Cooperative operation method for multiple long-distance gantry cranes

Abstract

The invention relates to the technical field of crane control, and discloses a long-distance collaborative operation method for multiple gantry cranes, which comprises the following steps: constructing a global coordinate system; acquiring position data of dynamic equipment of each crane in real time and mapping the position data to a global coordinate system to form an equipment coordinate set; synchronously processing all equipment coordinates through a central processing unit, and generating a position sharing data set; meanwhile, environment obstacle data are collected and mapped to a global coordinate system, and spatial constraint conditions are established; generating a first path scheme by adopting a collaborative planning algorithm based on the position sharing data and the spatial constraint condition; monitoring the operation deviation in real time in the execution process, and obtaining an optimized second path scheme through deviation correction; and finally, dynamically monitoring environment change, triggering obstacle avoidance path updating when the environment change exceeds a preset threshold value, and outputting a collaborative operation scheme. According to the method provided by the invention, multi-machine cooperative path planning and dynamic obstacle avoidance under a cross-spatial scale are realized, the conflict risk is reduced, and the operation continuity and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of crane control, and in particular to a method for coordinated operation of multiple gantry cranes at a long distance. Background Art

[0002] In large-scale construction projects (such as port loading and unloading, nuclear power installation, and bridge prefabrication), multiple (≥3) gantry cranes are often required to work collaboratively over long distances (>1km) and across regions. Each crane relies on an independent local coordinate system, which prevents direct correlation of cross-machine position data. When the operating radius exceeds 500 meters, the accumulated coordinate conversion error can reach more than ±0.5m, causing path conflicts or lifting deviations. In addition, path planning often uses a static map pre-loading mode, requiring manual updating of obstacle data. When temporary stacking of goods (such as suddenly stacked containers) or sudden changes in wind speed cause the boom to swing, the system cannot adaptively adjust the path, resulting in downtime and waiting. In actual operation, the crane will produce systematic offsets due to track settlement and load deformation (for example, the gantry will offset 0.3 meters laterally after traveling 10 meters). Traditional methods require global recalculation of the path, which takes more than 30 seconds and interrupts the operation process. This results in path conflicts, obstacle avoidance failures, and poor real-time performance due to the large spatial span and high dynamics of the equipment. Summary of the Invention

[0003] To this end, the purpose of the present invention is to solve the problems in the prior art of path conflicts, obstacle avoidance failure and poor real-time performance caused by the large spatial span and high dynamics of the equipment when multiple gantry cranes work together over a long distance. A method for collaborative operation of multiple gantry cranes over a long distance is provided to realize multi-machine collaborative path planning and dynamic obstacle avoidance across spatial scales, reduce the risk of conflict, and improve operation continuity and safety.

[0004] To solve the above technical problems, the present invention provides a method for the coordinated operation of multiple gantry cranes at a long distance, comprising: Construct a global coordinate system; Acquire real-time position data of a gantry crane including dynamic equipment, map the real-time position data to the global coordinate system, and obtain a set of equipment coordinates; The device coordinate sets of all the portal cranes are synchronously transmitted to a central processor to obtain a position shared data set; Obtaining obstacle position data in the working environment, mapping the obstacle position data to the global coordinate system, and obtaining spatial constraint conditions; Combining the location sharing data set and the spatial constraint condition, performing collaborative path planning for the multiple gantry cranes to obtain a first path solution; Running the first path plan, obtaining an operation deviation of each of the dynamic devices, and correcting the first path plan based on the operation deviation to obtain a second path plan; The second path plan is run, and environmental changes in the operating environment are monitored. If the monitored environmental changes exceed a preset threshold, an obstacle avoidance path update is triggered to obtain a collaborative operation plan for multiple gantry cranes at a long distance.

[0005] Preferably, constructing a global coordinate system includes: obtaining initial position data of multiple gantry cranes, extracting the physical coordinates of the local origin of each gantry crane in the global space from the initial position data; calculating the minimum distance between devices and the standard deviation of coordinates in each direction based on the physical coordinates; judging whether the distribution of the multiple gantry cranes meets the preset uniformity condition based on the minimum distance and the standard deviation of coordinates in each direction: if the uniformity condition is met, calculating the average value of all the physical coordinates as the origin of the global coordinate system; if the uniformity condition is not met, calculating the weighted average value of all the physical coordinates according to preset weights as the origin of the global coordinate system; wherein the preset weights are determined according to the load capacity of the gantry cranes.

[0006] Preferably, the uniformity condition includes: D min ≥D* and ; Among them, D min Indicates the minimum distance between devices; D* indicates the safety distance threshold; Indicates the standard deviation of the X-direction coordinate; Indicates the standard deviation of the Y-direction coordinate; Indicates the standard deviation threshold.

[0007] Preferably, combining the location sharing dataset and the spatial constraint condition, performing collaborative path planning on the plurality of gantry cranes to obtain a first path solution includes: Step 1: generating an initial path point sequence for each gantry crane according to the equipment coordinate set and the spatial constraint conditions; Step 2: Based on the initial path point sequence and the location sharing dataset, predict the multi-machine motion trajectories in the future time period and detect spatial and temporal conflicts; Step 3: For the gantry cranes detected to be in conflict, determine the crane priority based on the load capacity of the gantry cranes, execute an adjustment strategy based on the crane priority, and obtain a temporary path point sequence; Step 4: Combining the temporary path point sequence and the spatial constraints, a path planning algorithm is used to avoid static obstacles and dynamic obstacles by inserting deceleration path points or detour path points to obtain a first path point sequence; Step 5: Perform conflict verification on the first path point sequence. If it fails the verification, return to step 2 and iterate until the conflict is cleared to obtain the first path solution.

[0008] Preferably, the adjustment strategy includes: freezing the waypoint adjustment authority of cranes with high crane priority; and inserting new waypoints for cranes with low crane priority.

[0009] Preferably, inserting new waypoints for the crane with a low crane priority includes inserting at least three new waypoints, and the three new waypoints are evenly distributed around the conflict area.

[0010] Preferably, correcting the first path plan based on the operation deviation to obtain the second path plan includes: analyzing the operation deviation to obtain a deviation type; the deviation type includes system deviation, local deviation and dynamic deviation; correcting the first path plan according to the deviation type to obtain the second path plan; Among them, for the system deviation, the global offset is calculated, and all path points in the first path plan are translated and corrected by the global offset; for the local deviation, the compensation path point is inserted into the path segment corresponding to the local deviation; for the dynamic deviation, the local obstacle avoidance planning is called to update the path segment corresponding to the dynamic deviation.

[0011] Preferably, the interpolated compensation path points are generated by cubic spline curves.

[0012] Preferably, monitoring the environmental changes of the working environment includes: monitoring the Euclidean distance between the current position of a static obstacle and the initial position of the static obstacle; monitoring the minimum distance between the current position of a dynamic obstacle and the path points included in the second path plan; and monitoring the current speed change rate of the dynamic obstacle.

[0013] Preferably, if the Euclidean distance between the current position of the static obstacle and the initial position of the static obstacle exceeds 1.2~1.5m, the minimum distance between the current position of the dynamic obstacle and the path point included in the second path plan exceeds 0.5~0.8m, and the current speed change rate of the dynamic obstacle exceeds 0.1~0.3m / s, the obstacle avoidance path update is triggered when any one of the three is met.

[0014] The above technical solution of the present invention has the following beneficial effects compared with the prior art: The method for collaborative operation of multiple gantry cranes over a long distance described in the present invention realizes multi-machine collaborative path planning and dynamic obstacle avoidance across spatial scales, reduces conflict risks, and improves operation continuity and safety.

[0015] Among them, by constructing a mapping from the global coordinate system to real-time position data, and then centrally synchronizing it with the central processor, multiple gantry cranes that were originally scattered within an operating range of hundreds of meters or even kilometers are unified into the same time and space benchmark, eliminating the accumulation of local coordinate system errors, improving the consistency of global position data, and laying a data foundation for multi-machine collaboration.

[0016] The device coordinate sets of all cranes are continuously aggregated to the central processor to form a position shared data set. The position changes of any device are globally visible, avoiding "blind collaboration" and significantly reducing the risk of collision due to information lag.

[0017] During the path planning stage, the obstacle position data in the working environment is mapped to spatial constraints in the global coordinate system, and environmental changes are continuously monitored during the operation stage. When the environmental changes exceed the preset threshold, the obstacle avoidance path is updated. This closed-loop mechanism upgrades traditional static pre-planning to dynamic adaptation, realizing the simultaneous avoidance of static and dynamic obstacles and ensuring operation continuity.

[0018] After running the first path plan, the operating deviations of each dynamic device, including position deviation and time deviation, are collected in real time, and a second path plan is generated based on this. If the environment changes again, it is iterated and updated again. This closed-loop process of "run-detect-correct-rerun" compresses the error correction time from the traditional tens of seconds to seconds, avoiding global shutdown and recalculation, and significantly improving operational efficiency and safety.

[0019] Through unified path planning in the global coordinate system, hard boundary restrictions of spatial constraints, and real-time deviation correction, spatiotemporal conflicts between multiple machines and between equipment and obstacles are systematically eliminated, bringing the risk of conflict to near zero. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 Flowchart of a method for collaborative operation of multiple gantry cranes at a long distance in a preferred embodiment of the present invention; Figure 2 A flowchart of constructing a global coordinate system in a preferred embodiment of the present invention; Figure 3 This is a flow chart of obtaining a first path solution in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0022] The purpose of the embodiments of the present invention is to solve the problems of path conflict, obstacle avoidance failure and poor real-time performance caused by large spatial span and high equipment dynamics when multiple gantry cranes work together at a long distance. A method for collaborative operation of multiple gantry cranes at a long distance is provided to realize multi-machine collaborative path planning and dynamic obstacle avoidance across spatial scales, reduce the risk of conflict, and improve operation continuity and safety.

[0023] Example 1: Reference Figure 1 As shown, an embodiment of the present invention discloses a method for collaborative operation of multiple gantry cranes over a long distance, comprising: S100, constructing a global coordinate system; S200, obtaining real-time position data of a gantry crane including dynamic equipment, mapping the real-time position data to a global coordinate system, and obtaining a set of equipment coordinates; S300, the equipment coordinate sets of all the portal cranes are synchronously transmitted to the central processor to obtain a position sharing data set; S400, obtaining obstacle position data in the working environment, mapping the obstacle position data to a global coordinate system, and obtaining spatial constraint conditions; S500: Combine the location shared data set and the spatial constraint conditions to perform collaborative path planning for multiple gantry cranes to obtain a first path solution; S600: Run the first path plan, obtain the operation deviation of each dynamic device, and correct the first path plan based on the operation deviation to obtain a second path plan; S700: Run the second path plan and monitor environmental changes in the working environment. If the monitored environmental changes exceed a preset threshold, the obstacle avoidance path update is triggered to obtain a collaborative operation plan for multiple gantry cranes at a long distance.

[0024] In specific application scenarios, when multiple gantry cranes operate over long distances, they each use a local coordinate system (the origin is usually located at the base of a single machine). The lack of a unified global reference frame makes it impossible to directly link the position data of different cranes, causing the accumulation of coordinate system offset errors, which affects the accuracy and safety of collaborative operations. For example, the actual physical positions of the "origin (0, 0, 0)" of crane A and the "origin (0, 0, 0)" of crane B may be hundreds of meters apart, resulting in position data conflicts during path planning. When multiple machines collaborate, the difference in coordinate systems leads to the risk of spreader collision or overlapping operating areas. Based on this, in the embodiment of the present invention, the initial position data of the base of each gantry crane is obtained through GPS positioning, and a global coordinate system is constructed based on the initial position data of the bases of multiple gantry cranes to achieve the unification of the spatial reference of multiple devices. The global coordinate system here is the same spatial reference system established for the collaborative operation of multiple gantry cranes, a unified reference framework, including a fixed point in the operation area (such as a control tower) or the center of the equipment cluster as the origin (0, 0, 0), and the definition of standard axes (usually the X-axis points to the east, the Y-axis points to the north, and the Z-axis is vertically upward). By constructing a global coordinate system, multiple gantry cranes originally scattered within an operating range of hundreds or even kilometers are unified to the same spatiotemporal reference, eliminating the accumulation of local coordinate system errors, improving the consistency of global position data, and laying a data foundation for multi-machine collaboration.

[0025] The position and status data of dynamic equipment are fundamental to ensuring safe and efficient collaborative operations of gantry cranes. Multi-sensor fusion (Inertial Measurement Unit (IMU) + LiDAR + Encoders) is used to obtain real-time position data for each gantry crane's dynamic equipment. Dynamic equipment on a gantry crane refers to components whose position or status changes dynamically during crane operation, including the spreader, hoisting mechanism, and trolley / carriage mechanism. Spreaders, as the terminal components that directly carry cargo, directly impact operational accuracy and safety. Dynamic equipment position data is a key input for path planning and collision detection. For example, real-time monitoring of the Z-axis height difference between each crane's spreaders can help prevent vertical collisions during lifting. Dynamic equipment position changes are weighted by load capacity to optimize path priority. For example, a fully loaded crane prioritizes low-speed routes. To accurately determine the position of dynamic equipment, high-precision sensors are deployed. Absolute encoders monitor drum rotation angles to convert wire rope lengths; laser rangefinders or encoders combined with track limit switches monitor trolley / carriage mechanism position; and inclination sensors monitor spreader swing angles to compensate for horizontal offset.

[0026] Mapping real-time location data to a global coordinate system yields a set of device coordinates. By mapping real-time location data to a global coordinate system, the local location data of all devices is converted into unified absolute coordinates, eliminating the cumulative error caused by inconsistent coordinate systems and ensuring spatial consistency of multi-device location data.

[0027] The local control of each gantry crane binds the mapped device coordinates with the timestamp and crane ID, encapsulating them into a standard data packet. After compression and encryption, it is transmitted to the central processor using the DDS or TSN protocol to ensure the real-time and accuracy of multi-machine data transmission. Each crane sends a data packet to the central processor every 100ms, and the compressed bandwidth usage does not exceed 1Mbps per crane. The central processor synchronizes the multi-machine data packets based on the timestamp to ensure that all data is based on the same time base. It checks whether the origin of each crane's global coordinate system is consistent with the central system's benchmark and stores the synchronized coordinate data by crane ID to form a location-sharing data set. The synchronous transmission of the device coordinate set to the central processor is the "nerve center" of the gantry crane's collaborative operation. Its core value lies in transforming the dispersed multi-machine data into a globally unified real-time situational awareness capability, providing an irreplaceable data foundation for high-precision path planning, dynamic obstacle avoidance, and task collaboration.

[0028] Obstacle location data acquisition in the work environment involves both static and dynamic obstacle data collection. Static obstacle data is collected using laser scanners mounted on the crane or around the work area, generating point cloud data of the work area. The physical coordinates of fixed obstacles (such as buildings and pillars) are manually calibrated using a total station. Multi-sensor fusion (visual cameras, millimeter-wave radar, and ultra-wideband) is used to track the position data of moving objects (such as vehicles and personnel) in real time. GPS / RTK is used to collect the real-time position and speed of mobile devices (such as automated guided vehicles and trucks). For static obstacle mapping, if the obstacle coordinates are acquired using RTK-GPS or a total station, they are directly converted to the global coordinate system. If the obstacle is acquired through point cloud data, the ICP (Iterative Minimum Point) algorithm is used to align the local point cloud to the global coordinate system. For dynamic obstacle mapping, the global coordinate system offset is directly overlaid on the GPS / RTK data of the dynamic device. For dynamic targets detected by vision or radar, their global coordinates are obtained through multi-sensor data fusion.

[0029] By constructing a mapping from the global coordinate system to real-time position data and then centrally synchronizing it on the central processor, multiple gantry cranes originally scattered within an operating range of hundreds or even kilometers are unified into the same time and space benchmark, eliminating the accumulation of local coordinate system errors, improving the consistency of global position data, and laying a data foundation for multi-machine collaboration.

[0030] Spatial constraints refer to a set of three-dimensional spatial restriction rules, consisting of information such as the position, shape, and motion state of static and dynamic obstacles in a collaborative operating environment involving multiple gantry cranes. These constraints are used to define the safety boundaries and dynamic avoidance requirements that cranes and their spreaders must adhere to during operation. Static obstacles (such as buildings, bridge piers, and fixed equipment bases) are defined by their three-dimensional coordinates and geometric shapes (such as cuboids, cylinders, and irregular polygons). Crane spreaders must maintain a minimum safe distance from the surfaces of static obstacles to avoid collision risks. The positions of dynamic obstacles (such as AGVs, transport vehicles, and personnel) change over time, and their constraints are defined by predicted trajectories. Based on real-time speed and direction, linear extrapolation or kinematic models (such as uniformly accelerated motion) are used to predict the position sequence for future time periods. The minimum distance between the crane path point and the predicted trajectory of the dynamic obstacle must be greater than a safety threshold.

[0031] The real-time location coordinates, operating status, and timestamps of each crane's dynamic equipment are obtained from the shared location dataset. Based on spatial constraints, the boundary coordinates and safety distance thresholds of static obstacles, as well as the real-time locations and predicted trajectories of dynamic obstacles, are obtained, along with the minimum safety spacing and priority rules between cranes. Combining the shared location dataset and spatial constraints, a path planning algorithm is used to perform collaborative path planning for multiple gantry cranes. An initial path point sequence is generated for each crane, satisfying the spatial constraints and resulting in a first path solution. Global path planning is used to avoid physical collisions between cranes and between cranes and obstacles.

[0032] The central processing unit (CPU) sends operating instructions (target position, velocity, and acceleration) to each crane controller based on the timestamp. The crane servo system executes motion control, driving the spreader, trolley, and carriage according to the first path plan. It also obtains the crane's real-time motion status, including its real-time position, acceleration, and velocity. It calculates the position and time deviation between the real-time position and the corresponding path point in the first path plan. Based on the position and time deviations, it corrects the path point sequence in the first path plan to obtain the second path plan. Real-time monitoring and correction ensure that the crane's actual motion trajectory is consistent with the planned path, preventing cumulative errors that could lead to collisions or mission failures.

[0033] The central processing unit sends operating instructions (target position, speed, acceleration) to each crane controller based on the timestamp. The crane servo system performs motion control, driving the spreader, trolley, and carriage to move according to the second path plan and obtaining the crane's real-time motion status, including real-time position, acceleration, and speed. A laser scanner or visual camera is used to obtain the latest position of static obstacles in the work area, and the Euclidean distance between the current static obstacle position and the initial recorded position is calculated. Multi-sensor fusion is used to track the position and speed of moving objects in real time, and the minimum distance between dynamic obstacles and each path point in the second path plan and the rate of change of the dynamic obstacle's speed are calculated. This generates an environmental change dataset and determines whether the Euclidean distance between the current static obstacle position and the initial recorded position, the minimum distance between dynamic obstacles and each path point in the second path plan, and the rate of change of the dynamic obstacle's speed exceed a threshold. If the threshold is exceeded, an obstacle avoidance path update is triggered to obtain the final collaborative work plan.

[0034] After running the first path plan, the operating deviations of each dynamic device, including position deviation and time deviation, are collected in real time, and a second path plan is generated based on this. If the environment changes again, it is iterated and updated again. This closed-loop process of "run-detect-correct-rerun" compresses the error correction time from the traditional tens of seconds to seconds, avoiding global shutdown and recalculation, and significantly improving operational efficiency and safety.

[0035] Through unified path planning in the global coordinate system, hard boundary restrictions of spatial constraints, and real-time deviation correction, spatiotemporal conflicts between multiple machines and between equipment and obstacles are systematically eliminated, bringing the risk of conflict to near zero.

[0036] If the initial positions of the cranes are unevenly distributed (such as locally clustered or dispersed), simply taking the average of the physical coordinates as the origin will cause the origin to deviate from the actual operation center, further amplifying the path planning error. Specifically, the crane-dense area has a large impact on the origin, and the sparse area is ignored, resulting in significant deviations in the path planning of cranes far away from the dense area; in dynamic operations, the origin deviation accumulates over time, exacerbating the risk of collision. Based on this, on the basis of the above embodiment, refer to Figure 2 As shown, constructing a global coordinate system includes: obtaining initial position data of multiple gantry cranes, extracting the physical coordinates of the local origins of all gantry cranes in the global space from the initial position data; calculating the minimum distance between devices and the standard deviation of coordinates in each direction based on the physical coordinates; judging whether the distribution of the multiple gantry cranes meets the preset uniformity condition based on the minimum distance and the standard deviation of coordinates in each direction: if the uniformity condition is met, calculating the average value of all physical coordinates as the origin of the global coordinate system; if the uniformity condition is not met, calculating the weighted average value of all physical coordinates as the origin of the global coordinate system based on preset weights; wherein the preset weights are determined according to the load capacity of the gantry cranes.

[0037] In specific application scenarios, the initial position data of a gantry crane refers to the three-dimensional coordinates of its base in global space collected by the crane before the operation begins. This data serves as the initial input for constructing the global coordinate system and is used for subsequent calculations of the equipment distribution and the origin of the coordinate system. The origin of each crane's local coordinate system (i.e., the local origin) is mapped to the global coordinate system to obtain the physical coordinates in the global space. If the crane base is displaced due to wind or foundation settlement, the initial position data is updated. The minimum distance between devices is the minimum three-dimensional Euclidean distance of the local origins of multiple gantry cranes in global space, that is, the shortest spatial distance between any two cranes. It reflects the spatial density between cranes and directly affects the judgment of coordinate system uniformity. If the minimum distance is too small, the global origin needs to be optimized through weighted averaging to avoid local aggregation leading to coordinate system offset. The standard deviation of coordinates in each direction is the degree of dispersion of the crane's local origin in the X, Y, and Z coordinate axes in the global space. The standard deviations of the X and Y axes reflect the degree of dispersion of the crane's distribution in the horizontal plane, and the standard deviation of the Z axis reflects the height difference in the vertical direction. If the standard deviation in a certain direction is too large, it means that the crane is unevenly distributed in that direction, and the origin position needs to be optimized through weighted averaging.

[0038] By mapping the local origin coordinates to the global coordinate system and calculating the origin using either arithmetic or weighted average based on uniformity, local coordinate system offset errors are eliminated. When the distribution is uniform, the arithmetic average origin ensures balanced weighting of each crane position, with path planning deviations less than 0.2m. When the distribution is uneven, the load weight optimizes the origin position, improving path accuracy for high-load cranes by over 30% (positioning error reduced from ±0.5m to ±0.3m). Regularly updating the global origin adapts to crane position drift caused by foundation settlement, wind effects, and other factors, maintaining long-term operational accuracy within ±0.2m.

[0039] Specifically, the uniformity conditions include: min ≥D* and ; Among them, D min Indicates the minimum distance between devices; D* indicates the safety distance threshold, which ranges from 5 to 10 meters; Indicates the standard deviation of the X-direction coordinate; Indicates the standard deviation of the Y-direction coordinate; Indicates the standard deviation threshold, ranging from 1 to 1.5m.

[0040] Based on the above embodiments, Figure 3 As shown in the figure, by combining the location sharing dataset and spatial constraints, collaborative path planning is performed for multiple gantry cranes to obtain the first path solution, including: Step 1: Generate an initial path point sequence for each gantry crane based on the equipment coordinate set and spatial constraints; Step 2: Based on the initial path point sequence and the shared location dataset, predict the multi-machine movement trajectories in the future time period and detect spatial and temporal conflicts; Step 3: For the gantry cranes detected to be in conflict, the crane priority is determined based on the load capacity of the gantry cranes. The adjustment strategy is executed based on the crane priority to obtain a temporary path point sequence. Step 4: Combine the temporary waypoint sequence and spatial constraints, use a path planning algorithm to avoid static obstacles, and insert deceleration waypoints or detour waypoints to avoid dynamic obstacles, to obtain the first waypoint sequence; Step 5: Perform conflict verification on the first path point sequence. If it fails the verification, return to step 2 and iterate until the conflict is cleared to obtain the first path solution.

[0041] In the specific application scenario, the spreader of each crane is moved from the current position to the target operation point, and the kinematic and spatial constraints are met. The fast expansion random number algorithm is used, and the key parameter is set to the expansion step δ = 0.5m; the target deviation probability p goal = 0.3 (to guide the tree growth toward the target point); number of iterations N = 1000 (to ensure path convergence). During the expansion process, nodes whose distance from the static obstacle boundary is less than the safety distance threshold are eliminated. Based on the predicted trajectory of dynamic obstacles, nodes that may collide within the next ΔT time are eliminated. Nodes whose distance from other crane path points is less than the multi-crane safety distance are also eliminated to obtain the initial path point sequence.

[0042] Spatial conflict occurs when the three-dimensional positions of the dynamic equipment of multiple gantry cranes approach each other at a certain moment or within a certain period of time during operation, resulting in the minimum distance being less than the multi-machine safe distance, which may cause a physical collision risk. Specifically, it is manifested as a spatial conflict when the coordinate difference between the spreaders of two cranes in the X, Y, and Z directions is less than the safe distance. Spatial conflict detection prevents direct collisions between cranes or between cranes and fixed structures, ensuring operational safety.

[0043] Temporal conflict occurs when the dynamic movements of multiple gantry cranes are not only spatially close (satisfying spatial conflict conditions) but also temporally overlapping. This means that two cranes arrive at the same spatial location or intersection within the same timeframe, leading to a potential collision risk. Specifically, crane A arrives at pathpoint P1 at time t1, and crane B arrives at pathpoint P2 (spatially close to P1) at time t2. If the absolute value of t1 − t2 is less than a preset time difference (e.g., 30 seconds), a temporal conflict is identified. By detecting temporal conflicts, dynamic collisions caused by overlapping motion sequences (such as rear-end collisions or sideways collisions caused by one crane failing to decelerate in time) can be prevented.

[0044] When spatial or temporal conflicts are detected, the paths of lower-priority cranes are dynamically adjusted based on pre-set crane priorities, generating a new temporary path node sequence to avoid conflicts and maintain multi-machine coordination efficiency. This adjustment strategy includes freezing path point adjustment permissions for cranes with higher priorities to ensure they execute according to the original plan; and inserting new path points for cranes with lower priorities, including at least three new path points evenly distributed around the conflict area, to guide lower-priority cranes to detour or slow down. This minimizes the path adjustment costs for lower-priority cranes while ensuring the progress of higher-priority tasks, maintaining overall operational efficiency.

[0045] Inserting deceleration points: When dynamic obstacles (such as moving vehicles) or time conflicts are detected, speed reduction nodes are inserted at specific locations in the crane's path, allowing the crane to slow down in advance and avoid collisions with obstacles or high-priority cranes. Specifically, deceleration points are inserted before the conflict zone to reduce the speed to a safe level. This reduction in speed reduces braking distance and prevents load sway or equipment impact caused by emergency stops.

[0046] Detour path points: In the area where static obstacles (such as newly added containers) or dynamic obstacles overlap, new nodes that deviate from the original path are inserted to guide the crane around the obstacles and replan a safe path.

[0047] After adjusting the waypoints, the new waypoint sequence is rechecked for global or local collision risks to confirm whether spatial and temporal conflicts still exist, ensuring the safety and feasibility of the path plan. Specifically, the minimum distance between all crane waypoints is recalculated, multi-crane motion estimation is simulated, time window overlap is checked, and based on the latest obstacle position and speed predictions, deceleration or detours are confirmed to effectively avoid risks.

[0048] On the basis of the above embodiments, the first path plan is corrected based on the operation deviation to obtain the second path plan, including: analyzing the operation deviation to obtain the deviation type; the deviation type includes system deviation, local deviation and dynamic deviation; correcting the first path plan according to the deviation type to obtain the second path plan; wherein, for the system deviation, the global offset is calculated, and all path points in the first path plan are translated and corrected by the global offset; for the local deviation, the compensation path point is inserted in the path segment corresponding to the local deviation; for the dynamic deviation, the local obstacle avoidance planning is called to update the path segment corresponding to the dynamic deviation.

[0049] In specific application scenarios, the deviation type refers to the difference between the actual motion trajectory and the planned path when a multi-gantry crane executes the first path plan. It is divided into three categories based on the source of the deviation and the scope of influence: System deviation: A global deviation in the paths of multiple cranes caused by drift of the global coordinate system origin, clock synchronization errors, or overall offset of the global positioning system. This is manifested as the path points of all cranes offset in the same direction or distance, but the local trajectory shape remains unchanged.

[0050] Local deviation: Local deviation of a single crane's path caused by single-machine sensor noise, actuator lag (such as motor response delay), or local environmental interference (such as wind-induced spreader swing). This is manifested as jitter or offset of the path points in a local area, but the overall trend remains unchanged.

[0051] Dynamic deviation: Real-time path conflicts caused by the movement of dynamic obstacles (such as AGVs and personnel) or environmental changes (such as temporarily stacked goods). This is manifested as the risk of intersection between the original planned path and the future trajectory of the dynamic obstacle, requiring dynamic path adjustment to avoid it.

[0052] Compensation path points are inserted into the path segments corresponding to the local deviation. By comparing the real-time position data with the planned path, path segments with deviations exceeding a threshold (e.g., 0.2 m) are identified. At least three new path points are inserted before and after the deviation segment using cubic spline interpolation to ensure a smooth path transition. The interpolation point coordinates are calculated through local trajectory fitting to ensure that the jerk is less than or equal to 5 m / s. 3 The insertion point interval is less than or equal to 0.5m, covering the deviation area. By inserting compensation path points, the spreader swing or positioning jitter caused by sensor noise is suppressed, and the path smoothness is significantly improved.

[0053] Invoke local obstacle avoidance planning and update the path segments corresponding to the dynamic deviations: Based on the predicted trajectory of dynamic obstacles (linear extrapolation or Kalman filtering), locate the path segments within the future time period ΔT that intersect with the crane path. Insert deceleration path points before the intersection to reduce the speed to a safe speed. Invoke the A* algorithm (A-Star Algorithm) to generate subpaths that avoid dynamic obstacles and insert 3-5 new path points.

[0054] The use of cubic spline curves to generate and insert compensation path points has specific physical significance: First, the curve passes through all compensation points, ensuring a seamless path. The crane hoist moves strictly according to the planned trajectory, avoiding position jumps. Second, the continuous first-order derivative of the cubic spline curve allows for smooth transitions in the tangent direction and a continuous rate of velocity change, ensuring no sudden changes in crane speed, reducing motor shock and load inertia. Third, the continuous second-order derivative of the cubic spline curve allows for smooth curvature changes and limited jerk, reducing mechanical structural stress, preventing violent load swings, and improving motion stability. Furthermore, only a small number of compensation points need to be inserted within the deviation segment, resulting in high computational efficiency and rapid response to local deviations without compromising global path planning efficiency.

[0055] Based on the above embodiments, monitoring of environmental changes in the working environment includes: monitoring the Euclidean distance between the current position of a static obstacle and the initial position of the static obstacle; monitoring the minimum distance between the current position of a dynamic obstacle and the path points included in the second path solution; and monitoring the current speed change rate of the dynamic obstacle.

[0056] In specific application scenarios, by monitoring the Euclidean distance between the current position of a static obstacle and its initial position, it is determined whether the static obstacle has shifted due to external forces (such as wind, mechanical vibration) or human operation, thereby avoiding conflicts between the crane path and the actual position of the obstacle. If the Euclidean distance between the current position of the static obstacle and its initial position exceeds 1.2-1.5m, path replanning is triggered to ensure the minimum safe distance between the crane and the obstacle.

[0057] By monitoring the minimum distance between the current position of the dynamic obstacle and the path points included in the second path plan, it is determined whether the dynamic obstacle has invaded the crane's safety area. If the minimum distance between the current position of the dynamic obstacle and the path points included in the second path plan exceeds 0.5~0.8m, path replanning is triggered to ensure that the crane path avoids moving targets in real time.

[0058] By continuously measuring the speed of dynamic obstacles and calculating their speed change per unit time, i.e., the speed change rate, the system can identify sudden speed changes of dynamic obstacles (such as sudden acceleration or deceleration), predict the uncertainty of their future trajectory, and adjust the crane's obstacle avoidance strategy in advance. If the current speed change rate of a dynamic obstacle exceeds 0.1-0.3 m / s, it is determined to be a sudden speed change, triggering an update to the obstacle avoidance path, thus preventing sudden stops or drastic adjustments of the crane caused by sudden changes in the obstacle trajectory.

[0059] In summary, the method for collaborative operation of multiple gantry cranes over a long distance described in the present invention realizes multi-machine collaborative path planning and dynamic obstacle avoidance across spatial scales, reduces conflict risks, and improves operation continuity and safety.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for collaborative operation of multiple gantry cranes at a distance, characterized in that: include: Construct a global coordinate system; Acquire real-time position data of a gantry crane including dynamic equipment, map the real-time position data to the global coordinate system, and obtain a set of equipment coordinates; The device coordinate sets of all the portal cranes are synchronously transmitted to a central processor to obtain a position shared data set; Obtaining obstacle position data in the working environment, mapping the obstacle position data to the global coordinate system, and obtaining spatial constraint conditions; Combining the location sharing data set and the spatial constraint condition, performing collaborative path planning for the multiple gantry cranes to obtain a first path solution; Running the first path plan, obtaining an operation deviation of each of the dynamic devices, and correcting the first path plan based on the operation deviation to obtain a second path plan; The second path plan is run, and environmental changes in the operating environment are monitored. If the monitored environmental changes exceed a preset threshold, an obstacle avoidance path update is triggered to obtain a collaborative operation plan for multiple gantry cranes at a long distance.

2. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 1, characterized in that: Construct a global coordinate system, including: Acquiring initial position data of a plurality of gantry cranes, and extracting the physical coordinates of the local origin of each gantry crane in the global space from the initial position data; Calculate the minimum distance between devices and the standard deviation of coordinates in each direction based on the physical coordinates; Determine whether the distribution of the plurality of gantry cranes meets a preset uniformity condition based on the minimum distance and the standard deviation of the coordinates in each direction: If the uniformity condition is met, then calculating the average value of all the physical coordinates as the origin of the global coordinate system; If the uniformity condition is not met, a weighted average of all the physical coordinates is calculated according to preset weights as the origin of the global coordinate system; wherein the preset weights are determined according to the load capacity of the gantry crane.

3. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 2, characterized in that: The uniformity conditions include: D min ≥D* and ; Among them, D min Indicates the minimum distance between devices; D* indicates the safety distance threshold; Indicates the standard deviation of the X-direction coordinate; Indicates the standard deviation of the Y-direction coordinate; Indicates the standard deviation threshold.

4. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 1, characterized in that: Combining the location sharing dataset and the spatial constraint condition, collaborative path planning is performed on the multiple gantry cranes to obtain a first path solution, including: Step 1: generating an initial path point sequence for each gantry crane according to the equipment coordinate set and the spatial constraint conditions; Step 2: Based on the initial path point sequence and the location sharing dataset, predict the multi-machine motion trajectories in the future time period and detect spatial and temporal conflicts; Step 3: For the gantry cranes detected to be in conflict, determine the crane priority based on the load capacity of the gantry cranes, execute an adjustment strategy based on the crane priority, and obtain a temporary path point sequence; Step 4: Combining the temporary path point sequence and the spatial constraints, a path planning algorithm is used to avoid static obstacles and dynamic obstacles by inserting deceleration path points or detour path points to obtain a first path point sequence; Step 5: Perform conflict verification on the first path point sequence. If it fails the verification, return to step 2 and iterate until the conflict is cleared to obtain the first path solution.

5. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 4, characterized in that: The adjustment strategy includes: freezing the waypoint adjustment authority of cranes with high crane priority; and inserting new waypoints for cranes with low crane priority.

6. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 5, characterized in that: Inserting new waypoints for the crane with a low crane priority includes inserting at least three new waypoints, and the three new waypoints are evenly distributed around the conflict area.

7. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 1, characterized in that: Correcting the first path plan based on the operation deviation to obtain a second path plan includes: Analyze the operation deviation to obtain the deviation type; the deviation type includes system deviation, local deviation and dynamic deviation; Correcting the first path plan according to the deviation type to obtain a second path plan; wherein, a global offset is calculated for the system deviation, and translation correction is performed on all path points in the first path solution using the global offset; For the local deviation, inserting a compensation path point in the path segment corresponding to the local deviation; In response to the dynamic deviation, local obstacle avoidance planning is called to update the path segment corresponding to the dynamic deviation.

8. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 7, characterized in that: The interpolated compensation path points are generated by a cubic spline curve.

9. The method for coordinated operation of multiple gantry cranes at a distance according to claim 1, characterized in that ,monitoring environmental changes of the operating environment, including: Monitor the Euclidean distance between the current position of a static obstacle and its initial position; Monitoring the minimum distance between the current position of the dynamic obstacle and the path point included in the second path plan; Monitor the current speed change rate of dynamic obstacles.

10. The method for coordinated operation of multiple gantry cranes at a long distance according to claim 9, characterized in that: If the Euclidean distance between the current position of a static obstacle and its initial position exceeds 1.2-1.5m, the minimum distance between the current position of a dynamic obstacle and the path points included in the second path plan exceeds 0.5-0.8m, or the current speed change rate of the dynamic obstacle exceeds 0.1-0.3m / s, any of the three conditions will trigger an obstacle avoidance path update.

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