A control method and device for an intelligent guided transport vehicle

Abstract

The present application provides a control method and device for an intelligent guided vehicle. The method includes: during the driving of the IGV, acquiring first point cloud data of the surrounding environment in front of and above the IGV scanned by a lidar disposed at the front end of the IGV, and acquiring second point cloud data of the surrounding environment behind and above the IGV scanned by a lidar disposed at the rear end of the IGV; matching and identifying the first point cloud data and the second point cloud data with pre-stored target feature data of port machinery equipment; and controlling the driving of the IGV according to the position of the port machinery equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, so that the IGV cooperates with the port machinery equipment to perform loading and unloading operations. The present application expands the sensing range of the intelligent guided vehicle and improves the accuracy and precision of its detection and identification of port machinery equipment.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular, to a control method and device for an intelligent guided transport vehicle. Background Art

[0002] In the intelligent construction of ports, IGV (Intelligent Guided Vehicle) plays an important role in port transportation operations due to its advantages such as automation and driverless. Due to the complex working environment of ports, in order to complete the cooperative operation with port machinery equipment, the IGV needs to accurately perceive the position of port machinery equipment and the state of spreaders around the vehicle body in real time during driving.

[0003] The existing IGV perception solution is to install various vehicle-mounted sensors or cameras on the IGV to collect information about port machinery equipment around the vehicle body, and then use algorithms to perform target detection and recognition on the collected data for controlling the IGV operation process.

[0004] Since the IGV has no towing head and the sensors or cameras are installed on the IGV body, the data collection field of view is limited, and the data collection is incomplete, thus reducing the accuracy of the IGV's detection and recognition of port machinery equipment. Summary of the Invention

[0005] The embodiments of this application provide a control method and device for an intelligent guided transport vehicle to expand the perception range of the intelligent guided transport vehicle, improve the accuracy and precision of its detection and recognition of port machinery equipment, and thus improve its operation efficiency.

[0006] In a first aspect, the embodiments of this application provide a control method for an intelligent guided transport vehicle, including:

[0007] During the driving of the IGV, obtain the first point cloud data of the surrounding environment in front of and above the IGV scanned by the lidar set at the front end of the IGV, and obtain the second point cloud data of the surrounding environment behind and above the IGV scanned by the lidar set at the rear end of the IGV;

[0008] Match and identify the first point cloud data and the second point cloud data with the pre-stored target feature data of port machinery equipment;

[0009] According to the position of the port machinery equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, control the driving of the IGV so that the IGV cooperates with the port machinery equipment for loading and unloading operations.

[0010] In a possible implementation manner of the first aspect, before the matching and identification of the first point cloud data and the second point cloud data with the pre-stored target feature data of port machinery equipment, it further includes:

[0011] Obtain the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system with the coordinate origin of the vehicle-mounted coordinate system of the IGV.

[0012] In a possible implementation manner, the method further includes:

[0013] Generate a point cloud image of the surrounding environment in front of and above the IGV with the front of the IGV as the coordinate origin according to the first point cloud data, and generate a point cloud image of the surrounding environment behind and above the IGV with the front of the IGV as the coordinate origin according to the second point cloud data.

[0014] In a possible implementation manner, the target feature data of the port machine equipment includes: the height feature and the contour feature of the port machine equipment; the matching and recognition of the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machine equipment includes:

[0015] Identify the position and spreader state of the port machine equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment;

[0016] Store the identified position coordinates of the port machine equipment and the position coordinates of the spreader in the vehicle-mounted computer.

[0017] In a possible implementation manner, the identifying the position and spreader state of the port machine equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment includes:

[0018] Identify the type of the port machine equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment;

[0019] Identify the spreader state of the port machine equipment in the first point cloud data and the second point cloud data according to the spreader activity range corresponding to the type of the port machine equipment.

[0020] In a possible implementation manner, the identifying the spreader state of the port machine equipment in the first point cloud data and the second point cloud data according to the spreader activity range corresponding to the type of the port machine equipment includes:

[0021] Draw a histogram in the height direction of the port machine equipment according to the first point cloud data and the second point cloud data and perform binarization processing, and display the container state on the spreader through the histogram after binarization processing.

[0022] In a second aspect, an embodiment of the present application provides a control device for an intelligent guided vehicle, including:

[0023] An acquisition module, configured to acquire first point cloud data of the surrounding environment in front of and above the IGV scanned by a lidar disposed at the front end of the IGV during the driving of the IGV, and acquire second point cloud data of the surrounding environment in the rear and above the IGV scanned by a lidar disposed at the rear end of the IGV;

[0024] A processing module, configured to match and identify the first point cloud data and the second point cloud data with pre-stored target feature data of port machinery equipment;

[0025] A control module, configured to control the driving of the IGV according to the position of the port machinery equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, so that the IGV cooperates with the port machinery equipment to perform loading and unloading operations.

[0026] In a possible implementation manner of the second aspect, the acquisition module further includes:

[0027] Acquire the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system and the coordinate origin of the vehicle-mounted coordinate system of the IGV.

[0028] In a possible implementation manner, the processing module is specifically configured to:

[0029] Generate a point cloud image of the surrounding environment in front of and above the IGV with the IGV's vehicle head as the coordinate origin according to the first point cloud data, and generate a point cloud image of the surrounding environment in the rear and above the IGV with the IGV's vehicle head as the coordinate origin according to the second point cloud data.

[0030] In a possible implementation manner, the target feature data of the port machinery equipment includes: the height feature and the contour feature of the port machinery equipment; the processing module is specifically configured to:

[0031] Identify the position of the port machinery equipment and the state of the spreader in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machinery equipment;

[0032] Store the identified position coordinates of the port machinery equipment and the position coordinates of the spreader in the vehicle-mounted computer.

[0033] In a possible implementation manner, the processing module is specifically configured to:

[0034] Identify the type of the port machinery equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machinery equipment;

[0035] Identify the spreader status of the port machinery equipment from the first point cloud data and the second point cloud data according to the spreader movement range corresponding to the type of the port machinery equipment.

[0036] In a possible implementation, the processing module is specifically configured to:

[0037] According to the first point cloud data and the second point cloud data, draw a histogram in the height direction of the port machinery equipment and perform binarization processing, and display the container status on the spreader through the histogram after binarization processing.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor and a memory;

[0039] The memory stores computer execution instructions;

[0040] The at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method according to any one of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored, and when a processor executes the computer execution instructions, the method according to any one of the first aspect is implemented.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program; when the computer program is executed, the method according to any one of the first aspect is implemented.

[0043] For the control method and device of the intelligent guided vehicle provided by the present application, first, during the IGV driving process, obtain the first point cloud data and the second point cloud data of the surrounding environment in front of, behind, and above the IGV scanned by the lidars arranged at the front and rear ends of the IGV; then match and identify the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machinery equipment; and further control the IGV driving according to the identified position of the port machinery equipment and the spreader status to cooperate with the port machinery equipment for loading and unloading operations. By using the point cloud data generated by scanning the surrounding environment in front of, behind, and above the IGV during the IGV driving process by the lidars, the comprehensiveness and accuracy of data collection are improved, and further the accuracy of detection and identification is improved. Description of the Drawings

[0044] The drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0045] Figure 1Schematic diagram of the IGV system provided by the embodiment of the present application;

[0046] Figure 2 Schematic flow chart of a control method for an intelligent guided vehicle provided by Embodiment 1 of the present application;

[0047] Figure 3 Schematic flow chart of a control method for an intelligent guided vehicle provided by Embodiment 2 of the present application;

[0048] Figure 4 Schematic diagram of the spreader status histogram provided by the embodiment of the present application;

[0049] Figure 5 Schematic diagram of the structure of a control device for an intelligent guided vehicle provided by the embodiment of the present application;

[0050] Figure 6 Schematic diagram of the structure of an electronic device provided by the embodiment of the present application.

[0051] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0052] Here, the exemplary embodiments will be described in detail, and their examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0053] In the field of artificial intelligence technology, IGV plays an important role in the intelligent construction of ports. To complete the cooperative operation with port machinery equipment, IGV needs to accurately perceive the position of port machinery equipment around the vehicle body and the status of the spreader in real time during driving. The existing technical solution is to load various sensors or cameras on the IGV to collect information about port machinery equipment around the vehicle body, and then analyze and calculate the collected data to complete the control of the IGV operation process. However, the above-mentioned existing technology has the problem of limited data collection vision, and it is impossible to accurately identify the position of port machinery equipment and the status of the spreader.

[0054] To solve the above technical problems, the embodiments of the present application provide a control method and device for an intelligent guided vehicle, which are applied to the field of artificial intelligence technology.

[0055] Figure 1Schematic diagram of the IGV system provided by the embodiments of the present application. As Figure 1 shown, on the IGV101, there are: an on-vehicle computer 102, a lidar 103 arranged at the front end of the IGV101, and a lidar 104 arranged at the rear end of the IGV101. Among them, the lidars 103 and 104 are connected to the on-vehicle computer 102 through cables. The lidars 103 and 104 can select mechanical rotary lidars and solid-state lidars with an upward field of view, and adopt a vertical installation method to expand the detection range, ensuring that when the IGV101 reaches near the operation position, it can scan both the port machine equipment and the self-vehicle container at the same time. After the lidars 103 and 104 are installed, they enter the real-time working state.

[0056] The lidars 103 and 104 can scan the point cloud data of the surrounding environment in front of, behind, and above the IGV101, and this point cloud data is used for matching and identifying with the pre-stored target feature data of the port machine equipment;

[0057] The on-vehicle computer 102 can obtain the point cloud data generated by the lidars 103 and 104 and perform analysis and calculation on the point cloud data, so as to control the driving of the IGV101 to cooperate with the port machine equipment for loading and unloading operations.

[0058] Figure 1 The regulations on the front and rear of the vehicle head are mainly for convenient description. Further, it is stipulated that when the vehicle head is forward, it is a forward operation, and when the vehicle tail is forward, it is a reverse operation.

[0059] The following uses specific embodiments to elaborate on the technical solutions of the present disclosure in detail. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0060] Figure 2 Schematic diagram of the control method flow of a smart guided transport vehicle provided by Embodiment 1 of the present application. As Figure 2 shown, for the method of this Embodiment 1, in this embodiment, the execution subject is the on-vehicle computer set on the IGV. It can be understood that the following steps can be implemented through hardware, software, or a combination of hardware and software. It includes:

[0061] S201: During the driving of the IGV, obtain the first point cloud data of the surrounding environment in front of and above the IGV scanned by the lidar arranged at the front end of the IGV, and obtain the second point cloud data of the surrounding environment behind and above the IGV scanned by the lidar arranged at the rear end of the IGV.

[0062] In this embodiment, the IGV refers to a smart guided transport vehicle, which has no towing vehicle head compared with an unmanned container truck, uses a trailer as the vehicle body, and is used for port transportation operations. There is an on-vehicle computer on the IGV to control the driving of the IGV.

[0063] In this embodiment, the point cloud data refers to the scan data generated by the lidar when scanning the surrounding environment of the IGV, which is recorded in the form of points. Each point contains three-dimensional coordinates, and the point cloud data is identified in the coordinate system of the lidar.

[0064] During the driving of the IGV, the lidars installed at the front and rear ends of the IGV are in a real-time working state. The lidars generate point cloud data by scanning the surrounding environment of the IGV in real time. Among them, the first point cloud data reflects the environmental information in front of and above the IGV; the second point cloud data reflects the environmental information behind and above the IGV.

[0065] The lidars at the front and rear ends of the IGV transmit the collected point cloud data to the on-vehicle computer installed on the IGV. The on-vehicle computer obtains the above point cloud data and stores it in the built-in storage unit of the on-vehicle computer.

[0066] S202: Match and identify the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machine equipment.

[0067] In this embodiment, the port machine equipment mainly refers to the quay crane in the port, generally including the shore bridge (also called the gantry crane) and the rail-mounted crane (also called the gantry crane). The spreader is a device used to lift containers in the port machine equipment and is a part of the port machine equipment.

[0068] In this embodiment, the target feature data of the port machine equipment is used to represent the shape and contour of the port machine equipment. Generally speaking, it can include: the height feature and the contour feature of the port machine equipment; for example, taking the origin of the on-vehicle coordinate system as the reference, the height range of 14 - 16m along the z-axis of the coordinate system can be used as the height feature; and the two horizontal girders of the port machine equipment can be used as the contour feature. The target feature data of the port machine equipment is pre-stored in the on-vehicle computer for the on-vehicle computer to identify whether there is port machine equipment in the point cloud data collected by the lidars installed at the front and rear ends of the IGV.

[0069] In a possible implementation manner, the following method can be used for matching and identification: According to the height feature and the contour feature of the port machine equipment, identify the position and spreader state of the port machine equipment in the first point cloud data and the second point cloud data; store the identified position coordinates of the port machine equipment and the position coordinates of the spreader in the on-vehicle computer.

[0070] In this embodiment, the on-vehicle coordinate system built in the on-vehicle computer can be established with reference to the position of the IGV. For example, the front of the IGV can be used as the coordinate origin of the on-vehicle coordinate system.

[0071] Specifically, the on-vehicle computer obtains the pre-configured target feature data of the port machine equipment and stores it in the built-in storage unit of the on-vehicle computer. The on-vehicle computer analyzes and processes the first and second point cloud data according to the target feature data of the port machine equipment to achieve matching and recognition. Among them, various recognition algorithms can be used in the recognition process. For example, the PCL library can be called for matching and recognition. The PCL library is an open-source C++ library used to implement general algorithms related to a large amount of point cloud data and efficient point cloud data management. The on-vehicle computer can implement the call to the PCL library.

[0072] As a possible recognition method, the type of the port machine equipment can be recognized in the first point cloud data and the second point cloud data according to the height feature and contour feature of the port machine equipment. Since the spreader contours and operating ranges of different types of port machine equipment are different, in order to accurately control the driving path of the IGV and the loading and unloading cooperation with the port machine equipment, the type of the port machine equipment can be recognized through the point cloud data collected by the lidar at the front and rear ends of the IGV, and then the spreader operating range corresponding to the port machine equipment can be recognized.

[0073] As a possible recognition method, the type of the port machine equipment can be judged through the port map. Specifically, the on-vehicle computer of the IGV has a built-in port map, which contains the approximate positions and equipment information of the port machine equipment. By comparing the coordinates of the recognized port machine equipment with the port map in the on-vehicle computer, the type of the port machine equipment and the spreader operating range corresponding to the type of the port machine equipment can be obtained.

[0074] As a possible recognition method, the on-vehicle computer can obtain the type of the port machine equipment from the server of the dispatching center. Specifically, the port machine equipment can send its own equipment information to the server of the dispatching center, and the on-vehicle computer obtains the type of the port machine equipment by establishing communication with the dispatching center.

[0075] S203: Control the IGV to drive and cooperate with the port machine equipment for loading and unloading operations according to the position of the port machine equipment and the state of the spreader recognized from the first point cloud data and the second point cloud data.

[0076] The built-in control module of the on-vehicle computer is used to control the driving of the IGV. Recognize the position of the port machine equipment and the state of the spreader, and control the driving of the IGV so that the port machine equipment cooperates with the spreader for loading and unloading operations. For example, if it is determined that the spreader state is lifting a container, it means that the spreader is performing loading and unloading operations. At this time, the on-vehicle computer controls the IGV to stop driving. After waiting for the front operation to be completed, it then controls the IGV to drive along the specified route towards the operation position below the spreader for subsequent loading and unloading operations; if the spreader is in an idle state, the on-vehicle computer controls the IGV to continue driving along the specified route, and performs subsequent loading and unloading operations after reaching the operation position below the spreader.

[0077] In this embodiment, lidar sensors are respectively arranged at the front and rear ends of the IGV to scan the surrounding environment of the IGV in real time. The point cloud data generated by the lidar sensors is used to reflect the surrounding environment information. The on-vehicle computer calls the PCL library to calculate and analyze the point cloud data according to the target feature data of the port machinery equipment pre-configured, and then judges the positions of the port machinery equipment around the IGV and the states of the spreaders to control the driving of the IGV. This improves the comprehensiveness and accuracy of data collection, and further improves the accuracy of detection and recognition.

[0078] Figure 3 It is a schematic flow chart of a control method for an intelligent guided vehicle provided in Embodiment 2 of the present application. On the basis of Embodiment 1 shown above, this embodiment further elaborates on the specific implementation manner of Embodiment 1. Figure 2 On the basis of the above-mentioned Embodiment 1, this embodiment further elaborates on the specific implementation manner of Embodiment 1.

[0079] As Figure 3 shown, the method of this Embodiment 2 includes:

[0080] S301: Obtain the coordinate system of the lidar sensor, and perform rotation and translation on the coordinate system of the lidar sensor to control the origin of the coordinate system of the lidar sensor to coincide with the origin of the on-vehicle coordinate system of the IGV.

[0081] In this embodiment, the execution entity is the on-vehicle computer. As Figure 1 shown, a lidar sensor is respectively arranged at the front and rear ends of the IGV, and the on-vehicle computer is connected to the lidar sensors through cables. First, the on-vehicle computer calibrates the coordinate system of the lidar sensor in the on-vehicle coordinate system. Specifically, the on-vehicle computer obtains the coordinate system of the lidar sensor, and calibrates the above coordinate system in the on-vehicle coordinate system of the IGV through rotation and translation, that is, the origin of the coordinate system of the lidar sensor coincides with the origin of the on-vehicle coordinate system. In this embodiment, the coordinate origin of the on-vehicle coordinate system can be the coordinate of the IGV's head, but it can be understood that the coordinate origin of the on-vehicle coordinate system can also be the coordinate of the IGV's body or tail.

[0082] S302: Obtain the first point cloud data of the surrounding environment in front of and above the IGV scanned by the lidar sensor arranged at the front end of the IGV, and obtain the second point cloud data of the surrounding environment behind and above the IGV scanned by the lidar sensor arranged at the rear end of the IGV.

[0083] During the driving of the IGV, the lidar sensors arranged at the front and rear ends of the IGV scan the surrounding environment of the IGV in real time and generate point cloud data in their respective lidar coordinate systems.

[0084] It should be understood that the specific implementation manner of S302 can refer to the detailed description of S201 in Embodiment 1, and will not be elaborated here.

[0085] S303: Generate a point cloud image of the surrounding environment in front of and above the IGV with the IGV's vehicle head as the coordinate origin based on the first point cloud data, and generate a point cloud image of the surrounding environment behind and above the IGV with the IGV's vehicle head as the coordinate origin based on the second point cloud data.

[0086] In this embodiment, the point cloud image refers to an image composed of point cloud data that can display the external contour features of things.

[0087] Since the point cloud data is represented in the coordinate system of the lidar, after the vehicle-mounted computer calibrates the coordinate system of the lidar, the first and second point cloud data in the lidar coordinate system will also be rotated, translated, and merged to generate corresponding point cloud images reflecting the surrounding environment information of the IGV.

[0088] S304: Match and identify the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machinery equipment.

[0089] In this embodiment, the target feature data of the port machinery equipment may include: the height feature and the contour feature of the port machinery equipment.

[0090] The IGV vehicle-mounted computer obtains the pre-configured target feature data of the port machinery equipment, including the height feature and the contour feature of the port machinery equipment. Specifically, with the origin of the vehicle-mounted coordinate system as the reference, the height range of 14 - 16 m along the z-axis of the coordinate system is used as the height feature; the two horizontal girders of the port machinery equipment are used as the contour feature.

[0091] In a possible implementation manner, the matching and identification may be performed in the following way, including: identifying the position and spreader state of the port machinery equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machinery equipment; storing the identified position coordinates of the port machinery equipment and the position coordinates of the spreader in the vehicle-mounted computer.

[0092] The vehicle-mounted computer analyzes and calculates the point cloud data reflecting the surrounding environment information of the IGV according to the above-obtained target feature data of the port machinery equipment. Specifically, the vehicle-mounted computer calls the PCL library to extract point cloud data similar to the target data of the port machinery equipment from the point cloud data, further matches and identifies the extracted point cloud data with the target data of the port machinery equipment, and obtains the identification result. If it is detected that there is port machinery equipment around the IGV, the identified position coordinates of the port machinery equipment are stored in the vehicle-mounted computer.

[0093] Further, the following method can be used to determine the type of the port machine equipment and the state of the spreader. Specifically, according to the height feature and contour feature of the port machine equipment, the type of the port machine equipment is identified in the first point cloud data and the second point cloud data; according to the spreader movement range corresponding to the type of the port machine equipment, the spreader state of the port machine equipment is identified in the first point cloud data and the second point cloud data.

[0094] As a possible identification method, the type of the port machine equipment can be determined through the port map. Specifically, the port map is built in the IGV vehicle-mounted computer, and the rough positions and equipment information of the port machine equipment are included in the port map. By comparing the coordinates of the identified port machine equipment with the port map in the vehicle-mounted computer, the type of the port machine equipment and the spreader movement range corresponding to the type of the port machine equipment can be obtained.

[0095] As a possible identification method, the vehicle-mounted computer can obtain the type of the port machine equipment from the server of the dispatching center. Specifically, the port machine equipment can send its own equipment information to the server of the dispatching center, and the vehicle-mounted computer obtains the type of the port machine equipment by establishing communication with the dispatching center.

[0096] Further, the following method can be used to determine the identification result. Specifically, according to the first point cloud data and the second point cloud data, a histogram is drawn in the height direction of the port machine equipment and binarized, and the container state on the spreader is displayed through the binarized histogram.

[0097] Figure 4 This is a schematic diagram of the spreader state histogram provided by the embodiment of the present application. Specifically, according to the type of the port machine equipment identified above, the spreader movement range of the port machine equipment is set as the RoI, where the RoI needs to be confirmed according to the actual port machine equipment. Here, RoI (Region of Interest) refers to the region of interest, that is, a region to be processed is outlined in the form of a square, circle, ellipse, irregular polygon, etc. from the processed point cloud image. In this embodiment, the spreader movement range of the port machine equipment is used as the RoI.

[0098] In this embodiment, the histogram refers to a bar chart for statistically analyzing the spreader state data, which can represent the change of the spreader state.

[0099] Binarization refers to a method for realizing image segmentation. In this embodiment, the histogram can be converted into a binary image through binarization, that is, the histogram includes a non-zero value area and a zero value area. The vehicle-mounted computer obtains the state of the spreader by judging the length and height of the non-zero values. The vehicle-mounted computer calls the PCL library to extract the point cloud data within the RoI range, and draws a histogram and performs binarization processing on the extracted point cloud data along the z-axis direction of the vehicle-mounted coordinate system, that is, the height direction of the port machine equipment, with a specific step size. The specific step size and the binarization threshold need to be confirmed according to the actual situation. In this embodiment, the histogram can be converted into a binary image through binarization, that is, the histogram includes a non-zero value area and a zero value area. As Figure 4 shown, the black area in the histogram is the non-zero value area, and the white area is the zero value area. The vehicle-mounted computer judges the state of the spreader by analyzing the histogram after binarization processing, including the height of the spreader and whether there is a container suspended. Specifically, the non-zero value area of the histogram represents that there is an object in this area, that is, at least the spreader exists, and the height of the non-zero value area of the histogram represents the height of the spreader; it can be judged whether the spreader is suspended with a container by the length of the non-zero value area of the histogram. The national standard stipulates that the height of the container is about 2m. If the length of the non-zero value area of the histogram is greater than 2m, the spreader is suspended with a container, otherwise it is not. The state of the spreader can be quickly identified through the histogram.

[0100] S305: According to the positions of the port machine equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, control the IGV to travel so that the IGV cooperates with the port machine equipment to perform loading and unloading operations.

[0101] It should be understood that the specific implementation manner of S305 can refer to the detailed description of S203 in Embodiment 1, and will not be elaborated here.

[0102] This application can support the IGV to accurately monitor the positions and spreader states of port machine equipment around the vehicle body in real time under different working conditions, solve the problem of limited data acquisition vision, expand the detection range, improve the accuracy of detection and recognition, reduce the implementation cost, and improve the operation efficiency of the IGV.

[0103] Figure 5 It is a schematic structural diagram of a control device of an intelligent guided vehicle provided by an embodiment of the present disclosure. The device in this embodiment can be in the form of software and / or hardware. As Figure 5 shown, the traffic strategy detection device 500 provided in this embodiment includes: an acquisition module 501, a processing module 502, and a control module 503. Among them,

[0104] An acquisition module 501 is configured to, during the driving of the IGV, acquire first point cloud data of the surrounding environment in front of and above the IGV scanned by a lidar disposed at the front end of the IGV, and acquire second point cloud data of the surrounding environment behind and above the IGV scanned by a lidar disposed at the rear end of the IGV;

[0105] A processing module 502 is configured to match and identify the first point cloud data and the second point cloud data with pre-stored target feature data of port machine equipment;

[0106] A control module 503 is configured to control the driving of the IGV according to the position of the port machine equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, so as to cooperate with the port machine equipment for loading and unloading operations.

[0107] In a possible implementation manner of the second aspect, the acquisition module 501 further includes:

[0108] Acquire the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system with the coordinate origin of the vehicle-mounted coordinate system of the IGV.

[0109] In a possible implementation manner, the processing module 502 is specifically configured to:

[0110] Generate a point cloud image of the surrounding environment in front of and above the IGV with the IGV's vehicle head as the coordinate origin according to the first point cloud data, and generate a point cloud image of the surrounding environment behind and above the IGV with the IGV's vehicle head as the coordinate origin according to the second point cloud data.

[0111] In a possible implementation manner, the target feature data of the port machine equipment includes: the height feature and the contour feature of the port machine equipment; the processing module 502 is specifically configured to:

[0112] Identify the position of the port machine equipment and the spreader state in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment;

[0113] Store the identified position coordinates of the port machine equipment and the position coordinates of the spreader in the vehicle-mounted computer.

[0114] In a possible implementation manner, the processing module 502 is specifically configured to:

[0115] Identify the type of the port machine equipment in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment;

[0116] Identify the spreader state of the port machine equipment in the first point cloud data and the second point cloud data according to the spreader movement range corresponding to the type of the port machine equipment.

[0117] In a possible implementation, the processing module 502 is specifically configured to:

[0118] According to the first point cloud data and the second point cloud data, draw a histogram in the height direction of the port crane equipment and perform binarization processing, and display the container status on the spreader through the histogram after binarization processing.

[0119] A control device for an intelligent guided transport vehicle provided in this embodiment can be used to execute the control method of the intelligent guided transport vehicle provided in any method embodiment. The implementation principle and technical effects are similar and will not be elaborated here.

[0120] Figure 6 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 6 shown, the electronic device 600 in this embodiment may include: a processor 601 and a memory 602.

[0121] The memory 602 is used to store computer execution instructions;

[0122] The processor 601 is used to execute the computer execution instructions stored in the memory to implement each step executed by the control method in the above embodiment. Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0123] Optionally, the memory 602 can be either independent or integrated with the processor 601.

[0124] When the memory 602 is independently provided, the electronic device further includes a bus 603 for connecting the memory 602 and the processor 601.

[0125] An embodiment of the present application also provides a computer-readable storage medium. Computer execution instructions are stored in the computer-readable storage medium, and when the processor executes the computer execution instructions, the control method executed by the above electronic device is implemented.

[0126] An embodiment of the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it is used to execute the technical solution of the method for determining the vehicle trajectory in the above embodiment.

[0127] The above computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0128] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include well-known knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0129] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A control method for an intelligent guided transport vehicle, characterized in that Including: During the driving of the IGV, obtain the first point cloud data of the surrounding environment in front of and above the IGV scanned by the lidar set at the front end of the IGV, and obtain the second point cloud data of the surrounding environment behind and above the IGV scanned by the lidar set at the rear end of the IGV; Obtain the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system with the coordinate origin of the vehicle-mounted coordinate system of the IGV, and match and identify the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machine equipment; the target feature data of the port machine equipment includes the height feature and the contour feature of the port machine equipment, the first point cloud data and the second point cloud data exist in the coordinate system of the lidar, and the first point cloud data and the second point cloud data in the coordinate system of the lidar are used to match and identify the pre-stored target feature data of the port machine equipment; According to the position of the port machine equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, control the IGV to drive so that the IGV cooperates with the port machine equipment to perform loading and unloading operations.

2. The method according to claim 1, wherein The method further includes: Generate a point cloud image of the surrounding environment in front of and above the IGV with the IGV's vehicle head as the coordinate origin according to the first point cloud data, and generate a point cloud image of the surrounding environment behind and above the IGV with the IGV's vehicle head as the coordinate origin according to the second point cloud data.

3. The method according to claim 2, wherein The target feature data of the port machine equipment includes: the height feature and the contour feature of the port machine equipment; the matching and identifying the first point cloud data and the second point cloud data with the pre-stored target feature data of the port machine equipment includes: According to the height feature and the contour feature of the port machine equipment, identify the position of the port machine equipment and the state of the spreader in the first point cloud data and the second point cloud data; Store the identified position coordinates of the port machine equipment and the position coordinates of the spreader in the vehicle-mounted computer.

4. The method according to claim 3, wherein The identifying the position of the port machine equipment and the state of the spreader in the first point cloud data and the second point cloud data according to the height feature and the contour feature of the port machine equipment includes: According to the height feature and the contour feature of the port machine equipment, identify the type of the port machine equipment in the first point cloud data and the second point cloud data; According to the spreader activity range corresponding to the type of the port machine equipment, identify the state of the spreader of the port machine equipment in the first point cloud data and the second point cloud data.

5. The method according to claim 4, characterized in that, The identifying the state of the spreader of the port machine equipment in the first point cloud data and the second point cloud data according to the spreader activity range corresponding to the type of the port machine equipment includes: According to the first point cloud data and the second point cloud data, draw a histogram in the height direction of the port machine equipment and perform binarization processing, and display the state of the container on the spreader through the histogram after binarization processing.

6. A control device for an intelligent guided transport vehicle, characterized in that, Including: An acquisition module, configured to acquire first point cloud data of the surrounding environment in front of and above the IGV scanned by a lidar disposed at the front end of the IGV during the driving of the IGV, and acquire second point cloud data of the surrounding environment behind and above the IGV scanned by a lidar disposed at the rear end of the IGV; A processing module, configured to acquire the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system with the coordinate origin of the vehicle-mounted coordinate system of the IGV, and match and identify the first point cloud data and the second point cloud data with pre-stored target feature data of port machine equipment; the target feature data of the port machine equipment includes the height feature and the contour feature of the port machine equipment, the first point cloud data and the second point cloud data exist in the coordinate system of the lidar, and the first point cloud data and the second point cloud data in the coordinate system of the lidar are used for matching and identifying with the pre-stored target feature data of the port machine equipment; A control module, configured to control the driving of the IGV according to the position of the port machine equipment and the state of the spreader identified from the first point cloud data and the second point cloud data, so that the IGV cooperates with the port machine equipment to perform loading and unloading operations.

7. The device according to claim 6, characterized in that, The acquisition module further includes: Acquire the coordinate system of the lidar, and perform rotation and translation on the coordinate system of the lidar to control the coincidence of the coordinate origin of the lidar coordinate system with the coordinate origin of the vehicle-mounted coordinate system of the IGV.

8. An electronic device, characterized in that, It includes: At least one processor and a memory; The memory stores computer execution instructions; The at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the processor executes the computer execution instructions, the method according to any one of claims 1-5 is implemented.

Images