Method for constructing a weight lifting route planning model, weight lifting route planning method, and crane

Abstract

The present invention provides a lifting path planning model construction method, a lifting path planning method and a crane, the lifting path planning model construction method includes the steps of: creating a crane model; constructing a lifting system configuration space model including upper body data and lower body data of the crane according to the current work scene and the crane model; generating upper body grid map data of the crane for the lifting system configuration space model and the upper body data, generating lower body grid map data of the crane for the lifting system configuration space model and the lower body data; and using the A-star algorithm to combine the upper body grid map data and the lower body grid map data to construct a lifting path planning model. Since the constructed lifting path planning model is based on the upper body grid map data and the lower body grid map data, the entire path is divided into two groups, the upper body and the lower body, which effectively reduces the amount of data during path searching and improves the efficiency of path planning.

Description

Technical Field

[0001] The present invention relates to the field of route planning technology, and particularly to a method for constructing a hoisting route planning model, a hoisting route planning method, and a crane.

Background Art

[0002] With the complexity of hoisting construction sites and the requirements for the safety and accuracy of hoisting operations, the difficulty of hoisting work has increased. For a single hoisting task, in addition to the crane driver, one or more assistants are often required. At the same time, the quality of hoisting work is also greatly affected by the level of the driver. In recent years, with the development and application of technologies such as digital twins and smart construction sites, certain practical value has been recognized in hoisting route planning.

[0003] Conventionally, most hoisting systems based on hoisting route planning have a huge amount of data, and the efficiency during route search is relatively low.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention eliminates the drawback that the planning efficiency of the hoisting route in the prior art is poor, divides the hoisting route into an upper body route plan and a lower body route plan, thereby reducing the amount of data during route search, and realizing the improvement of route planning efficiency. The present invention provides a method for constructing a hoisting route planning model, a hoisting route planning method, and a crane.

Means for Solving the Problems

[0005] The present invention provides a method for constructing a hoisting route planning model, and the method for constructing the hoisting route planning model includes: creating a crane model; constructing a hoisting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model; For the hoisting system configuration space model and the upper body data, generating the upper body grid map data of the crane, and for the hoisting system configuration space model and the lower body data, generating the lower body grid map data of the crane; Constructing a hoisting path planning model by using the A-star algorithm and combining the upper body grid map data and the lower body grid map data.

[0006] According to the hoisting path planning model construction method provided by the present invention, the upper body data includes the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook. The step of generating the upper body grid map data of the crane for the hoisting system configuration space model and the upper body data includes: Determining the lifting length of the hook; Dividing the lifting length of the hook into a preset number of lifting intervals; For the endpoints of each lifting interval, performing a traversal search within the hoisting system configuration space model based on the main boom luffing angle and the upper body slewing angle, calculating the upper body collision information, and generating the upper body grid map data of the crane.

[0007] According to the hoisting path planning model construction method provided by the present invention, the lower body data includes a traveling parameter and a steering parameter. The step of generating the lower body grid map data of the crane for the hoisting system configuration space model and the lower body data includes: Performing a scanning traversal within the hoisting system configuration space model based on the traveling parameter and the steering parameter to obtain the lower body collision information; Generating the lower body grid map data of the crane based on the lower body collision information.

[0008] According to the method for constructing a weight-lifting path planning model provided by the present invention, in the step of constructing a weight-lifting path planning model by using the A* algorithm and combining the upper vehicle body grid map data and the lower vehicle body grid map data, the step of performing path planning on the upper vehicle body grid map data and the lower vehicle body grid map data respectively by using the A* algorithm to obtain an upper vehicle body path planning model and a lower vehicle body path planning model, and the step of constructing a weight-lifting path planning model by combining the upper vehicle body path planning model and the lower vehicle body path planning model.

[0009] The present invention further provides a weight-lifting path planning method, and the weight-lifting path planning method includes: the step of determining the starting point and the ending point of the weight-lifting path, inputting the coordinates of the starting point and the coordinates of the ending point into the weight-lifting path planning model obtained by the method for constructing a weight-lifting path planning model according to any one of the above, and outputting a weight-lifting planned path as the optimal weight-lifting path.

[0010] According to the weight-lifting path planning method provided by the present invention, after the step of outputting the weight-lifting planned path, further: for each of the upper vehicle body grid map data and the lower vehicle body grid map data, the step of searching for an upper vehicle body grid map data node and a lower vehicle body grid map data node from the starting point, the step of determining an actual path cost and a predicted cost for each of the upper vehicle body grid map data nodes and the lower vehicle body grid map data nodes, marking the actual path cost and the predicted cost in an open list, searching for a node with the minimum total cost in the open list, starting the search with the new starting point, and searching until the ending point.

[0011] According to the weight lifting route planning method provided by the present invention, after the step of outputting the weight lifting planned route, further, converting the weight lifting planned route into a crane operation sequence based on a weight lifting system configuration space model; and generating a crane control command based on the operation sequence.

[0012] The present invention further provides a weight lifting route planning model construction device, and the weight lifting route planning model construction device includes: a simulation module used to create a crane model; a configuration space module used to construct a weight lifting system configuration space model including upper body data and lower body data of a crane based on the current working scene and the crane model; a grouping processing module used to generate upper body grid map data of the crane for the weight lifting system configuration space model and the upper body data, and generate lower body grid map data of the crane for the weight lifting system configuration space model and the lower body data; a construction module used to construct a weight lifting route planning model by using the A* algorithm and combining the upper body grid map data and the lower body grid map data.

[0013] The present invention further provides a weight lifting route planning device, and the weight lifting route planning device includes: a determination module used to determine the starting point and the ending point of the weight lifting route; a planning module used to input the starting point and the ending point into a weight lifting route planning model obtained by the weight lifting route planning model construction method according to any one of the above, and output a weight lifting planned route.

[0014] The present invention further provides a crane that executes the weight lifting route planning method according to any one of the above.

[0015] The present invention further provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When executing the program, the processor implements any one of the above-described heavy-lifting path planning model construction methods.

[0016] The present invention further provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements any one of the above-described heavy-lifting path planning model construction methods.

[0017] The present invention further provides a computer program product including a computer program that, when executed by a processor, implements any one of the above-described heavy-lifting path planning model construction methods.

[0018] According to the heavy-lifting path planning model construction method, the heavy-lifting path planning method, and the crane provided by the present invention, the heavy-lifting path planning model construction method includes: creating a crane model; constructing a heavy-lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model; generating upper body grid map data of the crane for the heavy-lifting system configuration space model and the upper body data, and generating lower body grid map data of the crane for the heavy-lifting system configuration space model and the lower body data; and using the A* algorithm to construct a heavy-lifting path planning model by combining the upper body grid map data and the lower body grid map data. Since the constructed heavy-lifting path planning model is based on the upper body grid map data and the lower body grid map data, the entire path is divided into two groups, the upper body and the lower body, the data volume during path search is effectively reduced, and the path planning efficiency is improved.

[0019] To more clearly explain the technical solutions in the present invention or the prior art, the following briefly describes the accompanying drawings required for the description of the embodiments or the prior art. It is obvious that the accompanying drawings in the following description are only some embodiments of the present invention, and those skilled in the art can obtain other accompanying drawings based on these accompanying drawings without creative labor.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0021] To make the objectives, technical solutions and advantages of the present invention clearer, the following clearly and completely describes the technical solutions related to the present invention with reference to the accompanying drawings of the present invention. It is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative labor belong to the protection scope of the present invention.

[0022] With reference to FIGS. 1 to 6, a method for constructing a weight lifting route planning model, a weight lifting route planning method and a crane according to the present invention will be described.

[0023] FIG. 1 is a schematic flow chart of a method for constructing a weight lifting route planning model provided by the present invention.

[0024] As shown in FIG. 1, the method for constructing a weight lifting route planning model provided by an embodiment of the present invention may have a remote control system as an execution entity. Specifically, it includes the following steps.

[0025] In step 101, create a crane model.

[0026] Weight lifting generally refers to the installation and adjustment of equipment by a crane. During inspection and repair processes, various cranes are used to lift equipment, processed products, tools, materials, etc. and change their positions.

[0027] Specifically, first, a crane model needs to be created, that is, the crane is simulated and represented in digital form. It can be understood that the crane is placed in a coordinate system and each component structure of the crane corresponds to different coordinates. By simulating the crane in a database, a crane model is created. For cranes with different specifications, since their own parameters are different, the created crane models are also different.

[0028] In step 102, based on the current working scene and the crane model, construct a weight lifting system configuration space model including the upper body data and lower body data of the crane.

[0029] Determine the current working scene of the crane. The current working scene is the area where the crane works. For example, when the crane is at a construction site, the construction site can be set as the current working scene. Further, place the crane model in the current working scene and construct a weight lifting system configuration space model. Here, the weight lifting system configuration space model may be a multi-dimensional system model. The expression of the weight lifting system configuration space model may be as shown in formula (1). T=(C(p,d),U(α,β,L)) (1)

[0030] Here, C represents the lower body data of the crane, U represents the upper body data of the crane, p represents the Cartesian coordinates of the crane, d represents the direction vector of the crane, α represents the main boom luffing angle, β represents the upper body slewing angle, and L represents the lifting length of the hook. Regarding the jib length data that is omitted here, the jib length can be known in advance. For example, in the case of a lattice jib, the jib length can be calculated by simple addition or subtraction based on the known jibs of each section. In the case of a telescopic jib, it can be measured in advance by a length sensor attached to the jib.

[0031] When the state of the crane is determined, that is, when the crane position does not move and remains unchanged, the mutual conversion from the position coordinates (α, β, L) of the crane to the Cartesian coordinates (x, y, z) is possible.

[0032] Generally speaking, in the hoisting system configuration space model, various states of the crane are shown, including the traveling parameters and steering parameters in the lower body data, the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook in the upper body data. Therefore, the state information of the crane can be more comprehensively reflected by the hoisting system configuration space model.

[0033] In step 103, for the hoisting system configuration space model and the upper body data, generate the upper body grid map data of the crane, and for the hoisting system configuration space model and the lower body data, generate the lower body grid map data of the crane.

[0034] Specifically, after constructing the lifting system configuration space model, it is necessary to perform individual processing on the upper body data and the lower body data of the crane respectively. According to the operating characteristics of the crane, its operation is divided into two combinations. One group is the upper body operation including the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook. The other group is the lower body operation including the traveling parameters and the steering parameters. In this way, by calculating the data of the lifting system configuration space model in two parts, the difficulty of one calculation can be reduced, and the coupling degree can also be reduced.

[0035] When the crane is not moving, that is, under the premise that the traveling parameters and the steering parameters in the lower body data are determined, each of the U coordinates represents a configuration state of the crane. Therefore, it is necessary to create all the upper body grid map data of the upper body data. The upper body grid map data refers to the grid map composed of the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook. That is, for the three degrees of freedom of the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook, there are multiple types of data with different sizes respectively. By arranging and combining them respectively, the overall upper body grid map data can be constructed.

[0036] When the crane moves, that is, when the traveling parameters and / or the steering parameters in the lower body data change, it is necessary to calculate the lower body grid map data of the crane. Here, the generation method of the lower body grid map data is the same as that of the upper body grid map data, and the lower body grid map data represents the data in two degrees of freedom directions.

[0037] In step 104, use the A-star algorithm to construct the lifting path planning model by using the upper body grid map data and the lower body grid map data together.

[0038] Specifically, after obtaining the upper body grid map data and the lower body grid map data, it becomes possible to construct a weight lifting path planning model. The upper body grid map data contains several nodes, and the lower body grid map data also contains several nodes. Each node then constitutes several paths, that is, several weight lifting paths.

[0039] Using the A* algorithm, perform an optimal search in the upper body grid map data and the lower body grid map data. By combining the A* algorithm with the upper body grid map data and the lower body grid map data, the weight lifting path planning model can be successfully constructed. The operating principle of the weight lifting path planning model is to obtain the upper body grid map data and the lower body grid map data within the current working scene, and then use the A* algorithm to perform a traversal search in the upper body grid map data and the lower body grid map data to obtain the target path. On the other hand, planning a path using the A* algorithm has the advantages of excellent global optimality and continuity, and can effectively reduce the configuration data volume and lower the computational complexity.

[0040] The method for constructing a lifting path planning model provided by this embodiment includes the steps of creating a crane model, constructing a lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model, generating upper body grid map data of the crane for the lifting system configuration space model and the upper body data, and generating lower body grid map data of the crane for the lifting system configuration space model and the lower body data, and constructing a lifting path planning model by using the A-star algorithm in combination with the upper body grid map data and the lower body grid map data. Since the constructed lifting path planning model is based on the upper body grid map data and the lower body grid map data, the entire path is divided into two groups of the upper body and the lower body, the data volume during path search is effectively reduced, and the path planning efficiency is improved.

[0041] Furthermore, based on the above-described embodiment, the upper body data in this embodiment includes the main boom luffing angle, the upper body slewing angle, and the lifting length of the hook. Correspondingly, the step of generating the upper body grid map data of the crane for the lifting system configuration space model and the upper body data includes the steps of determining the lifting length of the hook, dividing the lifting length of the hook into a preset number of lifting intervals, and for the endpoints of each lifting interval, performing a traversal search in the lifting system configuration space model based on the main boom luffing angle and the upper body slewing angle, calculating the upper body collision information, and generating the upper body grid map data of the crane.

[0042] Specifically, regarding the relationship among the three operations of the main boom luffing angle, upper body slewing angle, and hook lifting length in the upper body data of the crane, during the operation of the crane, the operation of the hook lifting length is often an operation performed at the start and end of the load lifting process. On the other hand, the main boom luffing angle and the upper body slewing angle are operations in the intermediate process. Therefore, in order to further accelerate the path search, it may be selected to obtain the upper body grid map data using the hook lifting length L as a control parameter.

[0043] After dividing the hook lifting length into a preset number of lifting intervals and then obtaining the endpoints of each lifting interval, that is, assuming L = {L0, L1, L2, L3, L4……Lm}, the corresponding intervals are "L0, L1", "L1, L2", "L2, L3"……"Lm-1, Lm". Then, traverse search is performed with the main boom luffing angle α and the upper body slewing angle β, and (α, β) = {(α0, β0), (α0, β1), (α0, β2)……(α1, β0), (α1, β1), (α1, β2)……(αn, βq)}, calculate the upper body collision information, and generate one group of corresponding grid map data for each L endpoint data. Figure 2 is a schematic diagram of the configuration of the grid map provided by the present invention. As shown in Figure 2, which is a schematic diagram of the grid map, the radial direction is related to the main boom luffing angle, and the rotation angle is the upper body slewing angle. Therefore, each grid of each group of grid map data contains collision information, edge information, load information, etc. For each set hook lifting length L, there is one group of such data corresponding to it, and there are a total of m groups of corresponding grid map data. The grid map data of the entire crane is composed of all m groups of grid map data. By performing path planning on the m groups of grid map data, n effective paths can be obtained (which means there are n types of paths from the starting point to the ending point), compare the n effective paths, and select the optimal path as the current result path.

[0044] Furthermore, based on the above-described embodiments, the lower vehicle body data in this embodiment includes driving parameters and steering parameters. Correspondingly, for the hoisting system configuration space model and the lower vehicle body data, the step of generating the lower vehicle body grid map data of the crane includes: performing a scanning traverse within the hoisting system configuration space model based on the driving parameters and the steering parameters to obtain lower vehicle body collision information; and generating the lower vehicle body grid map data of the crane based on the lower vehicle body collision information.

[0045] Specifically, in the above-described embodiments, the generation method of the upper vehicle body grid map data of the crane was specifically described. Therefore, when the crane moves, it is first necessary to obtain the collision result of the upper vehicle body data, then calculate the collision result of the lower vehicle body data, and combine the upper vehicle body collision result and the lower vehicle body collision result to obtain the final collision result. This actually means ensuring that the entire hoisting system does not collide during the driving, steering of the lower vehicle body of the crane, and the roughing, slewing, and hook lifting of the upper vehicle body.

[0046] Among them, the process of generating the lower vehicle body grid map data is as follows: First, perform a scanning traverse within the hoisting system configuration space model based on the driving parameters and the steering parameters to obtain lower vehicle body collision information, and then generate the lower vehicle body grid map data based on the lower vehicle body collision data. The lower vehicle body grid map data is a one-to-one correspondence between the driving parameters and the steering parameters of the crane. According to the lower vehicle body grid map data, the corresponding steering parameters under all driving parameter conditions can be reflected, and similarly, the corresponding driving parameters under all steering parameter conditions can also be reflected.

[0047] Furthermore, based on the above-described embodiments, in this embodiment, the step of constructing a weight lifting path planning model by using the A* algorithm and combining the upper body grid map data and the lower body grid map data may include: using the A* algorithm to perform path planning for each of the upper body grid map data and the lower body grid map data to obtain an upper body path planning model and a lower body path planning model; and combining the upper body path planning model and the lower body path planning model to construct a weight lifting path planning model.

[0048] Specifically, the A* algorithm is also called an A * search algorithm. The characteristic of the A* algorithm is to introduce global information when inspecting each node that may be on the shortest path, estimate the distance from the current node to the end point, and use it as a measure to evaluate the possibility that the node is on the shortest path. Therefore, in this embodiment, by adopting the A* algorithm, the path planning can be performed better.

[0049] In order to minimize the amount of data processing and increase the speed of data processing during the path planning process, path planning is performed for each of the upper body grid map data and the lower body grid map data, and through grouping processing, the difficulty of one calculation can be reduced and the coupling degree can be reduced. By dividing the weight lifting path planning model into an upper body path planning model and a lower body path planning model, even when there is no movement in the lower body, the upper body path planning can be completed more quickly. Also, by performing path planning using the A* algorithm, global optimization becomes possible.

[0050] Furthermore, based on the above-described embodiments, the step of creating a crane model in this embodiment may include: obtaining structure data including the dimensional information, motion parameters, and load parameters of the crane; and creating a crane model based on the dimensional information, motion parameters, and load parameters.

[0051] Specifically, as a method for obtaining the structural data of the crane, the product manual of the crane may be directly read, key data may be input by a person, or different data may be measured by various sensors, as long as the structural data of the crane can be accurately obtained. After accurately obtaining the dimensional information, motion parameters, and load parameters of the crane, convert them into a spatial model, that is, simulate the crane structure with lines. By accurately obtaining the dimensional information, motion parameters, and load parameters, the accuracy of the crane simulation can also be ensured, and the accuracy of the load lifting path planning model can be improved.

[0052] The present invention further protects a load lifting path planning method based on the same overall inventive concept.

[0053] FIG. 3 is a schematic flow chart of the load lifting path planning method provided by the present invention.

[0054] As shown in FIG. 3, the load lifting path planning method provided by this embodiment may have an in-vehicle control device as the execution entity, or a remote operation terminal, etc., and mainly includes the following steps.

[0055] In step 301, determine the starting point and the ending point of the load lifting path.

[0056] Specifically, when performing path planning, it is first necessary to determine the starting point and the ending point of the crane operation, that is, the starting point and the ending point of the load lifting path. Usually, the starting point of the load lifting may be determined or directly obtained based on a positioning system. Therefore, in a specific implementation, it is not necessary to input the starting point data, and the ending point data may be directly input, that is, only the ending point of the load lifting path needs to be determined. On the other hand, as a method for determining the ending point of the load lifting, the ending point data input by the user may be directly read, or when the user designates a position, the position of the ending point may be automatically identified, as long as the starting point and the ending point of the load lifting path can be effectively obtained.

[0057] In 302, the coordinates of the starting point and the ending point are input into the weight-lifting route planning model obtained by the weight-lifting route planning model construction method described in any one of the above embodiments, and the weight-lifting planned route is output as the optimal weight-lifting route.

[0058] Specifically, when the starting point and the ending point of the weight-lifting route are obtained, the starting point data and the ending point data can be input into the weight-lifting route planning model. The weight-lifting route planning model performs route planning calculations based on the starting point and the ending point, and outputs the weight-lifting planned route.

[0059] Among them, the process of the weight-lifting route planning model planning the weight-lifting route from the starting point to the ending point may be understood as follows: the weight-lifting route planning model first plans the upper vehicle body route, then plans the lower vehicle body route, and then combines the upper vehicle body route and the lower vehicle body route to finally obtain the weight-lifting planned route. By grouping and planning the upper vehicle body route and the lower vehicle body route, the data processing volume can be effectively reduced, and the data processing speed can be improved. The weight-lifting route planning refers to selecting the optimal realization route between the starting point and the ending point. The weight-lifting system configuration space model constructed within the current working scene may be considered as a mesh intersecting vertically and horizontally. By using the A* algorithm, the traversal of each mesh node can be quickly completed, the optimal route can be found, and the weight-lifting route planning can be completed. For example, the finally completed weight-lifting route plan is the route with the shortest time required for weight-lifting. Among them, with the increase in the amount of data, the calculation efficiency of the A* algorithm will decrease to some extent.

[0060] Note that when performing weight-lifting route planning within the same working scene of the same type of crane, it is only necessary to obtain the starting point and the ending point of the weight-lifting route. Also, when there are changes in the crane or the working scene, in order to ensure the accuracy of the route plan, it is necessary to reconstruct the weight-lifting route planning model by reconstructing the weight-lifting system configuration space model.

[0061] Furthermore, based on the above-described embodiments, after the step of outputting the weight-lifting planned route, further, for each of the upper body grid map data and the lower body grid map data, a step of searching for an upper body grid map data node and a lower body grid map data node from the starting point, and for each upper body grid map data node and lower body grid map data node, a step of determining an actual route cost and a predicted cost, marking the actual route cost and the predicted cost in an open list, searching for a node with the minimum total cost in the open list, starting the search as a new starting point, and searching until the end point may be included.

[0062] Specifically, after the weight-lifting route plan is completed, it is necessary to perform calibration correction on the weight-lifting route output from the weight-lifting route planning model. As a correction method, the upper body route and the lower body route may be calibrated respectively. For each of the upper body grid map data and the lower body grid map data, search around from the starting point, determine each upper body grid map data node and lower body grid map data node, and then, for each upper body grid map data node and lower body grid map data node, determine the actual route cost and the predicted cost, and mark the actual route cost and the predicted cost in an open list. The open list refers to the searched notes. Then, search for a node with the minimum total cost in the open list, start the search as a new starting point, repeat the search operation until the end point, and find the optimal route from the open list to be the final weight-lifting planned route, thereby completing the optimization calibration of the weight-lifting planned route.

[0063] Furthermore, based on the above-described embodiments, after the step of outputting the weight-lifting planned route in this embodiment, further, based on the weight-lifting system configuration space model, a step of converting the weight-lifting planned route into a crane operation sequence, and a step of generating a crane control command based on the operation sequence may be included.

[0064] Specifically, after the lifting plan route is determined, it is necessary to convert the lifting plan route into the operation sequence of the crane and generate crane control instructions based on the operation sequence, so as to control the crane to move according to the determined lifting plan route. When the control instructions of the crane control each part of the crane to operate according to the lifting plan route and finally complete the control from the starting point to the ending point of the lifting, the lifting operation by the crane is completed. A reasonable lifting route plan can effectively improve the efficiency of the lifting operation.

[0065] In the present invention, by decoupling the crane operation, it can be flexibly applied to different working modes of the crane, and the calculation efficiency of the route planning module can be improved. In addition, through the hierarchical processing of the upper body and the lower body, the dimensionality reduction of the configuration space can be realized, so that the data volume is reduced, the position coordinate parameters of the crane are standardized, and the performance of the route planning algorithm is improved.

[0066] The present invention further protects a lifting route planning model construction device based on the same overall inventive concept. Hereinafter, the lifting route planning model construction device provided by the present invention will be described, and the lifting route planning model construction device described below and the above-described lifting route planning model construction method can be referred to in correspondence with each other.

[0067] FIG. 4 is a schematic configuration diagram of a lifting route planning model construction device provided by the present invention.

[0068] As shown in FIG. 4, the lifting route planning model construction device provided by an embodiment of the present invention includes a simulation module 401 used to create a crane model, a configuration space module 402 used to construct a lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model, For the hoisting system configuration space model and the upper body data, a group processing module 403 is used to generate the upper body grid map data of the crane. For the hoisting system configuration space model and the lower body data, it is used to generate the lower body grid map data of the crane. A construction module 404 that uses the A* algorithm and combines the upper body grid map data and the lower body grid map data to construct a hoisting route planning model is included.

[0069] The hoisting route planning model construction device provided by this embodiment includes steps of creating a crane model, constructing a hoisting system configuration space model including the upper body data and the lower body data of the crane based on the current working scene and the crane model, generating the upper body grid map data of the crane for the hoisting system configuration space model and the upper body data, generating the lower body grid map data of the crane for the hoisting system configuration space model and the lower body data, and using the A* algorithm to combine the upper body grid map data and the lower body grid map data to construct a hoisting route planning model. Since the constructed hoisting route planning model is based on the upper body grid map data and the lower body grid map data, the entire route is divided into two groups of the upper body and the lower body, the data volume during route search is effectively reduced, and the route planning efficiency is improved.

[0070] Furthermore, the upper body data in this embodiment includes the main boom roughing angle, the upper body slewing angle, and the lifting length of the hook. Specifically, the group processing module 403 determines the lifting length of the hook, divides the lifting length of the hook into a preset number of lifting intervals, For the endpoints of each lifting interval, traverse search is performed within the lifting system configuration space model based on the main boom luffing angle and the upper body slewing angle, upper body collision information is calculated, and upper body grid map data of the crane is generated.

[0071] Furthermore, the lower body data in this embodiment includes a travel parameter and a steering parameter. Specifically, the grouping processing module 403 further Based on the travel parameter and the steering parameter, perform a scanning traverse within the lifting system configuration space model to obtain lower body collision information. Generate lower body grid map data of the crane based on the lower body collision information.

[0072] Furthermore, specifically, the construction module 404 in this embodiment Use the A* algorithm to perform path planning for the upper body grid map data and the lower body grid map data respectively to obtain an upper body path planning model and a lower body path planning model. Combine the upper body path planning model and the lower body path planning model to construct a lifting path planning model.

[0073] The present invention further protects a lifting path planning device based on the same overall inventive concept. Hereinafter, the lifting path planning device provided by the present invention will be described, and the lifting path planning device described below and the above-described lifting path planning method can be referred to in correspondence with each other.

[0074] FIG. 5 is a schematic configuration diagram of a lifting path planning device provided by the present invention.

[0075] As shown in FIG. 5, the lifting path planning device provided by the present invention includes A determination module 501 used to determine the start point and end point of the lifting path. The coordinate of the starting point and the coordinate of the ending point are input into the weight-lifting route planning model obtained by the weight-lifting route planning model construction method described in any one of the above-described embodiments, and are used to output the weight-lifting planned route as the optimal weight-lifting route, including the planning module 502.

[0076] Furthermore, based on the above-described embodiment, this embodiment further includes a correction module, and the correction module For each of the upper body grid map data and the lower body grid map data, starting from the starting point, search for the upper body grid map data node and the lower body grid map data node. For each of the upper body grid map data nodes and the lower body grid map data nodes, determine the actual route cost and the predicted cost. Mark the actual route cost and the predicted cost in the open list, search for the node with the minimum total cost in the open list, start the search as a new starting point, and search until the ending point.

[0077] Furthermore, based on the above-described embodiment, this embodiment further includes a conversion module, and the conversion module Based on the weight-lifting system configuration space model, convert the weight-lifting planned route into the operation sequence of the crane. Generate a crane control command based on the operation sequence.

[0078] The present invention further protects a crane that executes the weight-lifting route planning method described in any one of the above-described embodiments based on the same overall inventive concept.

[0079] FIG. 6 is a structural schematic diagram of an electronic device provided by the present invention.

[0080] As shown in FIG. 6, this electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute the method for constructing a hoisting path planning model. This method includes steps of creating a crane model, constructing a hoisting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model, generating upper body grid map data of the crane for the hoisting system configuration space model and the upper body data, generating lower body grid map data of the crane for the hoisting system configuration space model and the lower body data, and using the A* algorithm to construct a hoisting path planning model by combining the upper body grid map data and the lower body grid map data.

[0081] Furthermore, if the logical instructions in the above-mentioned memory 630 can be realized as a software functional unit and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or some of the steps of the methods described in the embodiments of the present invention. On the other hand, the above storage medium includes various media capable of storing program codes, such as a USB memory, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0082] According to another aspect, the present invention further provides a computer program product, which is a computer program that can be stored in a non-transitory computer-readable storage medium. When executed by a processor, the computer can execute the weight-lifting path planning model construction method provided by each of the above methods. This method includes the steps of creating a crane model, constructing a weight-lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model, generating upper body grid map data of the crane for the weight-lifting system configuration space model and the upper body data, generating lower body grid map data of the crane for the weight-lifting system configuration space model and the lower body data, and constructing a weight-lifting path planning model by using the A* algorithm in combination with the upper body grid map data and the lower body grid map data.

[0083] According to another aspect, the present invention further provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, realizes the execution of the weight-lifting path planning model construction method provided by each of the above methods. This method includes the steps of creating a crane model, constructing a weight-lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model, generating upper body grid map data of the crane for the weight-lifting system configuration space model and the upper body data, generating lower body grid map data of the crane for the weight-lifting system configuration space model and the lower body data, and constructing a weight-lifting path planning model by using the A* algorithm in combination with the upper body grid map data and the lower body grid map data.

[0084] The device embodiments described above are merely exemplary. The units described as the separated components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be in one place or may be distributed among a plurality of network units. To achieve the objectives of the aspects of this embodiment, it is possible to select some or all of these modules according to actual needs. A person skilled in the art can understand and implement it without creative labor.

[0085] Through the description of the above embodiments, a person skilled in the art can clearly understand that each embodiment can be realized by adding a general-purpose hardware platform required for software (of course, it can also be realized by hardware). Based on such an understanding, in essence, or the part that contributes to the prior art, the above-described technical solution can be embodied in the form of a software product. The computer software product may be stored in a computer-readable storage medium (such as ROM / RAM, magnetic disk, optical disk, etc.), and may include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0086] Finally, the above embodiments do not limit the technical means of the present invention and are only used for explanation. Although the present invention has been described in detail with reference to the foregoing embodiments, a person skilled in the art can modify the technical means described in each of the foregoing embodiments or perform equivalent substitution on some of its technical features. It should be understood that these modifications or substitutions do not deviate from the essence of the corresponding technical means from the gist and scope of the technical means of each embodiment of the present invention.

Claims

1. A method for constructing a heavy lifting path planning model, comprising: creating a crane model; constructing a heavy lifting system configuration space model including upper body data and lower body data of the crane based on the current working scene and the crane model; generating upper body grid map data of the crane for the heavy lifting system configuration space model and the upper body data; generating lower body grid map data of the crane for the heavy lifting system configuration space model and the lower body data; constructing a heavy lifting path planning model by using the A-star algorithm and combining the upper body grid map data and the lower body grid map data. A method for constructing a heavy lifting path planning model, characterized by comprising the above steps.

2. The upper body data includes a main boom luffing angle, an upper body slewing angle, and a hook lifting length. For the heavy lifting system configuration space model and the upper body data, the step of generating the upper body grid map data of the crane includes: determining the hook lifting length; dividing the hook lifting length into a preset number of lifting intervals; for each endpoint of the lifting intervals, performing a traversal search within the heavy lifting system configuration space model based on the main boom luffing angle and the upper body slewing angle, calculating upper body collision information, and generating upper body grid map data of the crane. The method for constructing a heavy lifting path planning model according to Claim 1, characterized by comprising the above steps.

3. The lower body data includes a traveling parameter and a steering parameter. For the heavy lifting system configuration space model and the lower body data, the step of generating the lower body grid map data of the crane includes: performing a scanning traversal within the heavy lifting system configuration space model based on the traveling parameter and the steering parameter to obtain lower body collision information; generating lower body grid map data of the crane based on the lower body collision information. The method for constructing a heavy lifting path planning model according to Claim 1, characterized by comprising the above steps.

4. The step of constructing a weight-lifting path planning model by using the A-star algorithm and combining the upper vehicle body grid map data and the lower vehicle body grid map data is as follows: The step of performing path planning for the upper vehicle body grid map data and the lower vehicle body grid map data respectively by using the A-star algorithm to obtain an upper vehicle body path planning model and a lower vehicle body path planning model; The step of constructing a weight-lifting path planning model by combining the upper vehicle body path planning model and the lower vehicle body path planning model; The method for constructing a weight-lifting path planning model according to claim 1, characterized by including the above steps.

5. A weight-lifting path planning method, comprising: The step of determining the starting point and the ending point of the weight-lifting path; Inputting the coordinates of the starting point and the coordinates of the ending point into the weight-lifting path planning model obtained by the method for constructing a weight-lifting path planning model according to any one of claims 1 to 4, and outputting a weight-lifting planned path as the optimal weight-lifting path; The weight-lifting path planning method, characterized by including the above steps.

6. After the step of outputting the weight-lifting planned path, further comprising: For each of the upper vehicle body grid map data and the lower vehicle body grid map data, the step of searching for an upper vehicle body grid map data node and a lower vehicle body grid map data node from the starting point; The step of determining the actual path cost and the predicted cost for each of the upper vehicle body grid map data nodes and the lower vehicle body grid map data nodes; Marking the actual path cost and the predicted cost in the open list, searching for the node with the minimum total cost in the open list, starting the search with the new starting point, and searching until the ending point; The weight-lifting path planning method according to claim 5, characterized by including the above steps.

7. After the step of outputting the weight-lifting planned path, further comprising: Based on the weight-lifting system configuration space model, the step of converting the weight-lifting planned path into a crane operation sequence; The step of generating a crane control command based on the operation sequence; The weight-lifting path planning method according to claim 5, characterized by including the above steps.

8. A weight-lifting path planning model construction device, comprising: A simulation module used to create a crane model; A configuration space module used to construct a hoisting system configuration space model including upper body data and lower body data of a crane based on the current working scene and the crane model; A grouping processing module used to generate upper body grid map data of the crane for the hoisting system configuration space model and the upper body data, and to generate lower body grid map data of the crane for the hoisting system configuration space model and the lower body data; A construction module used to construct a hoisting route planning model by using the A-star algorithm and combining the upper body grid map data and the lower body grid map data; A hoisting route planning model construction device, characterized by comprising the above.

9. A hoisting route planning device, A determination module used to determine the starting point and the ending point of the hoisting route; A planning module used to input the starting point and the ending point into a hoisting route planning model obtained by the hoisting route planning model construction method according to any one of Claims 1 to 4 and output a hoisting planned route; A hoisting route planning device, characterized by comprising the above.

10. Executing the hoisting route planning method according to Claim 5 A crane, characterized by the above.

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