An automated rail-mounted gantry crane control method

Abstract

This invention discloses an automated rail-mounted gantry crane control method, comprising: real-time scanning of the yard environment using a multi-sensor perception module consisting of a lidar, binocular vision camera, ultrasonic sensor, and position encoder mounted on the rail-mounted gantry crane and the crane; data fusion using an extended Kalman filter algorithm to construct a real-time 3D environment model; point cloud processing of the 3D environment model to generate a yard contour height map; optimization with the shortest total path travel and lowest energy consumption as multi-objective objectives, generating an optimal travel path in the form of a parabola or elliptical arc under safety distance constraints, replacing the traditional fixed rectangular path mode of large and small gate frames; during crane operation, collision avoidance control is implemented through a three-level safety distance classification strategy, and dynamic updating of the yard contour height map and real-time path replanning are supported. This invention can significantly reduce crane operation energy consumption, shorten the single-container operation cycle, and ensure operational safety.

Description

Technical Field

[0001] This invention belongs to the field of automated terminal loading and unloading equipment control technology, specifically, it relates to an optimal path planning and collision avoidance control method for automated rail-mounted gantry cranes. Background Technology

[0002] With the rapid growth of global trade, automated container terminals have become an important direction for port development. Rail-mounted gantry cranes (RMGs), as the core loading and unloading equipment of automated terminals, directly impact the overall operational capacity and operating costs of the terminal due to the efficiency and safety of their spreaders. Path planning for the rail-mounted gantry crane spreaders within the yard—that is, determining the trajectory from the starting position to the target position—is a crucial aspect of the automated control of rail-mounted gantry cranes.

[0003] Currently, the travel paths of rail-mounted spreaders in automated container terminals mainly employ two fixed modes: a "large gate" and a "small gate." In the large gate mode, the spreader is first vertically lifted to its maximum safe height, then moved horizontally to directly above the target position, and finally vertically lowered to the working height, following a rectangular trajectory. In the small gate mode, the spreader is lifted to a height slightly above the highest obstacle, then moved horizontally before lowering to the target position, again following a segmented straight-line combination path. Both modes share the characteristic of fixed paths and do not consider the real-time status of the container yard.

[0004] The aforementioned fixed gantry path mode has the following drawbacks: First, it results in significant energy waste, as the fixed gantry mode does not consider the actual yard conditions, and the large amount of redundant vertical movement leads to increased energy consumption; second, it leads to low operational efficiency, as non-optimal paths prolong the single-container operation cycle, directly affecting the overall throughput of the terminal; third, it has excessive safety redundancy, as it lacks real-time perception capabilities and uses a fixed safety margin, making it impossible to dynamically adjust according to actual conditions; fourth, it lacks adaptive capabilities, failing to optimize the path based on real-time changes in the yard (such as changes in container stacking height and truck position). Summary of the Invention

[0005] The purpose of this invention is to propose an automated rail-mounted gantry crane control method. By using multi-sensor fusion to perceive the real-time environment of the yard, a three-dimensional environment model and a yard outline height map are constructed. Based on a curve path planning strategy, an energy-optimal parabolic or elliptical arc travel path is generated. At the same time, a three-level safety distance classification collision avoidance control strategy is adopted to achieve efficient, safe and energy-saving operation of the gantry crane.

[0006] The present invention is implemented using the following technical solutions:

[0007] An automated control method for rail-mounted crane spreaders is proposed, comprising the following steps:

[0008] S1: The multi-sensor sensing module installed on the rail-mounted gantry crane and spreader scans the yard environment in real time, acquires three-dimensional point cloud data and image information, and uses the extended Kalman filter algorithm to fuse the multi-sensor data to construct a real-time three-dimensional environment model of the yard.

[0009] S2: Based on the point cloud data of the real-time three-dimensional environment model, the storage yard is divided into grids, the maximum obstacle height in each grid cell area is extracted, and a storage yard outline height map is generated.

[0010] S3: Based on the starting and target position coordinates of the spreader, and combined with the obstacle height information of each grid cell in the yard outline height map, the optimal travel path in the form of a curve is generated with the shortest total path distance and the lowest energy consumption as the multi-objective optimization objectives, while satisfying the safety distance constraint; the curve is a parabolic path or an elliptical arc path.

[0011] S4: During the operation of the spreader along the optimal travel path, the closest distance D between the spreader and surrounding obstacles is detected in real time, and the corresponding anti-collision control response strategy is executed according to the preset three-level safety distance thresholds D1, D2 and D3.

[0012] In some embodiments of the present invention, the multi-sensor sensing module includes: a lidar installed at the center of the bottom of the trolley for 360° three-dimensional point cloud scanning; binocular vision cameras installed on both sides of the trolley for stereo vision depth imaging; ultrasonic sensors installed at the four corners of the lifting device for near-range obstacle detection; and a position encoder installed on the lifting mechanism for accurately measuring the lifting height of the lifting device.

[0013] In some embodiments of the present invention, the fusion processing of multi-sensor data using the extended Kalman filter algorithm specifically includes:

[0014] The data collected by each sensor is time-synchronized to align the data of each sensor to a unified time reference.

[0015] All sensor data are uniformly converted to a global coordinate system with the center of the rail-mounted trolley as the origin;

[0016] Using the extended Kalman filter algorithm, with lidar point cloud data as the subjective measurement and binocular visual depth data and ultrasonic ranging data as auxiliary observations, the three-dimensional position and shape of obstacles in the storage yard are fused and estimated, and the observation noise is filtered and suppressed to obtain the fused three-dimensional environmental data.

[0017] In some embodiments of the present invention, the construction of a real-time three-dimensional environment model of the stockpile environment specifically includes:

[0018] The fused 3D environment data is subjected to voxel downsampling and statistical filtering for noise reduction.

[0019] The iterative nearest-point algorithm is used to register and stitch together point cloud data from adjacent frames;

[0020] Based on the complete point cloud data after registration and stitching, a real-time three-dimensional environment model of the storage yard is constructed. The real-time three-dimensional environment model is stored in an octree structure and dynamically updated incrementally.

[0021] In some embodiments of the present invention, generating the stockpile outline height map specifically includes:

[0022] The horizontal area of ​​the storage yard is divided into equidistant grids according to a preset grid size, resulting in M×N grid units; wherein, the preset grid size is determined based on the standard size of the container, preferably 2.5m along the trolley travel direction and 6.2m along the truck travel direction;

[0023] Iterate through each grid cell and extract the maximum Z-axis coordinate from all 3D point cloud data within that grid cell, using this as the maximum obstacle height for that grid cell. ,in and These are the row and column indices of the grid cells, respectively.

[0024] A two-dimensional matrix is ​​formed by combining the maximum obstacle heights of all grid cells. This is the stockpile outline height map; the stockpile outline height map is dynamically refreshed as the sensor data is updated in real time.

[0025] In some embodiments of the present invention, generating the optimal travel path in the form of a curve specifically includes:

[0026] Let the starting position of the lifting device be... The target location is ,in The horizontal coordinate is... Vertical coordinates;

[0027] Insert at equal intervals along the horizontal direction between the starting position A and the target position B. Intermediate control points ( Horizontal coordinates of each intermediate control point ;

[0028] Query the yard outline height map to obtain each intermediate control point. Maximum obstacle height at the corresponding location And add a preset safety margin The minimum allowable height of the control point is obtained. ;

[0029] Using the vertical coordinates of each control point To optimize the variables, the multi-objective optimization function is to minimize the total path length and energy consumption. As constraints, a sequential quadratic programming algorithm is used to solve the problem and obtain the optimal vertical coordinates of each control point;

[0030] Curve fitting is performed on the starting position A, each optimal control point, and the target position B to generate a smooth parabolic path or elliptical arc path, which serves as the optimal travel path.

[0031] In some embodiments of the present invention, the multi-objective optimization function is defined as follows:

[0032] ;

[0033] in, This is the total path length. The total energy consumption along the path. and These are the weighting coefficients. ;

[0034] In some embodiments of the present invention, the total path length The calculation formula is:

[0035] .

[0036] The formula for calculating the total energy consumption E along the route is:

[0037] .

[0038] in, The total mass of the spreading equipment and the container it carries. It is the acceleration due to gravity. This represents the vertical displacement between adjacent control points. This represents the horizontal displacement between adjacent control points. The equivalent friction coefficient for the movement of the vehicle.

[0039] In some embodiments of the present invention, the safety margin As a dynamic safety margin, it is based on the current operating speed of the spreader. The specific calculation formula for dynamic adjustment of load status is as follows:

[0040] .

[0041] in, The basic safety margin ranges from 0.3m to 0.5m. This is the speed compensation coefficient; This represents the current operating speed of the spreader; is the load compensation coefficient is the mass of the loaded container.

[0042] In some embodiments of the present invention, the three-level safety distance thresholds and corresponding anti-collision control response strategies are specifically:

[0043] First level: emergency stop area. When the detected shortest distance D between the spreader and an obstacle satisfies D≤D1, emergency braking is immediately executed to stop all movements of the trolley traveling and the hoisting mechanism, and an acousto-optic alarm is triggered at the same time; wherein, the value of D1 ranges from 0.3m to 0.5m;

[0044] Second level: deceleration area. When it is detected that D2<D≤D3, the traveling speed of the trolley and the hoisting speed are automatically reduced to 30% to 50% of the rated speed, and the path replanning module is activated at the same time to regenerate an obstacle avoidance path based on the updated yard contour height map; wherein, the value of D2 ranges from 1.0m to 1.5m;

[0045] Third level: safety area. When it is detected that D>D3, the spreader operates at normal speed according to the optimal traveling path without intervention; wherein, the value of D3 ranges from 2.5m to 3.0m.

[0046] In some embodiments of the present invention, the safety distance thresholds D1, D2 and D3 are dynamically adjusted according to the operating speed and load state of the spreader, specifically:

[0047] .

[0048] Wherein, is the -th level reference value of the safety distance threshold, ; is the speed influence coefficient; is the current operating speed; is the maximum operating speed; is the load influence coefficient; is the current load mass; is the maximum rated load mass.

[0049] In some embodiments of the present invention, the method further comprises: during the operation of the spreader, at a preset time period dynamically updating the yard contour height map, and performing real-time replanning on the remaining path segments according to the updated yard contour height map, so as to adapt to dynamic changing scenarios such as entry and exit of container trucks and container loading and unloading in the yard.

[0050] In some embodiments of the present invention, the method further includes: the trolley travel drive and the hoisting mechanism drive adopt a synchronous linkage control mode to achieve smooth trajectory tracking of the spreader along the curved path, avoiding sudden speed changes and mechanical shocks caused by segmented motion. Specifically, based on the parametric equation of the optimal travel path, the path is decomposed into horizontal displacement components and vertical displacement components, generating speed command curves for the trolley travel motor and the hoisting motor, respectively. After the two speed command curves undergo S-shaped acceleration and deceleration processing, a synchronization signal triggers millisecond-level linkage accuracy.

[0051] In some embodiments of the present invention, the method further includes: constraining the curvature of the path during path planning, such that the curvature of the path at any position is constrained. Not exceeding the preset maximum curvature threshold This is to ensure that the acceleration of the lifting mechanism and the trolley traveling mechanism does not exceed the allowable range of the equipment.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0053] First, this invention achieves real-time, accurate, and comprehensive perception of the yard environment through multi-sensor fusion sensing technology, overcoming the limitations of single sensors in terms of detection range, accuracy, and anti-interference capability, and providing a reliable environmental information foundation for path planning.

[0054] Secondly, this invention adopts a parabolic or elliptical arc curve path planning strategy to replace the traditional fixed rectangular path mode of the large and small gate frames. Through multi-objective optimization, the shortest path and the lowest energy consumption travel trajectory are obtained, which can significantly reduce the energy consumption of spreader operation by 30% to 50%, effectively shorten the single container operation cycle, and improve the overall throughput of the terminal.

[0055] Third, this invention designs a three-level safety distance classification collision avoidance control strategy, realizing gradient safety management from emergency stop to deceleration and obstacle avoidance to normal operation. Moreover, the safety threshold can be dynamically and adaptively adjusted according to the operating speed and load status, which not only ensures the safety of operation, but also avoids the efficiency loss caused by fixed safety redundancy.

[0056] Fourth, this invention enables dynamic updating of the yard outline height map and real-time path replanning, allowing the spreader to adaptively respond to dynamic scenarios such as truck entry and exit and changes in container stacking height in the yard, exhibiting good environmental adaptability and robustness.

[0057] Other features and advantages of the present invention will become clearer after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Attached Figure Description

[0058] The accompanying drawings, as part of this invention, are provided to further illustrate the invention. The illustrative embodiments and descriptions are used to explain the invention but do not constitute an undue limitation thereof. Clearly, the drawings described below are merely some embodiments; those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of sensor installation provided in an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of the overall system architecture provided in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram comparing the door frame path and the optimal parabolic path provided in an embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram illustrating the classification of collision avoidance safety distances provided in an embodiment of the present invention;

[0063] Figure 5 This is a flowchart of the automated rail-mounted crane lifting device control method proposed in this invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0065] In automated container terminals, rail-mounted gantry cranes are the core loading and unloading equipment for yard operations. Traditional rail-mounted gantry crane spreaders use fixed large or small gate frames for their travel path, which results in problems such as high energy consumption, low efficiency, excessive safety redundancy, and lack of adaptive capabilities.

[0066] This invention proposes an automated rail-mounted gantry crane control method based on multi-sensor fusion sensing and curve path optimization. By sensing the yard environment in real time, constructing a yard contour height map, generating the optimal curve path, and coordinating with a graded collision avoidance strategy, the method achieves efficient, safe, and energy-saving operation of the gantry crane. The technical solution of this invention is described in detail below with reference to the accompanying drawings.

[0067] The method of this invention is based on Figure 1 and Figure 2 The automated rail-mounted gantry crane path planning system shown is implemented. The system consists of five layers: perception layer, modeling layer, decision planning layer, execution control layer, and physical equipment layer.

[0068] The sensing layer includes various sensor modules, installed on the trolley and spreading device of the rail-mounted gantry crane, specifically including:

[0069] (1) LiDAR: Installed at the center of the bottom of the trolley, it can perform 360° all-round three-dimensional point cloud scanning, and the scanning range covers the entire yard area below and around the spreader. The resolution of the LiDAR is not less than 1800×1800, the frame rate is not less than 10fps, and the measurement accuracy reaches ±2cm. It can accurately obtain the three-dimensional spatial position and shape information of obstacles such as container stacks, trucks, and rail-mounted crane components in the yard.

[0070] (2) Binocular vision cameras: Installed on both sides of the vehicle, one on each side, with a resolution of not less than 1920×1080 and a frame rate of not less than 30fps. The binocular vision cameras acquire depth image information through the principle of stereo vision, which is used to assist the lidar in identifying the type and boundary features of obstacles, especially in providing supplementary information in identifying fine features such as the position of container corner pieces and the outline of trucks.

[0071] (3) Ultrasonic sensors: Installed at the four corners of the spreader, one at each corner, for a total of four. The ultrasonic sensors are used to perform close-range accurate distance measurement when the spreader approaches an obstacle. The detection range is 0.1m to 3.0m, and the measurement accuracy reaches ±1cm, providing the last line of safety for collision avoidance control.

[0072] (4) Position encoder: Installed on the hoisting mechanism, used to accurately measure the lifting height of the spreader. The position encoder has a resolution of not less than 1 mm and can provide real-time feedback on the precise vertical position of the spreader.

[0073] The perception layer also includes a multi-sensor data fusion module, which uses the Extended Kalman Filter (EKF) algorithm to achieve multi-sensor data fusion.

[0074] (1) First, the data collected by each sensor is time-synchronized. Since the sampling frequencies of different sensors are different (10fps for lidar, 30fps for binocular vision camera, 50Hz for ultrasonic sensor, and 1000Hz for position encoder), it is necessary to align the data of each sensor to a unified time reference by means of hardware triggering or software interpolation.

[0075] (2) Secondly, the data from each sensor are uniformly converted to a global coordinate system with the center of the rail-mounted trolley as the origin. Since the sensors are installed in different locations, coordinate transformation is required using a pre-calibrated extrinsic parameter matrix. Specifically, let the sensor... Extrinsic parameter matrix in global coordinate system ,in, It is a 3×3 rotation matrix. If it is a 3×1 translation vector, then the sensor Local coordinates The transformed global coordinates are: .

[0076] (3) Finally, the extended Kalman filter algorithm is used to fuse the multi-source sensor data. Using lidar point cloud data as the subjective measurement and binocular vision depth data and ultrasonic ranging data as auxiliary observations, the system's state equation and observation equation are established. The state vector is defined as the three-dimensional position and velocity of each obstacle in the storage yard. The extended Kalman filter performs optimal estimation of the obstacle state at each time step through alternating prediction and update steps, while effectively suppressing sensor observation noise.

[0077] The modeling layer performs real-time 3D environment model building, yard outline height map generation, and obstacle monitoring and classification identification.

[0078] 3D Environment Model Construction: After voxel downsampling (voxel size 5cm×5cm×5cm) and statistical filtering for denoising, the fused 3D environment data is registered and stitched together using the Iterative Closest Point (ICP) algorithm to construct a real-time 3D environment model of the storage yard. This 3D environment model is stored in an octree structure, supporting dynamic incremental updates. That is, each time new sensor data is received, only the changed areas are locally updated without reconstructing the entire model, thus ensuring real-time performance.

[0079] Storage Yard Profile Height Map Generation: The storage yard profile height map is a two-dimensional matrix that records the maximum obstacle height at each grid location after projecting the 3D environment model onto a 2D horizontal plane. It is the core input information for path planning. The specific generation process is as follows:

[0080] (1) Divide the horizontal area of ​​the yard into equal-distance grids according to the preset grid size to obtain M×N grid units. The grid size is set with reference to the standard size of the container. It is preferred that the grid spacing along the trolley travel direction (Y-axis) is 2.5m (approximately the equal division of the width of a 20-foot standard container) and the grid spacing along the trolley travel direction (X-axis) is 6.2m (approximately the length of a 20-foot standard container).

[0081] (2) Traverse each grid cell Extract all 3D point cloud data within this grid cell. The maximum value of the axis coordinate is used as the maximum obstacle height for that grid cell. For grid cells with unobstructed coverage, Set it to 0. Create a two-dimensional matrix from the maximum obstacle heights of all grid cells. This is the outline height map of the storage yard.

[0082] (3) The stockpile outline height map is dynamically updated at a preset time period Δt (preferably Δt=200ms). Each time it is updated, the maximum obstacle height of each grid cell is recalculated based on the latest sensor fusion data. For grid cells that do not change after multiple consecutive detections, a low-pass filtering strategy is used to suppress height fluctuations caused by sensor noise.

[0083] The decision planning layer consists of three parts: the optimal path planning module, the collision avoidance control module, and the path optimization solution engine.

[0084] like Figure 3 As shown, traditional gate frame paths and small gate frame paths both use a combination of segmented straight lines, resulting in a large amount of redundant vertical motion. The optimal path planning module of this invention adopts a curved path planning strategy, generating a smooth parabolic path or elliptical arc path that fits the contour of the obstacle based on real-time information from the yard outline height map, thereby minimizing travel distance and reducing energy consumption.

[0085] The specific process of path planning is as follows:

[0086] (1) Let the starting position of the lifting device be... The target location is ,in Let be the horizontal coordinates along the direction the car is traveling. These are the vertical height coordinates.

[0087] (2) Insert at equal intervals along the horizontal direction between the starting position A and the target position B. Intermediate control points ( The horizontal coordinate of each intermediate control point is Number of control points Determined based on the horizontal distance between the starting position and the target position, preferably... =10 to 20.

[0088] (3) For each intermediate control point Query the stockpile outline height map to obtain the horizontal coordinates. Maximum obstacle height of the corresponding grid cell Then add dynamic safety margin. The minimum allowable height of the control point is obtained. Dynamic safety margin The calculation formula is: ,in Based on the basic safety margin (values ​​ranging from 0.3m to 0.5m). For speed compensation coefficient, The current operating speed of the spreader. This is the load compensation coefficient. The mass of the container being transported.

[0089] (4) Using the vertical coordinates of each control point To optimize the variables, a multi-objective optimization problem is established:

[0090]

[0091]

[0092]

[0093] in, This is the total path length. The total energy consumption along the path. and Weighting coefficients ( ,default , (i.e., focusing on energy consumption optimization) For the path in curvature at that point The maximum permissible curvature (determined by the maximum acceleration of the hoisting mechanism and the trolley traveling mechanism).

[0094] Total path length The calculation formula is:

[0095] .

[0096] The formula for calculating the total energy consumption E along the route is:

[0097] .

[0098] in, The total mass of the spreading equipment and the container it carries. It is the acceleration due to gravity. This represents the vertical displacement between adjacent control points. This represents the horizontal displacement between adjacent control points. The equivalent friction coefficient for the movement of the vehicle.

[0099] The Sequential Quadratic Programming (SQP) algorithm is used to solve the above optimization problem to obtain the optimal vertical coordinates of each control point. Then, for the starting position A and each optimal control point... Perform cubic spline curve fitting or least squares parabolic fitting with the target position B to generate a smooth and continuous optimal travel path curve.

[0100] In a specific embodiment, when the starting position and the target position are located on both sides of the same row of containers, the path presents an approximately parabolic shape; when the height difference between the starting position and the target position is large, the path presents an approximately elliptical arc shape. Compared with the traditional large-frame path, the optimal parabolic path can reduce the redundant travel in the vertical direction by 40% to 60%, reduce the total energy consumption by 30% to 50%, and shorten the single-container operation cycle by 15% to 25%.

[0101] In the anti-collision control module, as Figure 4 shown, an anti-collision control strategy with three-level safety distance classification is designed, which takes the obstacle as the center and is divided into three regions from inside to outside: an emergency stop area, a deceleration area and a safety area in sequence.

[0102] During the operation of the spreader along the optimal travel path, the ultrasonic sensors installed at the four corners of the spreader and the lidar at the bottom of the trolley continuously detect the shortest distance D between the spreader (including the loaded container) and surrounding obstacles in real time. The distance D is defined as the shortest Euclidean distance from the outer surface of the spreader and the loaded container to the surface of the nearest obstacle.

[0103] The three-level anti-collision control response strategy is specifically as follows:

[0104] First level: Emergency stop area. When D≤D1 (the reference value of D1 is 0.5m), it indicates that the spreader has entered the dangerous area. The system immediately performs an emergency braking operation, stops all movements of the trolley traveling motor and the hoisting motor, and triggers the acousto-optic alarm device in the yard to notify the operator to perform manual intervention. Emergency braking adopts a combined mode of electric braking and mechanical braking to ensure that the spreader stops completely in the shortest time.

[0105] Second level: Deceleration area. When D2<D≤D3 (the reference value of D2 is 1.5m), it indicates that the spreader is approaching an obstacle. The system automatically reduces the trolley traveling speed and hoisting speed to 30% to 50% of the rated speed, and starts the path replanning module at the same time. Based on the latest yard contour height map, the path replanning module re-executes the optimization solution process of step S3 for the remaining path section from the current position of the spreader to the target position, and generates a new obstacle avoidance path. If the distance D continues to decrease after deceleration, the speed will be further reduced until entering the first-level response.

[0106] Third level: Safety area. When D>D3 (the reference value of D3 is 3.0m), it indicates that a sufficient safety distance is maintained between the spreader and surrounding obstacles, and the spreader operates at normal speed along the optimal travel path without any intervention.

[0107] The above three-level safety distance thresholds D1, D2 and D3 are not fixed values, but are dynamically adjusted according to the operating speed and load state of the spreader. The dynamic adjustment formula is:

[0108]

[0109] in For the first The baseline value for the safety distance threshold. The velocity influence coefficient (preferably between 0.2 and 0.5) This is the load influence coefficient (preferably between 0.1 and 0.3). This means that when the spreader's operating speed is higher or the load is heavier, the safety distance thresholds at each level will increase accordingly to ensure sufficient braking distance and reaction time.

[0110] like Figure 2 As shown, in the execution control layer, the trolley travel drive and the hoisting mechanism drive adopt a synchronous linkage control mode. Since the curved path requires the spreader to move simultaneously in both horizontal and vertical directions, coordinated control of the trolley travel motor and the hoisting motor is necessary. Specifically, based on the parametric equation of the optimal travel path, the path is decomposed into horizontal and vertical displacement components, generating speed command curves for the trolley travel motor and the hoisting motor, respectively. After S-shaped acceleration and deceleration processing, the two speed command curves are triggered by a synchronization signal to achieve millisecond-level linkage accuracy, thereby ensuring smooth trajectory tracking of the spreader along the curved path.

[0111] Furthermore, to ensure smooth motion and mechanical safety, the curvature of the path was constrained. The curvature of the path at any position... It must not exceed the maximum curvature threshold ,in The maximum acceleration of the hoisting mechanism and the maximum acceleration of the trolley's traveling mechanism Jointly decided, specifically ,in This represents the maximum combined operating speed of the lifting device.

[0112] In actual yard operations, environmental conditions are not static. Container trucks enter and exit the yard to load and unload containers, while other rail-mounted gantry cranes may be operating in adjacent bays. Therefore, this invention continuously updates the yard outline height map dynamically during the spreader's operation using a path optimization solution engine, and performs real-time replanning of the remaining path segments based on the updated height map.

[0113] The triggering conditions for real-time replanning include: (1) periodic triggering, replanning is performed periodically at a fixed time period Δt (preferably 200ms); (2) event triggering, replanning is triggered immediately when the height change of a grid cell in the yard outline height map exceeds a preset threshold (preferably 0.5m); (3) collision avoidance triggering, replanning is triggered to generate an obstacle avoidance path when the collision avoidance control enters the second deceleration zone.

[0114] The replanning process only optimizes the remaining path segment between the current position of the spreader and the target position, rather than replanning the entire path, in order to reduce computation and ensure real-time performance. The new path generated by the replanning is kept continuous with the already traveled path through spline interpolation (i.e., position, velocity, and acceleration are all continuous), avoiding velocity jumps at path junctions.

[0115] Based on the above, such as Figure 5 As shown, the automated rail-mounted crane lifting device control method proposed in this invention includes the following steps:

[0116] S1: The multi-sensor sensing module installed on the rail-mounted gantry crane and spreader scans the yard environment in real time to acquire three-dimensional point cloud data and image information. The extended Kalman filter algorithm is used to fuse the multi-sensor data to construct a real-time three-dimensional environment model of the yard.

[0117] S2: Based on the point cloud data of the real-time 3D environment model, the storage yard is divided into grids, the maximum obstacle height in each grid cell area is extracted, and a storage yard outline height map is generated.

[0118] S3: Based on the starting and target position coordinates of the spreader, and combined with the obstacle height information of each grid cell in the yard outline height map, the optimal travel path in the form of a curve is generated with the shortest total path distance and the lowest energy consumption as the multi-objective optimization objectives, while satisfying the safety distance constraint.

[0119] The curve can be a parabolic path or an elliptical arc path.

[0120] S4: During the operation of the spreader along the optimal travel path, the closest distance between the spreader and surrounding obstacles is detected in real time, and the corresponding anti-collision control response strategy is executed according to the preset three-level safety distance threshold.

[0121] The implementation process of the method of the present invention is illustrated below through a specific embodiment:

[0122] Suppose that in an automated container terminal yard, a rail-mounted gantry crane needs to move a 40-foot container (weighing approximately 30 tons) from the second level of the third row (starting position A) to the ground level of the seventh row (target position B). The coordinates of the starting position A are (7.5m, 5.2m), and the coordinates of the target position B are (17.5m, 0m).

[0123] In the traditional gate frame path, the lifting equipment is first vertically lifted from point A to the maximum safe height of 15.0m (the clearance height under the rail-mounted crane beam minus the safety margin), then moved horizontally 10.0m to directly above the target position, and finally vertically lowered 15.0m to the ground. The total path distance is 15.0 + 10.0 + 15.0 = 40.0m.

[0124] Using the method of this invention, a real-time contour height map of the container yard is obtained through multi-sensor fusion perception. The maximum obstacle heights detected above the path are: 8.6m for row 3 (4 layers of containers), 6.5m for row 4 (3 layers of containers), 4.3m for row 5 (2 layers of containers), 2.2m for row 6 (1 layer of containers), and 0m for row 7 (empty space). After adding a dynamic safety margin Δh = 0.5m, the optimal parabolic path is obtained by solving a sequential quadratic programming algorithm. The total distance of this path is approximately 22.3m, which is 44.3% shorter than the gate frame path. According to the path energy consumption formula, the total energy consumption is reduced by approximately 42.1%.

[0125] During operation, when the spreader reached above the 5th row, the collision avoidance system detected a truck loading containers in the 6th row, with a roof height of 4.5m. The system immediately updated the height value of the corresponding grid cell in the yard outline height map and triggered real-time replanning to generate a new path segment to bypass the truck. In the new path segment, the height of the spreader above the 6th row was adjusted to 4.5 + 0.5 = 5.0m (obstacle height plus safety margin), ensuring safe obstacle avoidance. The entire replanning process was completed within 50ms, and the spreader operation was unaffected.

[0126] In summary, this invention achieves intelligent path planning and safety control for rail-mounted gantry cranes by organically combining multi-sensor fusion perception, yard contour height map construction, multi-objective optimization planning of curved paths, and three-level safety distance graded collision avoidance control. While ensuring operational safety, it significantly improves operational efficiency and energy utilization efficiency, and has broad prospects for engineering applications.

[0127] It should be noted that, in the specific implementation process, the above-mentioned control part can be implemented by a hardware processor executing computer-executable instructions in software form stored in memory, which will not be elaborated here. The programs corresponding to the actions performed by the above control circuit can all be stored in the computer-readable storage medium of the system in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0128] The computer-readable storage media mentioned above may include volatile memory, such as random access memory; may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; and may also include combinations of the above types of memory.

[0129] The term "processor" as mentioned above can also refer to a collective of multiple processing elements. For example, a processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor, and it can also be a special-purpose processor.

[0130] It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An automated control method for rail-mounted crane lifting devices, characterized in that, The method includes: S1: The multi-sensor sensing module installed on the rail-mounted gantry crane and spreader scans the yard environment in real time, acquires three-dimensional point cloud data and image information, and uses the extended Kalman filter algorithm to fuse the multi-sensor data to construct a real-time three-dimensional environment model of the yard. S2: Based on the point cloud data of the real-time three-dimensional environment model, the storage yard is divided into grids, the maximum obstacle height in each grid cell area is extracted, and a storage yard outline height map is generated. S3: Based on the starting and target position coordinates of the spreader, and combined with the obstacle height information of each grid cell in the yard outline height map, the optimal travel path in the form of a curve is generated with the shortest total path distance and the lowest energy consumption as the multi-objective optimization objectives, while satisfying the safety distance constraint; the curve is a parabolic path or an elliptical arc path. S4: During the operation of the spreader along the optimal travel path, the closest distance between the spreader and surrounding obstacles is detected in real time, and the corresponding anti-collision control response strategy is executed according to the preset three-level safety distance threshold.

2. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, The multi-sensor sensing module includes: The lidar installed at the center of the bottom of the vehicle is used for 360° three-dimensional point cloud scanning. The binocular vision cameras installed on both sides of the vehicle are used for stereo vision depth imaging. Ultrasonic sensors installed at the four corners of the spreader are used for short-range obstacle detection; A position encoder installed on the hoisting mechanism is used to accurately measure the lifting height of the spreader.

3. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, S1 employs an extended Kalman filter algorithm to fuse multi-sensor data, specifically including: The data collected by each sensor is time-synchronized to align the data of each sensor to a unified time reference. All sensor data are uniformly converted to a global coordinate system with the center of the rail-mounted trolley as the origin; Using the extended Kalman filter algorithm, with lidar point cloud data as the subjective measurement and binocular visual depth data and ultrasonic ranging data as auxiliary observations, the three-dimensional position and shape of obstacles in the storage yard are fused and estimated to obtain fused three-dimensional environmental data.

4. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, S2 generates a stockpile outline height map, specifically including: The horizontal area of ​​the storage yard is divided into equidistant grids according to a preset grid size, resulting in M×N grid cells; Iterate through each grid cell and extract the maximum Z-axis coordinate from all 3D point cloud data within that grid cell, using this as the maximum obstacle height for that grid cell. ,in and These are the row and column indices of the grid cells, respectively. A two-dimensional matrix is ​​formed by combining the maximum obstacle heights of all grid cells. The stockpile outline height map is obtained; wherein the stockpile outline height map is dynamically refreshed as the sensor data is updated in real time.

5. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, S3 generates the optimal travel path in the form of a curve, specifically including: Insert at equal intervals along the horizontal direction between the starting position and the target position. One intermediate control point; Query the yard outline height map to obtain the maximum obstacle height at the corresponding location of each intermediate control point, and add the dynamic safety margin to obtain the minimum allowable height of that control point; Using the vertical coordinates of each control point as the optimization variable, the shortest total path length and the lowest energy consumption as the multi-objective optimization function, and the constraint that the vertical coordinates of the control points are not lower than the minimum allowable height, the sequential quadratic programming algorithm is used to solve the problem and obtain the optimal vertical coordinates of each control point. Curve fitting is performed on the starting position, each optimal control point, and the target position to generate a smooth parabolic path or elliptical arc path.

6. The automated rail-mounted crane lifting device control method according to claim 5, characterized in that, The multi-objective optimization function is defined as follows: ； in, This is the total path length. The total energy consumption along the path. and These are the weighting coefficients. .

7. The automated rail-mounted crane lifting device control method according to claim 5, characterized in that, The formula for calculating the dynamic safety margin is as follows: ； in, The basic safety margin ranges from 0.3m to 0.5m. This is the speed compensation coefficient; This represents the current operating speed of the spreader; Load compensation coefficient The mass of the container being transported.

8. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, The three-level safety distance thresholds and the corresponding collision avoidance control response strategies are as follows: Level 1: Emergency stop area. When D≤D1, emergency braking is performed immediately, all movements are stopped, and an acousto-optic alarm is triggered; Level 2: Deceleration area. When D2<D≤D3, the speed is automatically reduced to 30% to 50% of the rated speed, and the path replanning module is activated at the same time to generate an obstacle avoidance path; Level 3: Safety area. When D>D3, the spreader operates at normal speed according to the optimal travel path; Wherein, D is the shortest distance between the spreader and surrounding obstacles, and D1, D2 and D3 are three-level safety distance thresholds respectively.

9. The automated rail-mounted crane lifting device control method according to claim 8, characterized in that, The safety distance thresholds D1, D2 and D3 are dynamically adjusted according to the operating speed and load status of the spreader, and the adjustment formula is: ； in, For the first The baseline value for the safety distance threshold. ; This is the speed influence coefficient; Current running speed; Maximum operating speed; This is the load influence coefficient; The current load mass; This is the maximum rated load mass.

10. The automated rail-mounted crane lifting device control method according to claim 1, characterized in that, The method further comprises: During the operation of the spreader, the yard contour height map is dynamically updated at a preset time period; When the height change of a grid cell in the yard contour height map exceeds a preset threshold, real-time path replanning is triggered; The replanning only optimizes and solves the remaining path segment from the current position of the spreader to the target position, and the new path remains continuous with the traveled path; The trolley traveling drive and the hoisting mechanism drive adopt a synchronous linkage control mode, so as to realize smooth motion trajectory tracking of the spreader along the curved path.

Images