Anti-collision control method for slewing mechanism of portal crane

Abstract

The invention provides an anti-collision control method for a slewing mechanism of a door seat machine, which comprises the following steps of: acquiring obstacle information in a slewing radius range through a laser radar visual camera arranged on a slewing platform of the door seat machine and a millimeter wave radar and a high-definition visual camera which are arranged at the end part of a slewing arm; meanwhile, the real-time rotating speed, the rotating angle and the hoisting load data of the rotating mechanism are obtained through a door seat machine control system; the laser radar visual camera, the millimeter wave radar and the high-definition visual camera adopt a complementary detection mode; according to the method, the environmental data and the equipment operation data of the rotation area of the portal machine are collected through multi-sensor fusion, and the hierarchical anti-collision control is realized in combination with a dynamic early-warning algorithm. The data are subjected to integration processing, dynamic early-warning threshold calculation, grouped anti-collision control unit judgment and recovery operation judgment in sequence. The method has the advantages that the environmental data and the equipment operation data of the rotation area of the portal machine are collected through multi-sensor fusion, and the hierarchical anti-collision control is realized; the problems that a traditional anti-collision method is lagged in response, prone to being interfered by the environment and low in early warning precision are solved, and the safety and reliability of rotation operation of the portal crane are effectively improved.

Description

Technical Field

[0001] This invention relates to a gantry crane rotation anti-collision control method, and relates to the field of gantry crane safety control technology. Background Technology

[0002] Gantry cranes are widely used in ports, storage yards, and other similar locations. Their slewing mechanisms need to cover a large area during operation, making them prone to collisions with surrounding fixed facilities (such as dock pillars and other equipment) or moving targets (such as workers and transport vehicles). Traditional collision avoidance methods often rely on a single infrared sensor or a preset fixed safety distance, which has the following drawbacks: 1. A single sensor is susceptible to environmental interference such as dust, strong light, and water mist, which may lead to misjudgment or missed detection of obstacles; 2. The fixed safety distance does not take into account dynamic factors such as rotation speed and load. When the speed is too high or the load is too heavy, the braking response is delayed and collisions are likely to occur. When the speed is too low, the work efficiency will be affected. 3. The lack of a graded early warning mechanism means that relying solely on emergency braking can easily lead to violent swinging of the lifting equipment, which in turn increases safety risks. Summary of the Invention

[0003] This invention provides a collision avoidance control method for the slewing mechanism of a gantry crane. This method uses multi-sensor fusion to collect environmental data and equipment operation data of the gantry crane's slewing area, and combines it with a dynamic early warning algorithm to achieve hierarchical collision avoidance control. This solves the problems of slow response, susceptibility to environmental interference, and low early warning accuracy of traditional collision avoidance methods, and effectively improves the safety and reliability of gantry crane slewing operations.

[0004] The object of this invention is achieved by the following means: A collision avoidance control method for a gantry crane's slewing mechanism includes the following steps: S1: Data Acquisition: A lidar vision camera is installed on the gantry crane's slewing platform, and a millimeter-wave radar vision camera is installed at the end of the slewing arm. The lidar vision camera on the gantry crane's slewing platform, along with the millimeter-wave radar and high-definition vision camera at the end of the slewing arm, collects obstacle information within the slewing radius. Simultaneously, the gantry crane's control system acquires real-time rotational speed, slewing angle, and load data of the slewing mechanism. The lidar vision camera and the millimeter-wave radar and high-definition vision camera employ a complementary detection method. The lidar camera is used for high-precision identification of fixed obstacle outlines, while the millimeter-wave radar and high-definition vision camera are used to penetrate dust, water mist, and other interfering environments to identify moving obstacles, such as workers and other equipment. s2: Data fusion processing: The information on obstacles within the collected slewing radius, the real-time rotation speed, slewing angle and load data of the slewing mechanism are denoised, calibrated and fused to establish a three-dimensional coordinate model of the obstacle. Combined with the operating data of the slewing mechanism, the real-time distance and relative speed between the obstacle and the slewing component of the gantry crane are calculated. S3: Dynamic warning threshold calculation: Based on the current slewing speed of the gantry crane, the load data, and the type of obstacle (fixed or moving), the collision warning threshold is dynamically adjusted through a preset algorithm, including the first-level warning distance threshold and the second-level braking distance threshold. The calculation logic of the dynamic warning threshold is: warning distance = (current slewing speed × braking response time) + (load influence coefficient × safety redundancy distance), where the load influence coefficient increases linearly with the increase of load.

[0005] S4: Graded collision avoidance control unit: When the real-time distance exceeds the first-level warning distance threshold, normal rotation operation shall be maintained; When the real-time distance is less than or equal to the first-level warning distance threshold and greater than the second-level braking distance threshold, the first-level warning is triggered, the slewing mechanism is controlled to reduce speed, and an audible and visual alarm is issued to the operator. When the real-time distance is less than or equal to the secondary braking distance threshold, the secondary braking is triggered, immediately controlling the slewing mechanism to stop rotating, and simultaneously locking the brake to prevent the slewing component from sliding. During secondary braking, a stepped control strategy of "deceleration before braking" is adopted to avoid the swaying of the spreader or structural impact caused by emergency braking. S5: Resumption of Operation Judgment: Once the obstacle is removed or the operator confirms that it is safe, the anti-collision brake is released through the control system. The slewing operation can only be restarted after the slewing mechanism returns to normal.

[0006] In the above-mentioned anti-collision control method for the gantry crane slewing mechanism, step s1 involves symmetrically installing two sets of laser radar vision cameras on both sides of the slewing center of the gantry crane slewing platform.

[0007] In the above-mentioned anti-collision control method for the gantry crane slewing mechanism, in step s1, the detection angle of the laser radar vision camera is 120°, the ranging accuracy is ±2cm, a high-definition vision camera with night infrared function is installed at the end of the slewing arm, the detection range of the millimeter-wave radar is 0-50m, the anti-interference level of the millimeter-wave radar is IP67, and at the same time, it communicates with the gantry crane control system through the fieldbus to obtain the rotation speed, slewing angle and lifting weight data of the slewing mechanism in real time.

[0008] The anti-collision control method for the gantry crane's slewing mechanism described above includes the following specific steps in step s2: 2.1) Filter and denoise the obstacle point cloud data collected by the lidar vision camera to remove environmental interference points; 2.2) Millimeter-wave radar data is matched with high-definition visual camera images, and the type of obstacle is identified through image recognition, such as "fixed wall", "moving truck" and "personnel"; 2.3) By integrating obstacle information within the slewing radius, real-time rotation speed, slewing angle, and load data of the slewing mechanism, the three-dimensional coordinates of the obstacle are determined in the coordinate system. Combined with the real-time operating parameters of the slewing mechanism, the real-time distance (accuracy ±5cm) and relative approach speed between the obstacle and the slewing arm are calculated using kinematic formulas.

[0009] Compared with the prior art, the present invention has the following technical effects: 1. High-precision recognition: Multi-sensor fusion avoids interference from a single device. That is, obstacle information within the turning radius, real-time rotation speed of the turning mechanism, turning angle and load data are integrated to avoid interference from a single device, and the obstacle recognition accuracy is improved to over 98%. 2. Dynamic adaptability: The warning threshold is adjusted in real time according to the rotation speed and the load, taking into account both safety and work efficiency; 3. Tiered protection: Stepped control avoids secondary risks associated with emergency braking, improving the safety of equipment and personnel; 4. High practicality: It is suitable for complex environments such as ports and storage yards, without the need for large-scale modification of existing gantry crane structures, and the cost is controllable. Attached Figure Description

[0010] Figure 1 This is a flowchart of the anti-collision control method for the gantry crane rotation of the present invention.

[0011] Figure 2 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0013] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0014] The specific structure of this invention is combined with the appendix. Figure 1 and Figure 2Describe it in detail.

[0015] Example 1: A collision avoidance control method for a gantry crane's slewing mechanism includes the following steps: S1: Data Acquisition: A lidar vision camera 1 is installed on the gantry crane's slewing platform, and a millimeter-wave radar 2 and a high-definition vision camera 4 are installed at the end of the slewing arm. Obstacle information within the slewing radius is collected through the lidar vision camera on the gantry crane's slewing platform and the millimeter-wave radar 2 and high-definition vision camera 4 installed at the end of the slewing arm. Simultaneously, the real-time rotation speed, slewing angle, and load data of the slewing mechanism 3 are obtained through the gantry crane's control system. The lidar vision camera, millimeter-wave radar, and high-definition vision camera employ a complementary detection method. The lidar camera is used for high-precision identification of fixed obstacle outlines, while the millimeter-wave radar and high-definition vision camera are used to penetrate dust, water mist, and other interfering environments to identify moving obstacles, such as workers and other equipment. s2: Data fusion processing: The information on obstacles within the collected slewing radius, the real-time rotation speed, slewing angle and load data of the slewing mechanism are denoised, calibrated and fused to establish a three-dimensional coordinate model of the obstacle. Combined with the operating data of the slewing mechanism, the real-time distance and relative speed between the obstacle and the slewing component of the gantry crane are calculated. S3: Dynamic warning threshold calculation: Based on the current slewing speed of the gantry crane, the load data, and the type of obstacle (fixed or moving), the collision warning threshold is dynamically adjusted through a preset algorithm, including the first-level warning distance threshold and the second-level braking distance threshold. The calculation logic of the dynamic warning threshold is: warning distance = (current slewing speed × braking response time) + (load influence coefficient × safety redundancy distance), where the load influence coefficient increases linearly with the increase of load.

[0016] S4: Graded collision avoidance control unit: When the real-time distance exceeds the first-level warning distance threshold, normal rotation operation shall be maintained; When the real-time distance is less than or equal to the first-level warning distance threshold and greater than the second-level braking distance threshold, the first-level warning is triggered, the slewing mechanism is controlled to reduce speed, and an audible and visual alarm is issued to the operator. When the real-time distance is less than or equal to the secondary braking distance threshold, the secondary braking is triggered, immediately controlling the slewing mechanism to stop rotating, and simultaneously locking the brake to prevent the slewing component from sliding. During secondary braking, a stepped control strategy of "deceleration before braking" is adopted to avoid the swaying of the spreader or structural impact caused by emergency braking. S5: Resumption of Operation Judgment: Once the obstacle is removed or the operator confirms that it is safe, the anti-collision brake is released through the control system. The slewing operation can only be restarted after the slewing mechanism returns to normal.

[0017] Example 2: S1: Data Acquisition: A lidar vision camera 1 is installed on the gantry crane's slewing platform. Two sets of millimeter-wave radar vision cameras 1 are symmetrically installed on both sides of the slewing center of the gantry crane's slewing platform. Millimeter-wave radars 2 are symmetrically installed on both sides of the slewing arm end. At the same time, two sets of high-definition vision cameras 4 are symmetrically installed on both sides of the slewing arm end, with two cameras in each set. Through the lidar vision camera on the gantry crane's slewing platform, and the millimeter-wave radars 2 and high-definition vision cameras 2 installed at the slewing arm end, obstacle information within the slewing radius is collected. At the same time, the real-time rotation speed, slewing angle, and load data of the slewing mechanism are obtained through the gantry crane control system. The lidar vision camera, millimeter-wave radar, and high-definition vision camera adopt a complementary detection method. The lidar camera is used to identify the outline of fixed obstacles with high precision, while the millimeter-wave radar and high-definition vision camera are used to penetrate the interference environment such as dust and water mist to identify moving obstacles, such as workers and other equipment. s2: Data fusion processing: The information on obstacles within the collected slewing radius, the real-time rotation speed, slewing angle and load data of the slewing mechanism are denoised, calibrated and fused to establish a three-dimensional coordinate model of the obstacle. Combined with the operating data of the slewing mechanism, the real-time distance and relative speed between the obstacle and the slewing component of the gantry crane are calculated. S3: Dynamic warning threshold calculation: Based on the current slewing speed of the gantry crane, the load data, and the type of obstacle (fixed or moving), the collision warning threshold is dynamically adjusted through a preset algorithm, including the first-level warning distance threshold and the second-level braking distance threshold. The calculation logic of the dynamic warning threshold is: warning distance = (current slewing speed × braking response time) + (load influence coefficient × safety redundancy distance), where the load influence coefficient increases linearly with the increase of load.

[0018] S4: Graded collision avoidance control unit: When the real-time distance exceeds the first-level warning distance threshold, normal rotation operation shall be maintained; When the real-time distance is less than or equal to the first-level warning distance threshold and greater than the second-level braking distance threshold, the first-level warning is triggered, the slewing mechanism is controlled to reduce speed, and an audible and visual alarm is issued to the operator. When the real-time distance is less than or equal to the secondary braking distance threshold, the secondary braking is triggered, immediately controlling the slewing mechanism to stop rotating, and simultaneously locking the brake to prevent the slewing component from sliding. During secondary braking, a stepped control strategy of "deceleration before braking" is adopted to avoid the swaying of the spreader or structural impact caused by emergency braking. S5: Resumption of Operation Judgment: Once the obstacle is removed or the operator confirms that it is safe, the anti-collision brake is released through the control system. The slewing operation can only be restarted after the slewing mechanism returns to normal.

[0019] Example 3: A collision avoidance control method for a gantry crane slewing mechanism includes the following steps: Based on Embodiment 2, step s1 is improved, namely, in step s1, the detection angle of the laser radar vision camera is 120°, the ranging accuracy is ±2cm, a high-definition vision camera with night infrared function is installed at the end of the slewing arm, the detection range of the millimeter-wave radar is 0-50m, the anti-interference level of the millimeter-wave radar is IP67, and at the same time, it communicates with the gantry crane control system through the fieldbus to obtain the rotation speed, slewing angle and load data of the slewing mechanism in real time.

[0020] Example 3: A collision avoidance control method for a gantry crane slewing mechanism includes the following steps: Based on Embodiment 3, step s2 is improved, that is, step s2 includes the following specific steps: 2.1) Filter and denoise the obstacle point cloud data collected by the lidar vision camera to remove environmental interference points; 2.2) Millimeter-wave radar data is matched with high-definition visual camera images, and the type of obstacle is identified through image recognition, such as "fixed wall", "moving truck" and "personnel"; 2.3) By integrating obstacle information within the slewing radius, real-time rotation speed, slewing angle, and load data of the slewing mechanism, the three-dimensional coordinates of the obstacle are determined in the coordinate system. Combined with the real-time operating parameters of the slewing mechanism, the real-time distance (accuracy ±5cm) and relative approach speed between the obstacle and the slewing arm are calculated using kinematic formulas.

[0021] Example 4: A collision avoidance control method for a gantry crane's slewing mechanism includes the following steps: 1. Data Acquisition Module Deployment: Two sets of lidar vision cameras (detection angle 120°, ranging accuracy ±2cm) are symmetrically installed on both sides of the slewing center of the gantry crane's slewing platform. Four high-definition vision cameras (with night infrared function) and two millimeter-wave radars (detection distance 0-50m, anti-interference level IP67) are installed at the end of the slewing arm. At the same time, the system communicates with the gantry crane control system (the main PLC of the gantry crane) via fieldbus to acquire real-time data on the rotation speed (0-5r / min), slewing angle (0-360°), and lifting weight (0-50t) of the slewing mechanism.

[0022] 2. Data fusion processing flow: 1) Filter and denoise the obstacle point cloud data collected by the lidar vision camera to remove environmental interference points; 2) Millimeter-wave radar data is matched with high-definition visual camera images, and the type of obstacle (such as "fixed wall", "moving truck", "person") is identified through image recognition. 3) By fusing data from multiple sensors, the three-dimensional coordinates of the obstacle are determined in the coordinate system. Combined with the real-time operating parameters of the rotary mechanism, the real-time distance (accuracy ±5cm) between the obstacle and the rotary arm is calculated using kinematic formulas.

[0023] 3. Example of dynamic early warning threshold calculation: 1) When the current rotation speed of the gantry crane is 3 r / min (linear speed is about 1.5 m / s), the lifting weight is 20 t (the lifting weight influence coefficient is taken as 1.2), the braking response time is 0.5 s, and the safety redundancy distance is preset to 1.5 m; 2) Level 1 warning distance = (1.5m / s × 0.5s) + (1.2 × 1.5m) = 0.75m + 1.8m = 2.55m; 3) Secondary braking distance = (1.5m / s × 0.5s) + (1.2 × 0.8m) = 0.75m + 0.96m = 1.71m; 4) If the lifting weight increases to 40t (the lifting weight influence coefficient is taken as 1.8), the first-level warning distance is adjusted to 0.75m + (1.8 × 1.5m) = 3.45m, and the second-level braking distance is adjusted to 0.75m + (1.8 × 0.8m) = 2.19m.

[0024] 4. Hierarchical control implementation: 1) When the real-time distance is 3m (greater than 2.55m), maintain normal rotation; 2) When the real-time distance drops to 2.5m (between 1.71m and 2.55m), a first-level warning is triggered: the PLC controls the rotary motor to slow down to 1r / min, the driver's cab audible and visual alarm emits a "beep" sound, and the display screen shows the location and distance of the obstacle; 3) When the real-time distance drops to 1.7m (less than 1.71m), the secondary braking is triggered: first, the motor is controlled to decelerate to 0.5r / min (lasting 0.3s), then the electromagnetic brake is activated to lock the slewing mechanism, and at the same time the slewing control signal is cut off. The cab will sound a long alarm, and the display screen will show the message "Emergency braking, do not operate". 5. Resumption of Operation Procedure: After the operator confirms through the camera that the obstacle has been removed, press the "Reset" button in the cab. The system will release the brake, and the slewing mechanism will return to standby mode. Normal operation will resume after the operator re-enters the slewing command.

[0025] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several changes and improvements without departing from the overall concept of the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A collision avoidance control method for a gantry crane's slewing mechanism, characterized in that: Includes the following steps: S1: Data Acquisition: A lidar vision camera is installed on the gantry crane's slewing platform, and a millimeter-wave radar vision camera is installed at the end of the slewing arm. The lidar vision camera on the gantry crane's slewing platform, along with the millimeter-wave radar and high-definition vision camera at the end of the slewing arm, collects obstacle information within the slewing radius. Simultaneously, the gantry crane's control system acquires real-time rotational speed, slewing angle, and load data of the slewing mechanism. The lidar vision camera and the millimeter-wave radar and high-definition vision camera employ a complementary detection method. The lidar camera is used for high-precision identification of fixed obstacle outlines, while the millimeter-wave radar and high-definition vision camera are used to penetrate dust, water mist, and other interfering environments to identify moving obstacles. s2: Data fusion processing: The information on obstacles within the collected slewing radius, the real-time rotation speed, slewing angle and load data of the slewing mechanism are denoised, calibrated and fused to establish a three-dimensional coordinate model of the obstacle. Combined with the operating data of the slewing mechanism, the real-time distance and relative speed between the obstacle and the slewing component of the gantry crane are calculated. S3: Dynamic warning threshold calculation: Based on the current slewing speed of the gantry crane, the load data, and the type of obstacle (fixed or moving), the collision warning threshold is dynamically adjusted through a preset algorithm, including the first-level warning distance threshold and the second-level braking distance threshold. The calculation logic of the dynamic warning threshold is: warning distance = (current slewing speed × braking response time) + (load influence coefficient × safety redundancy distance), where the load influence coefficient increases linearly with the increase of load. S4: Graded collision avoidance control unit: When the real-time distance exceeds the first-level warning distance threshold, normal rotation operation shall be maintained; When the real-time distance is less than or equal to the first-level warning distance threshold and greater than the second-level braking distance threshold, the first-level warning is triggered, the slewing mechanism is controlled to reduce speed, and an audible and visual alarm is issued to the operator. When the real-time distance is less than or equal to the secondary braking distance threshold, the secondary braking is triggered, immediately controlling the slewing mechanism to stop rotating, and simultaneously locking the brake to prevent the slewing component from sliding. During secondary braking, a stepped control strategy of "deceleration before braking" is adopted to avoid the swaying of the spreader or structural impact caused by emergency braking. S5: Resumption of Operation Judgment: Once the obstacle is removed or the operator confirms that it is safe, the anti-collision brake is released through the control system. The slewing operation can only be restarted after the slewing mechanism returns to normal.

2. The anti-collision control method for the gantry crane slewing mechanism according to claim 1, characterized in that: In step s1, two sets of lidar vision cameras are symmetrically installed on both sides of the rotation center of the gantry crane's rotating platform.

3. The anti-collision control method for the gantry crane slewing mechanism according to claim 1, characterized in that: In step s1, the detection angle of the lidar vision camera is 120° and the ranging accuracy is ±2cm. A high-definition vision camera with night infrared function is installed at the end of the slewing arm. The detection range of the millimeter-wave radar is 0-50m and the anti-interference level of the millimeter-wave radar is IP67. At the same time, it communicates with the gantry crane control system through the fieldbus to obtain the rotation speed, slewing angle and lifting weight data of the slewing mechanism in real time.

4. The anti-collision control method for the gantry crane slewing mechanism according to claim 2, characterized in that: Step s2 includes the following specific steps: 2.1) Filter and denoise the obstacle point cloud data collected by the lidar vision camera to remove environmental interference points; 2.2) Millimeter-wave radar data is matched with high-definition visual camera images, and the type of obstacle is identified through image recognition; 2.3) By integrating obstacle information within the slewing radius, real-time rotation speed, slewing angle, and load data of the slewing mechanism, the three-dimensional coordinates of the obstacle are determined in the coordinate system. Combined with the real-time operating parameters of the slewing mechanism, the real-time distance and relative approach speed between the obstacle and the slewing arm are calculated using kinematic formulas.

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