model. The point cloud data is clustered in the three-dimensional model to identify and mark different obstacles. After generating the three-dimensional model, the data processing unit can update the model in real time with an update cycle of no more than 0.5 seconds to ensure that the monorail crane equipment always performs path planning and operation control based on the latest environmental information. The data processing unit has a fault self-detection function. If the sensor or data transmission line fails, it can issue an alarm and locate the fault point within 10 seconds, and switch to the backup control mode at the same time to ensure the basic safe operation of the monorail crane equipment.
[0012] As a further solution of the present invention, by sensing the turning position and turning angle in advance, the rotation speed of the two wheels is controlled so that the output torque of the two wheels is consistent, including the following specific contents: the wheel spacing of the monorail crane device , at the corresponding turning angle , turning radius When , the difference between the running trajectories of the inner wheel and the outer wheel is In this path, the speed needs to be adjusted to reduce the speed of the inner wheel and increase the speed of the outer wheel. is proportional to; The turning radius is , the turning angle is , then the path calculation of a single round is: , Therefore, the inner wheel frequency can be designed to be The time to run on this path is , and during this time, the wheel diameter of the outer wheel needs to be , so the rotation speed of the outer wheel can be calculated as: , Similarly, the outer wheel may be kept If the inner wheel speed is unchanged, the corresponding inner wheel speed needs to be adjusted to: , Similarly, under other schemes, as long as the ratio of the rotational speeds of the inner wheel and the outer wheel is maintained as shown above, the torque stability during the turning process can be guaranteed. When the turning angle of the monorail crane equipment is greater than 30 degrees, the turning position is sensed 5 meters in advance. When the turning angle of the monorail crane equipment is less than or equal to 30 degrees, the turning position is sensed 3 meters in advance, so as to more accurately control the rotational speeds of the two wheels and ensure smooth turning.
[0013] The technical effects and advantages of the distance detection and turning path design method for monorail crane equipment of the present invention are as follows: The present invention uses multiple sensors arranged on the monorail crane equipment to work together to comprehensively and accurately identify the distance information of various obstacles around the front of the vehicle, analyze and process the data collected by the sensors, thereby calculating the real-time distance between the monorail crane and the surrounding environmental elements, and using the path planning algorithm to design a reliable turning radius. At the same time, the reliable turning radius and speed requirements are designed. In addition, during the actual operation, the speed ratio of the two wheels is matched to ensure stability and reliability during the turning process. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 The present invention is a flow chart of a distance detection and turning path design method for monorail crane equipment.
[0015] Figure 2 This is a diagram showing the effect of an improved YOLOv5 model of the present invention on obstacle recognition underground.
[0016] Figure 3 This is a schematic diagram of the speed calculation principle of two wheels turning provided by the present invention.
[0017] Figure 4 This is a diagram showing the relationship between turning speed and path provided by the present invention. DETAILED DESCRIPTION
[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0019] Example 1
[0020] See also Figure 1 As shown in the flowchart, an embodiment of the present invention provides a distance detection and turning path design method for a monorail crane device, which includes the following steps: Step S1, installing sensors on the monorail crane and transmitting data collected by the sensors to a data processing unit in real time, wherein the data processing unit identifies obstacles and calculates distances; Step S2, the data processing unit uses data fusion technology to process and generate a three-dimensional model of the surrounding environment of the monorail crane equipment; Step S3, establishing a turning path model according to the three-dimensional model, and outputting an optimal turning path; Step S4, by sensing the turning position and turning angle in advance, the rotation speeds of the two wheels are controlled to make the output torques of the two wheels consistent.
[0021] Furthermore, in step S1, sensors are installed on the monorail crane, and the collected data is transmitted to the data processing unit in real time. The data processing unit identifies obstacles and calculates distances, including: using sensors installed on the monorail crane to achieve 360-degree real-time obstacle detection around the monorail crane. In order to optimize the recognition effect of obstacles around the monorail crane head, multiple binocular vision cameras are installed on the monorail crane to achieve 360-degree all-round real-time monitoring through multi-view coverage, and the collected image data is transmitted to the data processing unit in real time. A high-definition wide-angle binocular vision camera is installed at the front center of the monorail crane to capture the panoramic view in front of the device and the image of obstacles at a distance. The installation height of the binocular vision camera is higher than the front structure of the monorail crane to avoid blind spots. In addition, a group of wide-angle binocular vision cameras are installed on the left and right sides of the monorail crane, respectively, which are suitable for capturing environmental changes on the left and right sides at close range, and improving the ability to recognize potential obstacles on the side of the monorail crane. The detection unit is used to monitor the obstacle detection and turning safety of the monorail crane during operation. The formula for the detection unit is: ,in, It is the safety detection score, which is used to judge the safety of the current operating status of the monorail crane. , indicating that there is a potential danger in the path, and it is necessary to update the optimal turning path model and recalculate the optimal turning path. , the monorail crane equipment operates safely in the current state, is the distance between the monorail crane and the obstacle, In order to adjust the influence of the distance between the monorail crane and the obstacle on the safety detection score, To adjust the impact of turning angle and turning radius on safety detection scores, is the relative speed between the monorail crane and the obstacle, To adjust the impact of the relative speed between the device and the obstacle on the safety detection score, It is the minimum safe distance that needs to be maintained between the monorail and obstacles during operation. It is used to adjust the influence of the ratio of the minimum safety distance to the obstacle distance on the safety detection score. At the same time, a short-range infrared auxiliary camera is arranged at the front, rear and left and right corners of the monorail crane to enhance the perception of close-range environments in low light or special conditions, especially for the recognition of close-range obstacles when turning and reversing.
[0022] In this embodiment, the data processing unit uses the improved YOLOv5 model to identify obstacles in the image acquired by the binocular vision camera. The specific improvement includes the following steps: Step Z1, replace the C3 module in YOLOv5 with a C3NN module that integrates different soft attention mechanisms, including CBAM attention mechanism, SE attention mechanism, ECA attention mechanism and CA attention mechanism. The CBAM attention mechanism includes channel attention and spatial attention, which are used to enhance the network's ability to capture significant features of the image; the SE attention mechanism adjusts the channel weight of the feature map by capturing the relationship between different channels; the ECA attention mechanism uses one-dimensional convolution to capture local cross-channel dependencies to avoid dimensionality reduction; the CA attention mechanism divides attention into two parallel processes on the height and width axes; In step Z2, the PANet in the Neck part of YOLOv5 is replaced with the BiFPN architecture. The BiFPN efficiently fuses features from different layers through bidirectional feature fusion and weight learning, thereby improving the recognition accuracy.
[0023] The ablation experiment of the improved YOLOv5 model to identify obstacles is as follows: Table 1 Ablation experiment of YOLOv5 model recognition accuracy Model Accuracy (%) Recall rate (%) F1 score Average accuracy (%) YOLOv5 81.418 82.654 81.863 87.854 Improved YOLOv5 86.591 83.059 84.788 88.854 In the embodiments, see Figure 2 The effect diagram shown is the effect of the binocular vision camera on the monorail using the improved YOLOv5 model to identify obstacles.
[0024] In this embodiment, a binocular vision depth estimation algorithm is used to calculate the distance between the binocular vision camera and the obstacle in real time. The calculation formula is: ,in, is the distance between the obstacle and the binocular vision camera, is the focal length of the binocular vision camera, The baseline distance between the two lenses of the binocular vision camera, is the horizontal pixel coordinate of the obstacle in the left lens of the binocular vision camera, is the horizontal pixel coordinate of the obstacle in the right lens of the binocular vision camera.
[0025] Furthermore, the data processing unit uses data fusion technology to process and generate a three-dimensional model of the environment around the monorail crane equipment, including: using data fusion technology in the data processing unit to fuse the data of the binocular vision camera and the short-range infrared auxiliary camera to generate accurate distance and obstacle information, and assigning different weights to the data of different sensors according to the detection accuracy of the sensors and environmental conditions, and generating a three-dimensional model of the environment around the monorail crane equipment with the result of data fusion. The three-dimensional model not only includes the distance information of the surrounding obstacles, but also includes their orientation and relative position. The generation of the three-dimensional model includes: establishing a unified coordinate system with the monorail crane equipment as the center, converting the data of the sensor into the coordinate system, and ensuring that all obstacle information in the three-dimensional model is The information position is accurate, the data obtained by the binocular vision camera can directly generate point cloud data, which is used as the basis of the three-dimensional model, and then supplemented by the data of the short-range infrared auxiliary camera to achieve a more complete three-dimensional model. The point cloud data is clustered in the three-dimensional model to identify and mark different obstacles. After generating the three-dimensional model, the data processing unit can update the model in real time, and the update cycle does not exceed 0.5 seconds to ensure that the monorail crane equipment always plans the path and controls the operation according to the latest environmental information. The data processing unit has a fault self-detection function. If the sensor or data transmission line fails, it can sound an alarm and locate the fault point within 10 seconds, and switch to the backup control mode at the same time to ensure the basic safe operation of the monorail crane equipment.
[0026] Furthermore, in step S3, a turning path model is established according to the three-dimensional model, and an optimal turning path is output, including: the formula of the optimal turning path model is: ; Where: is the total cost function, which evaluates the total “cost” that the monorail crane needs to pay during the turning process. is the distance that the monorail crane equipment needs to travel during the turning process, wherein: For all the key discrete points of the monorail crane during the turning process, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, is the energy consumed by the monorail crane during the turning process, wherein: For the monorail crane equipment The speed of a discrete point, For the monorail crane equipment The acceleration of a discrete point, To adjust the energy consumed to the total cost function The coefficient of influence, is the turning radius of the monorail crane during the turning process, satisfy , To adjust the turning radius to the total cost function The influencing coefficient is used to comprehensively evaluate the optimal path that the monorail crane equipment should take during the turning process through the optimal turning path model. The path planning algorithm also includes a speed limit for the monorail crane equipment. When driving on a straight road, the speed does not exceed 80% of the designed maximum speed. When turning, the speed is dynamically adjusted according to the turning radius and angle, and the maximum speed does not exceed 50% of the designed maximum speed to ensure operational safety.
[0027] Further, see Figure 3-Figure 4 In step S4, by sensing the turning position and turning angle in advance, the rotation speeds of the two wheels are controlled so that the output torques of the two wheels are consistent, including: the wheel spacing of the monorail crane device , at the corresponding turning angle , turning radius When , the difference between the running trajectories of the inner wheel and the outer wheel is In this path, the speed needs to be adjusted to reduce the speed of the inner wheel and increase the speed of the outer wheel. is proportional to; The turning radius is , the turning angle is , then the path calculation of a single round is: , Therefore, the inner wheel frequency can be designed to be The time to run on this path is , and during this time, the wheel diameter of the outer wheel needs to be , so the rotation speed of the outer wheel can be calculated as: , Similarly, the outer wheel may be kept If the inner wheel speed is unchanged, the corresponding inner wheel speed needs to be adjusted to: , Similarly, under other schemes, as long as the ratio of the rotation speed of the inner wheel and the outer wheel is maintained as shown above, the torque stability during the turning process can be guaranteed. When the turning angle of the monorail crane equipment is greater than 30 degrees, the turning position is sensed 5 meters in advance. When the turning angle of the monorail crane equipment is less than or equal to 30 degrees, the turning position is sensed 3 meters in advance.
[0028] The present invention installs a variety of sensors on the monorail crane equipment. These sensors work in coordination with each other to realize the recognition of obstacles around the monorail crane head and distance calculation. By conducting in-depth analysis and processing on the data collected by each sensor, the real-time distance status between the monorail crane equipment and various elements in the surrounding environment can be accurately calculated. Subsequently, the path planning algorithm is used to design the turning radius. At the same time, the corresponding speed requirement is determined. During actual operation, the double-wheel speed ratio is adjusted to ensure smooth and reliable turning.
[0029] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0030] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A distance detection and turning path design method for a monorail crane, characterized in that: The steps include: Step S1, installing sensors on the monorail crane and transmitting data collected by the sensors to a data processing unit in real time, wherein the data processing unit identifies obstacles and calculates distances; Step S2, the data processing unit uses data fusion technology to process and generate a three-dimensional model of the surrounding environment of the monorail crane equipment; Step S3, establishing a turning path model according to the three-dimensional model, and outputting an optimal turning path; Step S4, by sensing the turning position and turning angle in advance, the rotation speeds of the two wheels are controlled so that the output torques of the two wheels are consistent; The formula of the optimal turning path model is: ; Where: is the total cost function, For all the key discrete points of the monorail crane during the turning process, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, For the The horizontal position information of discrete points in three-dimensional space, For the The vertical position information of discrete points in three-dimensional space, For the The depth information of discrete points in three-dimensional space, For monorail crane equipment The speed of a discrete point, For monorail crane equipment The acceleration of a discrete point, To adjust the energy consumed to the total cost function The coefficient of influence, It is the turning radius of the monorail crane during the turning process. To adjust the turning radius to the total cost function The coefficient of influence.
2. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: In step S4, the wheel spacing of the monorail crane equipment , at the corresponding turning angle , turning radius When , the difference between the running trajectories of the inner wheel and the outer wheel is In this path, the speed needs to be adjusted to reduce the speed of the inner wheel and increase the speed of the outer wheel. The path calculation of a single round is: ,in, is the turning radius, is the turning angle.
3. A distance detection and turning path design method for a monorail crane device according to claim 2, characterized in that: The inner wheel frequency is The time to run on this path is , during this time, the wheel diameter of the outer wheel needs to be , the speed of the outer wheel is: , Keep the outer wheel If the inner wheel speed is unchanged, the corresponding inner wheel speed needs to be adjusted to: .
4. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: In step S1, the detection unit is used to determine the obstacle detection and turning safety of the monorail crane during operation. The formula determined by the detection unit is: , in, It is the safety detection score, which is used to judge the safety of the current operating status of the monorail crane. , indicating that there is a potential danger in the path, and it is necessary to update the optimal turning path model and recalculate the optimal turning path. , the monorail crane equipment operates safely in the current state, is the distance between the monorail crane and the obstacle, In order to adjust the influence of the distance between the monorail crane and the obstacle on the safety detection score, To adjust the impact of turning angle and turning radius on safety detection scores, is the relative speed between the monorail crane and the obstacle, To adjust the impact of the relative speed between the device and the obstacle on the safety detection score, It is the minimum safe distance that needs to be maintained between the monorail and obstacles during operation. It is used to adjust the influence of the ratio of the minimum safety distance to the obstacle distance on the safety detection score.
5. According to the distance detection and turning path design method for monorail crane equipment described in claim 4, a binocular vision camera is installed on the monorail crane equipment to transmit the collected image data to the data processing unit in real time; a high-definition wide-angle binocular vision camera is installed at the front center of the monorail crane equipment to capture the panoramic view in front of the equipment and the image of obstacles at a distance; the installation height of the binocular vision camera is higher than the front structure of the monorail crane equipment; a group of wide-angle binocular vision cameras are installed on the left and right sides of the monorail crane equipment to capture the environmental changes on the left and right sides at close range; a short-range infrared auxiliary camera is arranged at the front and back and the four left and right corners of the monorail crane equipment to enhance the perception ability of the close-range environment under low light or special conditions.
6. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: In step S1, the data processing unit uses the improved YOLOv5 model to perform obstacle recognition on the image acquired by the binocular vision camera, including the following steps: Step Z1, replace the C3 module in YOLOv5 with a C3NN module that integrates different soft attention mechanisms, including CBAM attention mechanism, SE attention mechanism, ECA attention mechanism and CA attention mechanism. The CBAM attention mechanism includes channel attention and spatial attention, which are used to enhance the network's ability to capture significant features of the image; the SE attention mechanism adjusts the channel weight of the feature map by capturing the relationship between different channels; the ECA attention mechanism uses one-dimensional convolution to capture local cross-channel dependencies to avoid dimensionality reduction; the CA attention mechanism divides attention into two parallel processes on the height and width axes; In step Z2, the PANet in the Neck part of YOLOv5 is replaced with the BiFPN architecture. The BiFPN fuses features from different layers through bidirectional feature fusion and weight learning, thereby improving the recognition accuracy.
7. A distance detection and turning path design method for a monorail crane device according to claim 5, characterized in that: In step S2, data fusion technology is used to fuse the data of the binocular vision camera and the short-range infrared auxiliary camera, and the result of data fusion is used to generate a three-dimensional model of the area around the monorail crane device. The three-dimensional model contains distance information of surrounding obstacles, as well as their orientation and relative position. The generation of the three-dimensional model includes: establishing a unified coordinate system with the monorail crane device as the center, converting the data of the sensor to the coordinate system, directly generating point cloud data from the data acquired by the binocular vision camera, using it as the basis of the three-dimensional model, and then supplementing it with data from other sensors to achieve a more complete three-dimensional model, performing cluster analysis on the point cloud data in the three-dimensional model, and identifying and marking different obstacles.
8. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: After generating the three-dimensional model, the data processing unit can update the model in real time, with the update cycle not exceeding 0.5 seconds.
9. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: When the turning angle of the monorail crane device is greater than 30 degrees, the turning position is sensed 5 meters in advance. When the turning angle of the monorail crane device is less than or equal to 30 degrees, the turning position is sensed 3 meters in advance.
10. A distance detection and turning path design method for a monorail crane device according to claim 1, characterized in that: The data processing unit has a fault self-checking function. If a sensor or data transmission line fails, an alarm will be issued within 10 seconds to locate the fault point and switch to the backup control mode at the same time.