WO2024021924A1 - 吊装路径规划模型构建方法、吊装路径规划方法及起重机 - Google Patents

吊装路径规划模型构建方法、吊装路径规划方法及起重机 Download PDF

Info

Publication number
WO2024021924A1
WO2024021924A1 PCT/CN2023/100942 CN2023100942W WO2024021924A1 WO 2024021924 A1 WO2024021924 A1 WO 2024021924A1 CN 2023100942 W CN2023100942 W CN 2023100942W WO 2024021924 A1 WO2024021924 A1 WO 2024021924A1
Authority
WO
WIPO (PCT)
Prior art keywords
hoisting
data
crane
path planning
model
Prior art date
Application number
PCT/CN2023/100942
Other languages
English (en)
French (fr)
Inventor
谢军
俞晓斌
吴琼
Original Assignee
浙江三一装备有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 浙江三一装备有限公司 filed Critical 浙江三一装备有限公司
Priority to DE112023000145.6T priority Critical patent/DE112023000145T5/de
Priority to US18/409,461 priority patent/US20240143859A1/en
Publication of WO2024021924A1 publication Critical patent/WO2024021924A1/zh

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/18—Control systems or devices
    • B66C13/48—Automatic control of crane drives for producing a single or repeated working cycle; Programme control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction

Definitions

  • the invention relates to the technical field of path planning, and in particular to a hoisting path planning model construction method, a hoisting path planning method and a crane.
  • hoisting operations have become more difficult.
  • a hoisting job often requires one or more auxiliary personnel; simultaneous hoisting
  • the quality of the work is also extremely dependent on the skill of the driver.
  • hoisting path planning has gained certain practical value.
  • the invention provides a hoisting path planning model construction method, a hoisting path planning method and a crane to solve the problem of low hoisting path planning efficiency in the prior art and realize the hoisting path planning by dividing the hoisting path into boarding path planning and alighting path planning. way to reduce the amount of data during path search and improve path planning efficiency.
  • the invention provides a hoisting path planning model construction method, which includes:
  • the hoisting system configuration space model includes the crane's loading and unloading data
  • the A-star algorithm is used to construct a hoisting path planning model by combining the boarding grid data and the alighting grid data.
  • the loading data includes: main arm luffing angle, loading rotation angle and hook lifting length;
  • the method of generating the crane's loading grid map data based on the hoisting system configuration space model and the loading data includes:
  • a traversal search is performed within the hoisting system configuration space model based on the main arm luffing angle and the loading rotation angle, the loading collision information is calculated, and the loading of the crane is generated. Raster plot data.
  • the alighting data includes walking parameters and steering parameters
  • Generating the crane's disembarking grid map data based on the hoisting system configuration space model and the disembarking data includes:
  • the disembarking grid map data of the crane is generated.
  • the A-star algorithm is used to construct a hoisting path planning model in combination with the boarding grid data and the alighting grid data, including:
  • the boarding raster image data and the alighting raster image data are processed respectively.
  • the data is used for path planning, and the boarding path planning model and the alighting path planning model are obtained;
  • a hoisting path planning model is constructed.
  • the invention also provides a hoisting path planning method, which includes:
  • the hoisting path planning model is the hoisting path planning model construction method according to any of the above. owned.
  • the method further includes:
  • the method further includes:
  • crane control instructions are generated.
  • the invention also provides a hoisting path planning model construction device, which includes:
  • the configuration space module is used to construct a hoisting system configuration space model based on the current operation scenario and the crane model.
  • the hoisting system configuration space model includes the loading and unloading data of the crane;
  • a grouping processing module configured to generate the loading grid diagram data of the crane based on the hoisting system configuration space model and the loading data; and generate the crane loading grid data based on the hoisting system configuration space model and the loading data. Get off the bus raster chart data;
  • a building module is used to construct a hoisting path planning model by using the A-star algorithm and combining the boarding grid data and the alighting grid data.
  • the invention also provides a hoisting path planning device, which includes:
  • Determination module used to determine the starting point and end point of the hoisting path
  • a planning module is used to input the starting point and the end point into a hoisting path planning model and output a hoisting planned path.
  • the hoisting path planning model is obtained according to any one of the above-mentioned hoisting path planning model construction methods.
  • the present invention also provides a crane, which is used to perform the hoisting path planning method as described in any one of the above.
  • the present invention also provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, it implements any of the above hoisting path planning. Model building methods.
  • the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the method for constructing a hoisting path planning model as described above is implemented.
  • the present invention also provides a computer program product, which includes a computer program.
  • the computer program When the computer program is executed by a processor, the computer program implements any one of the hoisting path planning model construction methods mentioned above.
  • the invention provides a hoisting path planning model construction method, a hoisting path planning method and a crane.
  • the hoisting path planning model construction method includes: establishing a crane model; based on the current operation scenario and the crane model, constructing a hoisting system configuration space model, and the hoisting system
  • the configuration space model includes the crane's loading and unloading data; for the hoisting system configuration space model and the loading data, the crane's loading raster map data is generated; for the hoisting system's configuration space model and the loading and unloading data, it generates Crane dismount raster data; using A Star algorithm is used to construct a hoisting path planning model by combining the boarding grid data and the alighting grid data.
  • the hoisting path planning model is based on the boarding grid data and the alighting grid data
  • the hoisting path planning model can be constructed by The entire path is divided into two groups: boarding and alighting, which effectively reduces the amount of data during path search and improves path planning efficiency.
  • Figure 1 is a schematic flow chart of the hoisting path planning model construction method provided by the present invention.
  • Figure 2 is a schematic structural diagram of a grid diagram provided by the present invention.
  • FIG. 3 is a schematic flowchart of the hoisting path planning method provided by the present invention.
  • Figure 4 is a schematic structural diagram of a hoisting path planning model construction device provided by the present invention.
  • Figure 5 is a schematic structural diagram of the hoisting path planning device provided by the present invention.
  • Figure 6 is a schematic structural diagram of the electronic device provided by the present invention.
  • Figure 1 is a schematic flowchart of the hoisting path planning model construction method provided by the present invention.
  • an embodiment of the present invention provides a hoisting path planning model construction method.
  • the execution subject may be a remote control system, which specifically includes the following steps:
  • Lifting refers to the general term for the installation and positioning of equipment by cranes. During the inspection or maintenance process, various cranes are used to lift equipment, workpieces, appliances, materials, etc., so that their positions change.
  • a crane model that is, simulate the crane, and express the crane in digital form, which can be understood as placing the crane in a coordinate system.
  • Each component structure of the crane corresponds to different coordinates.
  • the crane models established are also different.
  • the hoisting system configuration space model includes the crane's loading and unloading data.
  • the current operating scene refers to the area where the crane will operate. For example, if the crane is at a construction site, the construction site can be used as the current operating scene.
  • the crane model is then placed in the current operation scenario to construct a hoisting system configuration space model.
  • the hoisting system configuration space model can be a multi-dimensional system model.
  • C represents the crane's dismounting data
  • U represents the crane's boarding data
  • p represents the Cartesian coordinates of the crane
  • d represents the direction vector of the crane
  • ⁇ represents the main arm luffing angle
  • ⁇ represents the boarding rotation angle
  • L represents Hook lifting length
  • the length of the boom can be known in advance, such as a truss boom. Based on the known boom sections, the boom length can be calculated through simple addition and subtraction. Telescopic boom The length of the frame can be measured in advance through a length sensor installed on the arm.
  • the crane configuration coordinates ( ⁇ , ⁇ , L) to Cartesian coordinates (x, y, z) can be realized. Convert each other.
  • the hoisting system configuration space model shows various states of the crane, including the walking parameters and steering parameters in the disembarking data, the main arm luffing angle, the upper slewing angle and the hook in the getting-on data. Lifting length, etc., enable the hoisting system configuration space model to more comprehensively reflect the status information of the crane.
  • the loading and unloading data of the crane need to be processed separately.
  • its movements are divided into two combinations.
  • One group is the upper movement including the main arm luffing angle, the upper vehicle rotation angle and the hook lifting length, and the other is the lower movement including the walking parameters and steering parameters.
  • the data of the hoisting system configuration space model is divided into two calculations, which reduces the difficulty of a single calculation and also reduces the coupling degree.
  • the loading grid diagram data refers to the grid diagram composed of the main arm luffing angle, the loading rotation angle and the hook lifting length. That is, the main arm luffing angle, the upper vehicle rotation angle and the hook lifting length are three degrees of freedom. Each has several data of different sizes, and then they are arranged and combined separately to form the entire upper vehicle grid. lattice data.
  • the method of generating the alighting raster map data is the same as the method of generating the alighting raster map data.
  • the alighting raster map data represents data in two degrees of freedom directions.
  • the hoisting path planning model can be constructed.
  • the boarding grid data includes several nodes, and the getting off grid data also includes several nodes, and different nodes will then form several lines, that is, several hoisting paths.
  • the A-star algorithm is used to optimize the boarding grid data and the alighting grid data, and the A-star algorithm is combined with the boarding grid data and the alighting grid data to successfully construct Lifting path planning model.
  • the working principle of the hoisting path planning model is to obtain the boarding grid data and the getting off the grid data in the current operation scenario, and then use the A-star algorithm to calculate the boarding grid data and the getting off the grid data. Perform traversal search to obtain the target path.
  • the use of the A-star algorithm to plan paths has the advantages of global optimality and good continuity, and can effectively streamline the amount of configuration data and reduce computational complexity.
  • This embodiment provides a hoisting path planning model construction method, which includes: establishing a crane model; building a hoisting system configuration space model based on the current operation scenario and the crane model.
  • the hoisting system configuration space model includes the crane's loading data and unloading data. Based on the hoisting system configuration space model and the loading data, the crane's loading grid data is generated; based on the hoisting system configuration space model and the loading data, the crane's loading grid data is generated; A star is used to generate the crane's loading grid data.
  • Algorithm combines the boarding grid data and the getting off grid data to build a hoisting path planning model.
  • the hoisting path planning model is based on the boarding grid data and the getting off grid data, the entire hoisting path planning model can be constructed by The route is divided into two groups: boarding and alighting, which effectively reduces the amount of data during path search and improves path planning efficiency.
  • the loading data in this embodiment include: main arm luffing angle, loading rotation angle and hook lifting length; correspondingly, for the hoisting system configuration space model and loading Data is generated to generate the loading grid diagram data of the crane, including: determining the lifting length of the hook; dividing the lifting length of the hook into a preset number of lifting intervals; for the endpoint of each lifting interval, based on the main arm luffing angle and loading
  • the rotation angle is traversed and searched within the configuration space model of the hoisting system, the loading collision information is calculated, and the loading grid map data of the crane is generated.
  • main arm luffing angle In the crane loading data, there are three actions: main arm luffing angle, loading rotation angle and hook lifting length.
  • the relationship between the three actions is: when the crane is working, the hook lifting length action is often Actions are performed at the beginning and end of the hoisting process, while the main arm luffing angle and the upper vehicle rotation angle are actions in the middle process. Therefore, in order to further improve the speed of path search, you can choose to use the hook lifting length L as a control parameter to obtain the boarding grid map data.
  • FIG. 2 is a schematic structural diagram of a grid diagram provided by the present invention. As shown in Figure 2, it is a schematic diagram of a grid chart. The radial direction is related to the main arm luffing angle, and the rotation angle is the upper vehicle rotation angle.
  • each grid of each set of grid chart data contains collision information, Edge information, load information, etc., and each set hook lifting length L corresponds to a set of such data, and there are a total of m sets of corresponding raster map data. All m groups of raster map data constitute the entire crane loading raster map data.
  • n effective paths referring to n types of paths from the starting point to the end point
  • the n effective paths are compared and the optimal path is selected as the current result path.
  • the alighting data in this embodiment includes walking parameters and steering parameters; correspondingly, based on the hoisting system configuration space model and the alighting data, the alighting grid data of the crane is generated , including: based on the walking parameters and steering parameters, scanning and traversing the hoisting system configuration space model to obtain the disembarking collision information; based on the disembarking collision information, generating the crane's disembarking raster map data.
  • the above embodiment has carried out the method of generating the loading grid map data of the crane. Be specific. Therefore, when the crane is moving, it is necessary to first obtain the collision results of the onboard data, and then calculate the collision results of the offboard data. The final collision result is obtained by combining the onboard collision results and the offboard collision results. Its practical significance means that the crane ensures that the entire hoisting system does not collide when the crane is walking, turning when getting off the truck, luffing, turning, and lifting the hook when getting on the truck.
  • the process of generating the alighting grid map data is to first scan and traverse the hoisting system configuration space model based on the walking parameters and steering parameters to obtain the alighting collision information, and then generate the alighting collision data based on the alighting collision data.
  • the disembarking grid chart data refers to the one-to-one correspondence between the crane's traveling parameters and steering parameters.
  • the disembarking grid chart data can reflect the corresponding steering parameters under all walking parameter conditions. In the same way, it can also reflect all The corresponding walking parameters under the steering parameter conditions.
  • the A-star algorithm is used to combine the boarding grid data and the alighting grid data to construct a hoisting path planning model, which may include: using the A-star algorithm, respectively.
  • the A-star algorithm is also called the A* search algorithm.
  • the characteristic of the A-star algorithm is that it introduces global information when checking each possible node in the shortest path, estimates the distance from the current node to the end point, and uses it as a measure to evaluate the possibility of the node being on the shortest route. Therefore, in this embodiment, the A-star algorithm can be used to better complete path planning.
  • Path planning is performed on the boarding raster map data and the alighting raster map data respectively.
  • group processing the difficulty of single calculation can be reduced and the degree of coupling can be reduced.
  • establishing the crane model in this embodiment may include: obtaining the structural data of the crane.
  • the structural data includes size information, operation dynamic parameters and load parameters; based on the size information, motion parameters and load parameters, the crane model is established.
  • the way to obtain the structural data of the crane can be to directly read the product manual of the crane, or to manually enter key data, or to measure different data through various sensors, as long as the structural data of the crane can be accurately obtained. That’s it.
  • After accurately obtaining the dimensional information, motion parameters and load parameters of the crane it is converted into a spatial model, that is, the crane structure is simulated through lines.
  • the accuracy of the simulated crane can also be ensured, thereby improving the accuracy of the hoisting path planning model.
  • the present invention also protects a hoisting path planning method.
  • FIG. 3 is a schematic flowchart of the hoisting path planning method provided by the present invention.
  • the hoisting path planning method provided by this embodiment can be executed by a vehicle-mounted controller or a remote control terminal, and mainly includes the following steps:
  • the starting point and end point of the crane's work that is, the starting point and end point of the hoisting path.
  • the lifting starting point is determined or can be obtained directly from the positioning system. Therefore, in the specific implementation process, there is no need to enter the starting point data, and the end point data can be directly input, that is, only the end point of the hoisting path is determined.
  • the way to determine the hoisting end point can be to directly read the end point data input by the user, or to automatically locate the end point after the user specifies the location, as long as the starting point and end point of the hoisting path can be effectively obtained.
  • the hoisting path planning model is obtained according to the hoisting path planning model construction method of any of the above embodiments.
  • the starting point data and end point data can be input into the hoisting path planning model.
  • the hoisting path planning model will perform path planning calculations based on the starting point and end point and output the hoisting planned path.
  • the process of planning the hoisting path between the starting point and the end point by the hoisting path planning model can be understood as the hoisting path planning model first plans the boarding path, then plans the getting off path, and then combines the boarding path and the getting off path. Combined, the hoisting planning path is finally obtained.
  • Hoisting path planning refers to selecting the most appropriate implementation path between the starting point and the end point.
  • the hoisting system configuration space model constructed within the current operating scenario can be understood as a crisscross grid, which can be quickly completed through the A-star algorithm.
  • the final hoisting path planning is the path with the shortest hoisting time. Among them, as the amount of data increases, the computing efficiency of the A-star algorithm will decrease.
  • the hoisting planning path after outputting the hoisting planning path, it may also include: searching for the boarding grid map data and the getting off grid map data from the starting point respectively.
  • Car raster graph data node for each boarding raster graph data node and alighting raster graph data node, determine the traveled cost and predicted cost; mark the traveled cost and predicted cost in the open list, in the open list Search for the node with the smallest total cost and start the search as a new starting point until the search reaches the end point.
  • the correction method can be to separately calibrate the boarding path and the alighting path, respectively search the boarding grid data and the alighting grid data from the starting point to determine the location and location of each boarding grid data node and the surrounding area. Get off the bus raster chart data node, and then determine the trip cost and predicted cost for each boarding raster chart data node and get off the bus raster chart data node, and put the trip cost and predicted cost in the open list, open The list refers to the nodes that have been searched.
  • the node is used as a new starting point to start searching, and the search operation is repeated until the end point is searched, and then the optimal path is found from the open list as the final hoisting planning path, thereby completing the optimization and calibration of the hoisting planning path.
  • this embodiment after outputting the hoisting planning path, it may also include: converting the hoisting planning path into an action sequence of the crane based on the hoisting system configuration space model; and generating the crane based on the action sequence. Control instruction.
  • the crane control instructions are generated to control the crane to move according to the determined hoisting planning path.
  • the crane's control instructions Each part of the crane is controlled to move according to the hoisting planned path, and finally the control from the starting point to the end point of the hoisting is completed, and the crane completes the hoisting operation. Due to the rationality of the hoisting path planning, the efficiency of the hoisting operation can be effectively improved.
  • the hierarchical processing of getting on and off can achieve dimensionality reduction in the configuration space; reducing the amount of data and standardizing the configuration coordinate parameters of the crane improves the performance of the path planning algorithm.
  • the present invention also protects a hoisting path planning model construction device.
  • the hoisting path planning model construction device provided by the present invention is described below.
  • the hoisting path planning model construction device described below is the same as the hoisting path described above. Planning model construction methods can correspond to each other.
  • Figure 4 is a schematic structural diagram of a hoisting path planning model construction device provided by the present invention.
  • a hoisting path planning model construction device provided by an embodiment of the present invention includes:
  • Simulation module 401 used to establish a crane model
  • the configuration space module 402 is used to construct a hoisting system configuration space model based on the current operation scenario and the crane model.
  • the hoisting system configuration space model includes the loading and unloading data of the crane;
  • the grouping processing module 403 is used to generate the loading raster diagram data of the crane based on the hoisting system configuration space model and the loading data; generate Crane dismounting raster map data;
  • the construction module 404 is used to construct a hoisting path planning model by using the A-star algorithm and combining the boarding grid data and the alighting grid data.
  • This embodiment provides a hoisting path planning model construction device, which includes: establishing a crane model; building a hoisting system configuration space model based on the current operation scenario and the crane model.
  • the hoisting system configuration space model includes the loading data and unloading data of the crane. Based on the hoisting system configuration space model and the loading data, the crane's loading grid data is generated; based on the hoisting system configuration space model and the loading data, the crane's loading grid data is generated; A star is used to generate the crane's loading grid data. Algorithm, combines the boarding grid data and the getting off grid data to build a hoisting path planning model.
  • the hoisting path planning model is based on the boarding grid data and the getting off grid data, the entire hoisting path planning model can be constructed by The route is divided into two groups: boarding and alighting, which effectively reduces the amount of data during path search and improves path planning efficiency.
  • the loading data in this embodiment include: main arm luffing angle, loading rotation angle and hook lifting length;
  • Grouping processing module 403 is specifically used for:
  • a traversal search is performed within the hoisting system configuration space model based on the main arm luffing angle and the loading rotation angle, the loading collision information is calculated, and the loading of the crane is generated. Raster plot data.
  • the alighting data in this embodiment includes walking parameters and steering parameters
  • Grouping processing module 403 is also specifically used for:
  • the disembarking grid map data of the crane is generated.
  • building module 404 in this embodiment is specifically used for:
  • a hoisting path planning model is constructed.
  • the present invention also protects a hoisting path planning device.
  • the hoisting path planning device provided by the present invention is described below.
  • the hoisting path planning device described below and the hoisting path planning method described above can correspond to each other. .
  • Figure 5 is a schematic structural diagram of the hoisting path planning device provided by the present invention.
  • a hoisting path planning device provided by the present invention includes:
  • Determination module 501 used to determine the starting point and end point of the hoisting path
  • the planning module 502 is used to input the coordinates of the starting point and the coordinates of the end point into a hoisting path planning model, and output the hoisting planned path as an optimal hoisting path.
  • the hoisting path planning model is a hoisting path according to any of the above embodiments. Obtained by path planning model construction method.
  • this embodiment also includes: a correction module, used for:
  • this embodiment also includes: a conversion module, used for:
  • the hoisting planning path is converted into the crane's action sequences
  • crane control instructions are generated.
  • the present invention also protects a crane, which is used to perform the hoisting path planning method as in any of the above embodiments.
  • Figure 6 is a schematic structural diagram of the electronic device provided by the present invention.
  • the electronic device may include: a processor (processor) 610, a communication interface (Communications Interface) 620, a memory (memory) 630, and a communication bus 640.
  • the processor 610, the communication interface 620, and the memory 630 pass through The communication bus 640 completes mutual communication.
  • the processor 610 can call the logic instructions in the memory 630 to execute the hoisting path planning model construction method.
  • the method includes establishing a crane model; building a hoisting system configuration space model based on the current operation scenario and the crane model.
  • the hoisting system The configuration space model includes the loading and unloading data of the crane; for the hoisting system configuration space model and the loading data, the loading grid map data of the crane is generated; for the hoisting system configuration space model and the alighting grid data to generate the alighting grid map data of the crane; using the A-star algorithm and combining the getting on raster map data and the alighting grid map data, a hoisting path planning model is constructed.
  • the above-mentioned logical instructions in the memory 630 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .
  • the present invention also provides a computer program product, the computer program
  • the product includes a computer program, which can be stored on a non-transitory computer-readable storage medium.
  • the computer program can execute the hoisting path planning model construction method provided by the above methods.
  • the method includes establishing Crane model; Based on the current operation scenario and the crane model, a hoisting system configuration space model is constructed.
  • the hoisting system configuration space model includes the crane's loading and unloading data; for the hoisting system configuration space model and The loading data is used to generate the loading grid data of the crane; based on the hoisting system configuration space model and the getting off data, the loading grid data of the crane is generated; the A-star algorithm is used, combined with the The hoisting path planning model is constructed using the boarding grid map data and the alighting grid map data.
  • the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is implemented when executed by a processor to execute the hoisting path planning model construction method provided by each of the above methods.
  • the method includes establishing a crane model; based on the current operation scenario and the crane model, constructing a hoisting system configuration space model, where the hoisting system configuration space model includes the loading and unloading data of the crane; targeting the hoisting system configuration
  • the space model and the loading data are used to generate the loading grid data of the crane; based on the hoisting system configuration space model and the loading data, the loading grid data of the crane are generated; using the A-star algorithm, Combining the boarding grid map data and the getting off the bus grid map data, a hoisting path planning model is constructed.
  • the device embodiments described above are only illustrative.
  • the units described as separate 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 located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.
  • each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions are essentially The technical contributions can be reflected in the form of software products.
  • the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic disks, optical disks, etc., and include a number of instructions to make a computer
  • the device (which may be a personal computer, a server, a network device, etc.) executes the methods described in various embodiments or some parts of the embodiments.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Geometry (AREA)
  • Automation & Control Theory (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Control And Safety Of Cranes (AREA)

Abstract

本发明提供一种吊装路径规划模型构建方法、吊装路径规划方法及起重机，吊装路径规划模型构建方法包括：建立起重机模型；基于当前作业场景和起重机模型，构建吊装系统位形空间模型，吊装系统位形空间模型包括起重机的上车数据和下车数据；针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据；针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据；利用A星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型，由于构建的吊装路径规划模型是基于上车栅格图数据和下车栅格图数据，使得通过将整个的路径划分为上车和下车两组，有效地降低了路径搜索时的数据量，提高了路径规划效率。

Description

吊装路径规划模型构建方法、吊装路径规划方法及起重机 技术领域
本发明涉及路径规划技术领域，尤其涉及一种吊装路径规划模型构建方法、吊装路径规划方法及起重机。
背景技术
随着吊装施工场地的复杂化，以及对吊装操作的安全性和准确性的要求，使得吊装作业拥有较高的难度，一次吊装工作除起重机驾驶员外，往往还需要一至多名辅助人员；同时吊装作业的质量也极度依赖驾驶员的水平。近年来，随着数字孪生、智能工地等技术的发展和应用，使得吊装路径规划有了一定的落地价值。
目前，大多数的吊装路径规划所依赖的吊装系统数据量庞大，导致在路径搜索时的效率相对较低。
发明内容
本发明提供一种吊装路径规划模型构建方法、吊装路径规划方法及起重机，用以解决现有技术中吊装路径规划效率低的缺陷，实现通过将吊装路径分为上车路径规划和下车路径规划的方式，减小路径搜索时的数据量，提高路径规划效率。
本发明提供一种吊装路径规划模型构建方法，包括：
建立起重机模型；
基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；
针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；
利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
根据本发明提供的一种吊装路径规划模型构建方法，所述上车数据包括：主臂变幅角度、上车回转角度和吊钩升降长度；
所述针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据，包括：
确定所述吊钩升降长度；
划分所述吊钩升降长度为预设数量的升降区间；
针对每个所述升降区间的端点，基于所述主臂变幅角度和所述上车回转角度在所述吊装系统位形空间模型内进行遍历搜索，计算上车碰撞信息，生成起重机的上车栅格图数据。
根据本发明提供的一种吊装路径规划模型构建方法，所述下车数据包括行走参数和转向参数；
所述针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据，包括：
基于所述行走参数和所述转向参数，在所述吊装系统位形空间模型内进行扫描遍历，得到下车碰撞信息；
根据所述下车碰撞信息，生成起重机的下车栅格图数据。
根据本发明提供的一种吊装路径规划模型构建方法，所述利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型，包括：
利用A星算法，分别对所述上车栅格图数据和所述下车栅格图 数据进行路径规划，得到上车路径规划模型和下车路径规划模型；
结合所述上车路径规划模型和所述下车路径规划模型，构建吊装路径规划模型。
本发明还提供一种吊装路径规划方法，包括：
确定吊装路径的起点和终点；
输入所述起点的坐标和所述终点的坐标至吊装路径规划模型，输出吊装规划路径为最优的吊装路径，所述吊装路径规划模型为根据上述任一项所述的吊装路径规划模型构建方法得到的。
根据本发明提供的一种吊装路径规划方法，所述输出吊装规划路径之后，还包括：
分别针对上车栅格图数据和下车栅格图数据，从所述起点开始搜索上车栅格图数据节点和下车栅格图数据节点；
针对每个所述上车栅格图数据节点和所述下车栅格图数据节点，确定已行代价和预测代价；
标记所述已行代价和所述预测代价于开启列表中，在所述开启列表中搜索总代价最小的节点，作为新的起点开始搜索，直至搜索至所述终点。
根据本发明提供的一种吊装路径规划方法，所述输出吊装规划路径之后，还包括：
基于吊装系统位形空间模型，转化所述吊装规划路径为起重机的动作序列；
基于所述动作序列，生成起重机控制指令。
本发明还提供一种吊装路径规划模型构建装置，包括：
模拟模块，用于建立起重机模型；
位形空间模块，用于基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；
分组处理模块，用于针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；
构建模块，用于利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
本发明还提供一种吊装路径规划装置，包括：
确定模块，用于确定吊装路径的起点和终点；
规划模块，用于输入所述起点和所述终点至吊装路径规划模型，输出吊装规划路径，所述吊装路径规划模型为根据上述任一项所述的吊装路径规划模型构建方法得到的。
本发明还提供一种起重机，所述起重机用于执行如上述任一项所述的吊装路径规划方法。
本发明还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述吊装路径规划模型构建方法。
本发明还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上述任一种所述吊装路径规划模型构建方法。
本发明还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述任一种所述吊装路径规划模型构建方法。
本发明提供的一种吊装路径规划模型构建方法、吊装路径规划方法及起重机，吊装路径规划模型构建方法包括：建立起重机模型；基于当前作业场景和起重机模型，构建吊装系统位形空间模型，吊装系统位形空间模型包括起重机的上车数据和下车数据；针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据；针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据；利用A 星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型，由于构建的吊装路径规划模型是基于上车栅格图数据和下车栅格图数据，使得通过将整个的路径划分为上车和下车两组，有效地降低了路径搜索时的数据量，提高了路径规划效率。
附图说明
为了更清楚地说明本发明或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单的介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明提供的吊装路径规划模型构建方法的流程示意图；
图2是本发明提供的栅格图的结构示意图；
图3是本发明提供的吊装路径规划方法的流程示意图。
图4是本发明提供的吊装路径规划模型构建装置的结构示意图；
图5是本发明提供的吊装路径规划装置的结构示意图；
图6是本发明提供的电子设备的结构示意图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚，下面将结合本发明中的附图，对本发明中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
下面结合图1-图6描述本发明的一种吊装路径规划模型构建方法、吊装路径规划方法及起重机。
图1是本发明提供的吊装路径规划模型构建方法的流程示意图。
如图1所示，本发明实施例提供的一种吊装路径规划模型构建方法，执行主体可以是远程控制系统，具体包括以下步骤：
101、建立起重机模型。
吊装是指起重机对设备的安装、就位的统称，在检修或维修过程中利用各种起重机将设备、工件、器具、材料等吊起，使其发生位置变化。
具体的，首先要建立起重机模型，也就是模拟起重机，将起重机以数字形式表达出来，可以理解为将起重机放置于坐标系内，起重机的各个组件结构都对应着不同的坐标。在数据库内仿真起重机，从而建立起重机模型。针对不同规格的起重机，由于其自身参数不同，所以建立的起重机模型也有所不同。
102、基于当前作业场景和起重机模型，构建吊装系统位形空间模型，吊装系统位形空间模型包括起重机的上车数据和下车数据。
确定起重机的当前作业场景，当前作业场景指的是起重机要进行作业的区域，例如，起重机在建筑工地，便可以将建筑工地作为当前作业场景。再将起重机模型放置于当前作业场景之中，构建吊装系统位形空间模型，其中，吊装系统位形空间模型可以是多维系统模型。吊装系统位形空间模型的表达式可以如(1)所示：
T＝(C(p，d)，U(α，β，L))   (1)
其中，C表示起重机的下车数据，U表示起重机的上车数据，p表示起重机的笛卡尔坐标，d表示起重机的方向向量，α表示主臂变幅角度，β表示上车回转角度，L表示吊钩升降长度，此处忽略的臂架长度数据，臂架的长度预先可知，如桁架臂架，基于已知的各段臂节，通过简单的加减可计算出臂架长度，伸缩式臂架可通过安装在臂架上的长度传感器预先测出。
当起重机状态确定时，也就是起重机位置保持不变没有移动时，可以实现起重机位形坐标(α，β，L)到笛卡尔坐标(x，y，z)的 相互转换。
整体来说，即吊装系统位形空间模型中展示着起重机的多种状态，包括下车数据中的行走参数、转向参数，上车数据中的主臂变幅角度、上车回转角度和吊钩升降长度等，使得吊装系统位形空间模型能够更加全面的反映出起重机的状态信息。
103、针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据；针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据。
具体的，在构建得到吊装系统位形空间模型之后，需要分别针对起重机的上车数据和下车数据进行单独的处理。根据起重机的工作特点，将其动作分成两个组合，一组为包含主臂变幅角度、上车回转角度和吊钩升降长度的上车动作，另一组为包含行走参数和转向参数的下车动作，由此将吊装系统位形空间模型的数据分为两次计算，减小了单次计算难度，也降低了耦合度。
当起重机不移动时，也就是在下车数据中的行走参数和转向参数确定的前提下，每一个U坐标代表起重机的一个位形状态，因此，便需要建立上车数据的所有上车栅格图数据，上车栅格图数据指的是主臂变幅角度、上车回转角度和吊钩升降长度所构成的栅格图。即主臂变幅角度、上车回转角度和吊钩升降长度这三个自由度，每种都有若干种大小不一的数据，然后分别将其进行排列组合，便可以构成整个的上车栅格图数据。
当起重机发生移动时，即下车数据中的行走参数和/或转向参数发生变化时，便需要计算出起重机的下车栅格图数据。其中，生成下车栅格图数据的方式与上车栅格图数据的生成方式相同，下车栅格图数据表示的为两个自由度方向的数据。
104、利用A星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型。
具体的，在得到上车栅格图数据和下车栅格图数据之后，便可以构建吊装路径规划模型。上车栅格图数据包括有若干个节点，下车栅格图数据中也包括有若干个节点，而不同的节点之后便会构成若干条线路，即若干条吊装路径。
利用A星算法，在上车栅格图数据和下车栅格图数据中进行寻优，将A星算法与上车栅格图数据和下车栅格图数据进行结合，便成功地构建出吊装路径规划模型。吊装路径规划模型的工作原理便是，在当前作业场景内得到上车栅格图数据和下车栅格图数据，然后利用A星算法，在上车栅格图数据和下车栅格图数据中进行遍历搜寻，从而得到目标路径。而采用A星算法规划路径具有全局最优性和连续性好的优点，能够有效精简位形数据量，降低计算复杂度。
本实施例提供的一种吊装路径规划模型构建方法，包括：建立起重机模型；基于当前作业场景和起重机模型，构建吊装系统位形空间模型，吊装系统位形空间模型包括起重机的上车数据和下车数据；针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据；针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据；利用A星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型，由于构建的吊装路径规划模型是基于上车栅格图数据和下车栅格图数据，使得通过将整个的路径划分为上车和下车两组，有效地降低了路径搜索时的数据量，提高了路径规划效率。
进一步的，在上述实施例的基础上，本实施例中的上车数据包括：主臂变幅角度、上车回转角度和吊钩升降长度；对应的，针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据，包括：确定吊钩升降长度；划分吊钩升降长度为预设数量的升降区间；针对每个升降区间的端点，基于主臂变幅角度和上车回转角度在吊装系统位形空间模型内进行遍历搜索，计算上车碰撞信息，生成起重机的上车栅格图数据。
具体的，起重机的上车数据中主臂变幅角度、上车回转角度和吊钩升降长度三个动作，其三个动作之间的关系为：在起重机工作时，吊钩升降长度动作往往是在吊装过程的起始和结尾进行的动作，而主臂变幅角度、上车回转角度则是中间过程的动作。因此，为了进一步提高路径搜索的速度，可以选择将吊钩升降长度L作为控制参数进行上车栅格图数据的获取。
划分吊钩升降长度为预设数量的升降区间，然后获取每个升降区间的端点，即L＝{L0，L1，L2，L3，L4……Lm}，对应的区间则是【L0，L1】、【L1，L2】、【L2，L3】……【Lm-1，Lm】。然后按主臂变幅角度α和上车回转角度β进行遍历搜索，(α，β)＝{(α0，β0)，(α0，β1)，(α0，β2)……(α1，β0)，(α1，β1)，(α1，β2)……(αn，βq)}，计算上车碰撞信息，针对每一个L端点数据均生成一组对应的栅格图数据。图2是本发明提供的栅格图的结构示意图。如图2所示，为栅格图示意图，其中，径向方向为主臂变幅角度相关，旋转角度为上车回转角度，因此每组栅格图数据的每个栅格中包含碰撞信息、边缘信息、载荷信息等，而每一个设定的吊钩升降长度L对应一组这样的数据，一共有m组对应的栅格图数据。所有的m组栅格图数据构成了整个的起重机上车栅格图数据。通过对m组栅格图数据进行路径规划，可得到n条有效路径(指从起点到终点具有n种路径)，比较n条有效路径，选出最优的路径作为当前的结果路径。
进一步的，在上述实施例的基础上，本实施例中的下车数据包括行走参数和转向参数；对应的，针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据，包括：基于行走参数和转向参数，在吊装系统位形空间模型内进行扫描遍历，得到下车碰撞信息；根据下车碰撞信息，生成起重机的下车栅格图数据。
具体的，上述实施例对生成起重机的上车栅格图数据方式进行了 具体说明。因此，在起重机移动时，需要先取得上车数据的碰撞结果，然后再去计算下车数据的碰撞结果，结合上车碰撞结果和下车碰撞结果，得到最终的碰撞结果。其实际意义表示起重机在下车的行走、转向和上车的变幅、回转、吊钩升降时都确保整个吊装系统不发生碰撞。
其中，生成下车栅格图数据的过程则是，首先基于行走参数和转向参数，在吊装系统位形空间模型内进行扫描遍历，得到下车碰撞信息，然后再根据下车碰撞数据，生成下车栅格图数据。下车栅格图数据指的便是起重机行走参数与转向参数的一一对应关系，下车栅格图数据则能够反映出所有的行走参数条件下对应的转向参数，同理也能够反映出所有的转向参数条件下对应的行走参数。
进一步的在上述实施例的基础上，本实施例中的利用A星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型，可以包括：利用A星算法，分别对上车栅格图数据和下车栅格图数据进行路径规划，得到上车路径规划模型和下车路径规划模型；结合上车路径规划模型和下车路径规划模型，构建吊装路径规划模型。
具体的，A星算法又称为A*搜寻算法。A星算法的特点是在检查最短路径中每个可能的节点时引入了全局信息，对当前节点距终点的距离做出估计，并作为评价该节点处于最短路线上的可能性的量度。因此，在本实施例中采用A星算法可以更好地完成路径的规划。
为了在路径规划的过程中，尽量的减少数据处理量，增加数据处理的速度。分别对上车栅格图数据和下车栅格图数据进行路径规划，通过分组处理能够降低单次计算难度，降低耦合度。通过将吊装路径规划模型分割为上车路径规划模型和下车路径规划模型，也能够使得在下车没有移动时，更加快速的完成上车路径规划，并且通过A星算法进行路径规划，能够做到全局性最优。
进一步的，在上述实施例的基础上，本实施例中的建立起重机模型，可以包括：获取起重机的结构数据，结构数据包括尺寸信息、运 动参数和载荷参数；基于尺寸信息、运动参数和载荷参数，建立起重机模型。
具体的，获取起重机的结构数据的方式可以是直接读取起重机的产品说明书，也可以是人为输入关键数据，也可以是通过各种传感器测量不同的数据，只要能够准确地获取到起重机的结构数据即可。在准确的获取到起重机的尺寸信息、运动参数和载荷参数之后，将其转化为空间模型，也就是通过线条的方式模拟起重机结构进行仿真。通过准确地获取尺寸信息、运动参数和载荷参数，也能够保证模拟起重机的准确度，从而提高吊装路径规划模型的精准度。
基于同一总的发明构思，本发明还保护一种吊装路径规划方法。
图3是本发明提供的吊装路径规划方法的流程示意图。
如图3所示，本实施例提供的吊装路径规划方法，执行主体可以是车载控制器，也可以是远程控制终端等，主要包括以下步骤：
301、确定吊装路径的起点和终点。
具体的，在进行路径规划时，首先要确定出起重机工作的起点和终点，即吊装路径的起点和终点。通常，吊装起点是确定的，或者可以直接根据定位系统获取到。因此，在具体实现过程中，无需输入起点数据，可以直接输入终点数据，也就是只需确定出吊装路径的终点即可。而确定吊装终点的方式，可以是直接读取用户输入的终点数据，也可以是用户指定位置后，自动定位出终点的位置，只要能够有效地获取到吊装路径的起点和终点即可。
302、输入起点的坐标和终点的坐标至吊装路径规划模型，输出吊装规划路径为最优的吊装路径，吊装路径规划模型为根据上述任一实施例的吊装路径规划模型构建方法得到的。
具体的，在获取到吊装路径的起点和终点之后，便可以将起点数据和终点数据输入至吊装路径规划模型，吊装路径规划模型便会根据起点和终点进行路径规划计算，输出吊装规划路径。
其中，吊装路径规划模型规划起点到终点之间的吊装路径的过程，可以理解为，吊装路径规划模型首先规划出上车路径，然后再规划出下车路径，再将上车路径与下车路径结合，最终得到吊装规划路径。通过分组规划上车路径和下车路径，能够有效地降低数据处理量，提高数据处理速度。吊装路径规划指的是在起点和终点之间选择最为合适的实现路径，在当前作业场景内构建的吊装系统位形空间模型，可以理解为纵横交错的网格，通过A星算法可以快速地完成对每个网格节点的遍历，搜寻出最合适的路径，完成吊装路径规划，例如最终完成的吊装路径规划，为吊装用时最短的路径。其中，随着数据量的增长，A星算法的运算效率会有所降低。
需说明的是，在同一种起重机同一作业场景内，进行吊装路径规划时，只需要获取到吊装路径的起点和终点即可。而当起重机发生变化，或者是作业场景发生变化时，便需要重新构建吊装系统位形空间模型，以重新构建吊装路径规划模型，从而保证路径规划的准确性。
进一步的，在上述实施例的基础上，输出吊装规划路径之后，还可以包括：分别针对上车栅格图数据和下车栅格图数据，从起点开始搜索上车栅格图数据节点和下车栅格图数据节点；针对每个上车栅格图数据节点和下车栅格图数据节点，确定已行代价和预测代价；标记已行代价和预测代价于开启列表中，在开启列表中搜索总代价最小的节点，作为新的起点开始搜索，直至搜索至终点。
具体的，在完成吊装路径规划之后，还需要对吊装路径规划模型输出的吊装路径进行校准修正。修正的方式可以是分别校准上车路径和下车路径，分别针对上车栅格图数据和下车栅格图数据，从起点开始向周围搜索，确定出每个上车栅格图数据节点和下车栅格图数据节点，然后针对每个上车栅格图数据节点和下车栅格图数据节点，确定已行代价和预测代价，并将已行代价和预测代价于开启列表中，开启列表指的是已经搜索过的节点。然后在开启列表中搜索总代价最小的 节点，作为新的起点开始搜索，重复搜索操作，直至搜索至终点，然后再从开启列表中找到最优路径，作为最终的吊装规划路径，从而便完成了对吊装规划路径的优化校准。
进一步的，在上述实施例的基础上，本实施例中在输出吊装规划路径之后，还可以包括：基于吊装系统位形空间模型，转化吊装规划路径为起重机的动作序列；基于动作序列，生成起重机控制指令。
具体的，在确定出吊装规划路径之后，便需要将其吊装规划路径转化为起重机的动作序列，基于动作序列，生成起重机控制指令，从而控制起重机按照确定的吊装规划路径进行移动，起重机的控制指令控制起重机的各个部分按照吊装规划路径进行动作，最终完成吊装起点至终点的控制，起重机便完成吊装作业，由于吊装路径规划的合理性，便可以有效地提高吊装作业效率。
本发明中通过对起重机动作解耦合，灵活适用于起重机不同的工作模式，提高路径规划模块的计算效率。而且上车和下车的分层处理可以实现位形空间降维；降低了数据量并标准化起重机的位形坐标参数，提高了路径规划算法的性能。
基于同一总的发明构思，本发明还保护一种吊装路径规划模型构建装置，下面对本发明提供的吊装路径规划模型构建装置进行描述，下文描述的吊装路径规划模型构建装置与上文描述的吊装路径规划模型构建方法可相互对应参照。
图4是本发明提供的吊装路径规划模型构建装置的结构示意图。
如图4所示，本发明实施例提供的一种吊装路径规划模型构建装置，包括：
模拟模块401，用于建立起重机模型；
位形空间模块402，用于基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；
分组处理模块403，用于针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；
构建模块404，用于利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
本实施例提供的一种吊装路径规划模型构建装置，包括：建立起重机模型；基于当前作业场景和起重机模型，构建吊装系统位形空间模型，吊装系统位形空间模型包括起重机的上车数据和下车数据；针对吊装系统位形空间模型和上车数据，生成起重机的上车栅格图数据；针对吊装系统位形空间模型和下车数据，生成起重机的下车栅格图数据；利用A星算法，结合上车栅格图数据和下车栅格图数据，构建吊装路径规划模型，由于构建的吊装路径规划模型是基于上车栅格图数据和下车栅格图数据，使得通过将整个的路径划分为上车和下车两组，有效地降低了路径搜索时的数据量，提高了路径规划效率。
进一步的，本实施例中的所述上车数据包括：主臂变幅角度、上车回转角度和吊钩升降长度；
分组处理模块403，具体用于：
确定所述吊钩升降长度；
划分所述吊钩升降长度为预设数量的升降区间；
针对每个所述升降区间的端点，基于所述主臂变幅角度和所述上车回转角度在所述吊装系统位形空间模型内进行遍历搜索，计算上车碰撞信息，生成起重机的上车栅格图数据。
进一步的，本实施例中的所述下车数据包括行走参数和转向参数；
分组处理模块403，具体还用于：
基于所述行走参数和所述转向参数，在所述吊装系统位形空间模型内进行扫描遍历，得到下车碰撞信息；
根据所述下车碰撞信息，生成起重机的下车栅格图数据。
进一步的，本实施例中的构建模块404，具体用于：
利用A星算法，分别对所述上车栅格图数据和所述下车栅格图数据进行路径规划，得到上车路径规划模型和下车路径规划模型；
结合所述上车路径规划模型和所述下车路径规划模型，构建吊装路径规划模型。
基于同一总的发明构思，本发明还保护一种吊装路径规划装置，下面对本发明提供的吊装路径规划装置进行描述，下文描述的吊装路径规划装置与上文描述的吊装路径规划方法可相互对应参照。
图5是本发明提供的吊装路径规划装置的结构示意图。
如图5所示，本发明提供的一种吊装路径规划装置，包括：
确定模块501，用于确定吊装路径的起点和终点；
规划模块502，用于输入所述起点的坐标和所述终点的坐标至吊装路径规划模型，输出吊装规划路径为最优的吊装路径，所述吊装路径规划模型为根据上述任一实施例的吊装路径规划模型构建方法得到的。
进一步的，在上述实施例的基础上，本实施例中还包括：修正模块，用于：
分别针对上车栅格图数据和下车栅格图数据，从所述起点开始搜索上车栅格图数据节点和下车栅格图数据节点；
针对每个所述上车栅格图数据节点和所述下车栅格图数据节点，确定已行代价和预测代价；
标记所述已行代价和所述预测代价于开启列表中，在所述开启列表中搜索总代价最小的节点，作为新的起点开始搜索，直至搜索至所述终点。
进一步的，在上述实施例的基础上，本实施例中还包括：转化模块，用于：
基于吊装系统位形空间模型，转化所述吊装规划路径为起重机的 动作序列；
基于所述动作序列，生成起重机控制指令。
基于同一总的发明构思，本发明还保护一种起重机，起重机用于执行如上述任一实施例的吊装路径规划方法。
图6是本发明提供的电子设备的结构示意图。
如图6所示，该电子设备可以包括：处理器(processor)610、通信接口(Communications Interface)620、存储器(memory)630和通信总线640，其中，处理器610，通信接口620，存储器630通过通信总线640完成相互间的通信。处理器610可以调用存储器630中的逻辑指令，以执行吊装路径规划模型构建方法，该方法包括建立起重机模型；基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
此外，上述的存储器630中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
另一方面，本发明还提供一种计算机程序产品，所述计算机程序 产品包括计算机程序，计算机程序可存储在非暂态计算机可读存储介质上，所述计算机程序被处理器执行时，计算机能够执行上述各方法所提供的吊装路径规划模型构建方法，该方法包括建立起重机模型；基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
又一方面，本发明还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现以执行上述各方法提供的吊装路径规划模型构建方法，该方法包括建立起重机模型；基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现 有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (10)

  1. 一种吊装路径规划模型构建方法，其特征在于，包括：
    建立起重机模型；
    基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；
    针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；
    利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
  2. 根据权利要求1所述的吊装路径规划模型构建方法，其特征在于，所述上车数据包括：主臂变幅角度、上车回转角度和吊钩升降长度；
    所述针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据，包括：
    确定所述吊钩升降长度；
    划分所述吊钩升降长度为预设数量的升降区间；
    针对每个所述升降区间的端点，基于所述主臂变幅角度和所述上车回转角度在所述吊装系统位形空间模型内进行遍历搜索，计算上车碰撞信息，生成起重机的上车栅格图数据。
  3. 根据权利要求1所述的吊装路径规划模型构建方法，其特征在于，所述下车数据包括行走参数和转向参数；
    所述针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据，包括：
    基于所述行走参数和所述转向参数，在所述吊装系统位形空间模型内进行扫描遍历，得到下车碰撞信息；
    根据所述下车碰撞信息，生成起重机的下车栅格图数据。
  4. 根据权利要求1-3任一项所述的吊装路径规划模型构建方法，其特征在于，所述利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型，包括：
    利用A星算法，分别对所述上车栅格图数据和所述下车栅格图数据进行路径规划，得到上车路径规划模型和下车路径规划模型；
    结合所述上车路径规划模型和所述下车路径规划模型，构建吊装路径规划模型。
  5. 一种吊装路径规划方法，其特征在于，包括：
    确定吊装路径的起点和终点；
    输入所述起点的坐标和所述终点的坐标至吊装路径规划模型，输出吊装规划路径为最优的吊装路径，所述吊装路径规划模型为根据权利要求1至4任一项所述的吊装路径规划模型构建方法得到的。
  6. 根据权利要求5所述的吊装路径规划方法，其特征在于，所述输出吊装规划路径之后，还包括：
    分别针对上车栅格图数据和下车栅格图数据，从所述起点开始搜索上车栅格图数据节点和下车栅格图数据节点；
    针对每个所述上车栅格图数据节点和所述下车栅格图数据节点，确定已行代价和预测代价；
    标记所述已行代价和所述预测代价于开启列表中，在所述开启列表中搜索总代价最小的节点，作为新的起点开始搜索，直至搜索至所述终点。
  7. 根据权利要求5所述的吊装路径规划方法，其特征在于，所述输出吊装规划路径之后，还包括：
    基于吊装系统位形空间模型，转化所述吊装规划路径为起重机的动作序列；
    基于所述动作序列，生成起重机控制指令。
  8. 一种吊装路径规划模型构建装置，其特征在于，包括：
    模拟模块，用于建立起重机模型；
    位形空间模块，用于基于当前作业场景和所述起重机模型，构建吊装系统位形空间模型，所述吊装系统位形空间模型包括起重机的上车数据和下车数据；
    分组处理模块，用于针对所述吊装系统位形空间模型和所述上车数据，生成起重机的上车栅格图数据；针对所述吊装系统位形空间模型和所述下车数据，生成起重机的下车栅格图数据；
    构建模块，用于利用A星算法，结合所述上车栅格图数据和所述下车栅格图数据，构建吊装路径规划模型。
  9. 一种吊装路径规划装置，其特征在于，包括：
    确定模块，用于确定吊装路径的起点和终点；
    规划模块，用于输入所述起点和所述终点至吊装路径规划模型，输出吊装规划路径，所述吊装路径规划模型为根据权利要求1至4任一项所述的吊装路径规划模型构建方法得到的。
  10. 一种起重机，其特征在于，所述起重机用于执行如权利要求5至7任一项所述的吊装路径规划方法。
PCT/CN2023/100942 2022-07-29 2023-06-19 吊装路径规划模型构建方法、吊装路径规划方法及起重机 WO2024021924A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
DE112023000145.6T DE112023000145T5 (de) 2022-07-29 2023-06-19 Bauweise für ein hebepfad-planungsmodell, hebepfadplanungsverfahren und kran
US18/409,461 US20240143859A1 (en) 2022-07-29 2024-01-10 Hoisting path planning model construction method, hoisting path planning method and crane

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210908341.3A CN115330035A (zh) 2022-07-29 2022-07-29 吊装路径规划模型构建方法、吊装路径规划方法及起重机
CN202210908341.3 2022-07-29

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US18/409,461 Continuation US20240143859A1 (en) 2022-07-29 2024-01-10 Hoisting path planning model construction method, hoisting path planning method and crane

Publications (1)

Publication Number Publication Date
WO2024021924A1 true WO2024021924A1 (zh) 2024-02-01

Family

ID=83918771

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2023/100942 WO2024021924A1 (zh) 2022-07-29 2023-06-19 吊装路径规划模型构建方法、吊装路径规划方法及起重机

Country Status (4)

Country Link
US (1) US20240143859A1 (zh)
CN (1) CN115330035A (zh)
DE (1) DE112023000145T5 (zh)
WO (1) WO2024021924A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115330035A (zh) * 2022-07-29 2022-11-11 浙江三一装备有限公司 吊装路径规划模型构建方法、吊装路径规划方法及起重机

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104627842A (zh) * 2014-12-01 2015-05-20 长安大学 一种臂架式起重机吊装作业防碰撞方法及系统
JP2020021312A (ja) * 2018-08-01 2020-02-06 Ｊｆｅ物流株式会社 経路探索方法及びクレーンの動作パターンの作成方法
CN111735470A (zh) * 2020-07-29 2020-10-02 上海国际港务（集团）股份有限公司 一种动态环境下的自动导引运输车路径规划方法
CN111897341A (zh) * 2020-08-05 2020-11-06 三一专用汽车有限责任公司 泊车路径规划方法、装置和计算机可读存储介质
CN112069698A (zh) * 2020-09-27 2020-12-11 中国化学工程第六建设有限公司 基于bim的吊装仿真施工方法及其系统
CN113901611A (zh) * 2021-10-13 2022-01-07 广州市建筑科学研究院集团有限公司 一种基于a*算法改进的塔机吊运路径规划方法及装置
CN115330035A (zh) * 2022-07-29 2022-11-11 浙江三一装备有限公司 吊装路径规划模型构建方法、吊装路径规划方法及起重机

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104627842A (zh) * 2014-12-01 2015-05-20 长安大学 一种臂架式起重机吊装作业防碰撞方法及系统
JP2020021312A (ja) * 2018-08-01 2020-02-06 Ｊｆｅ物流株式会社 経路探索方法及びクレーンの動作パターンの作成方法
CN111735470A (zh) * 2020-07-29 2020-10-02 上海国际港务（集团）股份有限公司 一种动态环境下的自动导引运输车路径规划方法
CN111897341A (zh) * 2020-08-05 2020-11-06 三一专用汽车有限责任公司 泊车路径规划方法、装置和计算机可读存储介质
CN112069698A (zh) * 2020-09-27 2020-12-11 中国化学工程第六建设有限公司 基于bim的吊装仿真施工方法及其系统
CN113901611A (zh) * 2021-10-13 2022-01-07 广州市建筑科学研究院集团有限公司 一种基于a*算法改进的塔机吊运路径规划方法及装置
CN115330035A (zh) * 2022-07-29 2022-11-11 浙江三一装备有限公司 吊装路径规划模型构建方法、吊装路径规划方法及起重机

Also Published As

Publication number Publication date
DE112023000145T5 (de) 2024-04-04
CN115330035A (zh) 2022-11-11
US20240143859A1 (en) 2024-05-02

Similar Documents

Publication Publication Date Title
Lei et al. A methodology for mobile crane lift path checking in heavy industrial projects
Chang et al. A fast path planning method for single and dual crane erections
WO2024021924A1 (zh) 吊装路径规划模型构建方法、吊装路径规划方法及起重机
CN112115929B (zh) 一种作业臂架移动位姿的确定方法、装置及存储介质
US20130013251A1 (en) Crane maneuvering assistance
KR20190039438A (ko) 자재 핸들링 차량 경로 검증 및 동적 경로 수정
CN113085850B (zh) 车辆避障方法、装置、电子设备及存储介质
TW200931357A (en) A crane simulation method and system
CN111256722A (zh) 一种路径规划方法、装置、设备和存储介质
Zhang et al. Collaborative multi-agent systems for construction equipment based on real-time field data capturing
An et al. Re-optimization strategy for truck crane lift-path planning
US11898330B2 (en) Heavy equipment hazard warning apparatus and system and method for use of same
WO2020215711A1 (zh) 一种基站位置坐标的确定方法、装置、设备及存储介质
CN112212866A (zh) 机器人及其跨楼层导航方法和装置
WO2024000616A1 (zh) 行驶路径边界确定方法及装置、车辆、存储介质、终端
Zhu et al. A hierarchical deep reinforcement learning framework with high efficiency and generalization for fast and safe navigation
CN104528540A (zh) 臂架式起重机车载控制器内吊装方案实时生成方法及系统
CN114348678B (zh) 物料转运自动驾驶行车的控制方法、装置及电子设备
JP2020177416A (ja) 機械の自動運転制御方法、及びシステム
CN111998858B (zh) 一种基于改进a*算法的无人机航路规划方法
CN106709264A (zh) 双起重机系统变幅角响应建模算法及随机响应域预测方法
CN113978465A (zh) 一种变道轨迹规划方法、装置、设备以及存储介质
Lin et al. Lift path planning for tower cranes based on environmental point clouds
CN115877853A (zh) 一种智慧仓储物流路径规划系统及方法
CN112214037B (zh) 一种基于野外台站的无人机遥感组网航迹规划方法

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 112023000145

Country of ref document: DE

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23845137

Country of ref document: EP

Kind code of ref document: A1