Automatic Loading and Unloading Method, Device, Computer Equipment and Storage Medium

Abstract

The present invention relates to the technical field of intelligent robots, and discloses an automatic loading and unloading method, device, computer device and storage medium. The method includes: obtaining a point cloud data set inside a cargo container collected by a first lidar device during the descending process, wherein the lidar device is suspended above the platform by a lifting device, and the lifting device drives the lidar device to move up and down; determining the box size of the cargo container based on the point cloud data set; obtaining the size of a pallet for placing goods; determining a first target position of an autonomous forklift based on the box size and the pallet size, the first target position being the position where the autonomous forklift is located when loading or unloading goods in the cargo container; sending the first target position to the autonomous forklift, and when the autonomous forklift runs to the first target position, performing a loading or unloading task. The present invention can adapt to different types of cargo containers and can effectively improve the loading and unloading efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent robots, and particularly to an automatic loading and unloading method, device, computer device, and storage medium. Background Art

[0002] The platform plays an important role in logistics and transportation. Currently, such a scenario can often be seen on the platforms of large factories. A truck loaded with a cargo container docks at the internal platform of the factory. Then, the internal personnel of the factory open the adjustment platform, and the adjustment platform builds a path from the inside of the factory to the cargo container. Next, factory workers start to shuttle between the cargo container - platform - warehouse to perform loading and unloading. Regarding platform loading, the goods inside the warehouse are manually lifted by a forklift, transported through the platform to the designated position inside the cargo container and then put down until the container is full of goods, completing the loading. Regarding platform unloading, a forklift is driven manually into the cargo container to pick up the goods and put them into the warehouse through the platform until the unloading is completed.

[0003] Due to the variety of truck models, there are often problems of inaccurate docking when the truck docks at the platform, which brings great obstacles to the automatic loading and unloading of the platform. Therefore, most of the entire loading and unloading process of the cargo container is completed manually. However, for the loading and unloading completed manually, although the position or posture can be adjusted according to the actual situation during the loading and unloading process, ensuring the flexibility of loading and unloading, its loading and unloading efficiency is low, and the working intensity of factory workers is increased. Summary of the Invention

[0004] In view of this, the present invention provides an automatic loading and unloading method, device, computer device, and storage medium to solve the problem in the prior art that the automatic loading and unloading cannot adapt to different trucks, and only manual labor can be used to achieve the loading and unloading between the cargo container and the platform.

[0005] In a first aspect, the present invention provides an automatic loading and unloading method, which includes:

[0006] Obtain the point cloud data set inside the cargo container collected by the first lidar device during the descending process, wherein the lidar device is suspended above the platform through a lifting device, and the lifting device drives the lidar device to move up and down;

[0007] Based on the point cloud data set, determine the box size of the cargo container, where the box size includes the box length and the box width;

[0008] Obtain the size of the pallet for placing goods;

[0009] Based on the dimensions of the box and the pallet, determine the first target position of the driverless forklift, where the first target position is the position where the driverless forklift is located when loading or unloading goods in the cargo container;

[0010] Send the first target position to the driverless forklift. When the driverless forklift runs to the first target position, it performs the loading or unloading task.

[0011] In the automatic loading and unloading method provided in this embodiment, the automatic loading and unloading can be automatically completed after the truck stops at the platform, and it can adapt to various cargo containers with different lengths, widths, heights, and internal environments. It can enable the intelligent driverless forklift to perform tasks safely and orderly, effectively improve the loading and unloading efficiency, reduce or even eliminate the need for factory employees to participate, achieve one-key automatic loading and unloading, and achieve a lower cargo damage rate and a higher intelligent management level.

[0012] In an alternative embodiment, the method further includes:

[0013] Based on the point cloud data set, determine the vertical distance and the offset angle between the first lidar device and one inner wall of the box;

[0014] Based on the vertical distance and the offset angle, determine the docking deviation of the cargo container;

[0015] Determine the loading arrangement or unloading arrangement of the cargo container;

[0016] Based on the docking deviation, the loading arrangement or the unloading arrangement, determine the second target position for the driverless forklift to enter the cargo container;

[0017] Send the second target position to the driverless forklift, and the driverless forklift enters the cargo container from the second target position.

[0018] In this embodiment, it is possible to adaptively adjust the target position before entering the cargo container according to the docking deviation, which can effectively improve the success rate and working efficiency of the driverless forklift for picking up and placing goods.

[0019] In an alternative embodiment, the method further includes:

[0020] Obtain the lowering height of the first lidar device, where the lowering height is the lowering height of the first lidar device when it first collects the point cloud data set;

[0021] Obtain the docking plate length and the angle between the docking plate and the ground when the platform docking device is docked with the cargo container;

[0022] Based on the lowering height, the docking plate length, and the angle, determine the box height of the cargo container;

[0023] Adjust the height of the second lidar device on the driverless forklift according to the height of the box body. The second lidar device is used to scan the object data in front of the running driverless forklift.

[0024] In this embodiment, the driverless forklift can adaptively adjust the height of the lidar on the top of the driverless forklift according to the measured height of the cargo container, ensuring that the forklift will not be unable to enter the interior of the cargo container due to the internal height of different cargo containers, and will not cause positioning failure inside the carriage due to the height of the goods. Furthermore, it can effectively guarantee the success rate of loading and unloading goods and the working efficiency.

[0025] In an alternative embodiment, when the driverless forklift performs a loading task, determining the first target position of the driverless forklift based on the box body size and the pallet size includes:

[0026] Determine the loading arrangement of the cargo container;

[0027] Determine the current loading sequence number of the driverless forklift;

[0028] Based on the loading arrangement, the current loading sequence number, the box body size, and the pallet size, determine the first target position of the driverless forklift.

[0029] In this embodiment, by determining the first target position of the driverless forklift according to the loading arrangement of the cargo container and the current loading sequence number, the intelligent driverless forklift can perform tasks safely and orderly, and can effectively improve the loading efficiency.

[0030] In an alternative embodiment, the method further includes:

[0031] Based on the point cloud data set, determine the bottom surface points and non-bottom surface points of the box body;

[0032] Based on the bottom surface points of the box body, determine the flatness of the bottom surface of the box body;

[0033] Based on the flatness of the bottom surface, determine the running speed of the driverless forklift inside the cargo container;

[0034] Based on the non-bottom surface points of the box body, determine whether there are foreign objects inside the cargo container;

[0035] In the case of the existence of foreign objects, issue an alarm prompt.

[0036] In this embodiment, it can automatically detect the flatness of the bottom surface of the cargo container, realize the self-adjustment of the running speed of the driverless forklift inside the cargo container to simultaneously meet the running efficiency and safety of the system, and can also detect foreign objects in the internal environment of the cargo container to ensure safety.

[0037] In an alternative embodiment, the method further includes:

[0038] Determine whether there is a situation where two or more unmanned forklifts pass through the same first target position at the same time;

[0039] In the case where multiple unmanned forklifts pass through the same first target position at the same time, lock the paths of the unmanned forklifts within the preset area and issue a stop command.

[0040] This embodiment can support multiple unmanned forklifts to operate safely at the same time, enabling the automatic loading and unloading system to have a high operating efficiency.

[0041] In a second aspect, the present invention provides an automatic loading and unloading system, which includes:

[0042] A lifting platform device, which includes a lifting device and a first lidar device. The first lidar device is suspended above the platform through the lifting device, and the lifting device drives the lidar device to move up and down to detect the point cloud data inside the cargo container through the first lidar device;

[0043] An unmanned forklift, which includes a forklift body and a forklift control module;

[0044] A background scheduling device, which is communicatively connected to the lifting platform device and the forklift control module. The background scheduling device is also communicatively connected to the platform docking device, and the background scheduling device is used for any of the above automatic loading and unloading methods.

[0045] In an optional implementation manner, the background scheduling device includes:

[0046] A point cloud data acquisition module, which is used to acquire the point cloud data set inside the cargo container collected by the first lidar device during the descending process. Among them, the lidar device is suspended above the platform through the lifting device, and the lifting device drives the lidar device to move up and down;

[0047] A box size determination module, which is used to determine the box size of the cargo container based on the point cloud data set. The box size includes the box length and the box width;

[0048] A pallet size acquisition module, which is used to acquire the size of the pallet for placing goods;

[0049] A first target position determination module, which is used to determine the first target position of the unmanned forklift based on the box size and the pallet size. The first target position is the position where the unmanned forklift is located when loading or unloading goods in the cargo container;

[0050] An instruction sending module, which is used to send the first target position to the unmanned forklift. When the unmanned forklift runs to the first target position, it executes the loading or unloading task.

[0051] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the automatic loading and unloading method according to the first aspect or any corresponding embodiment thereof.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the automatic loading and unloading method according to the first aspect or any corresponding embodiment thereof.

[0053] It should be noted that since the automatic loading and unloading device, computer device, and computer-readable storage medium provided by the present invention correspond to the above-mentioned automatic loading and unloading method. Therefore, for the beneficial effects of the automatic loading and unloading device, computer device, and computer-readable storage medium, please refer to the description of the corresponding beneficial effects of the automatic loading and unloading method above, and will not be elaborated here. Description of the Drawings

[0054] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 is a schematic flowchart of the automatic loading and unloading method according to an embodiment of the present invention;

[0056] Figure 2 is a schematic diagram of the position where the first lidar device is arranged according to an embodiment of the present invention;

[0057] Figure 3 is a schematic coordinate diagram of the determination of the first target position according to an embodiment of the present invention;

[0058] Figure 4 is a structural block diagram of the automatic loading and unloading system according to an embodiment of the present invention;

[0059] Figure 5 is a structural block diagram of the background scheduling device according to an embodiment of the present invention;

[0060] Figure 6 is a schematic hardware structure diagram of the computer device according to an embodiment of the present invention. Detailed Embodiments

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0062] If one wants to achieve full-process automation and closed-loop of factory logistics, the platform is an important scenario that cannot be avoided. Since there are various truck models with different lengths, heights, and internal environments, it will lead to inaccurate docking of trucks at the platform and inaccurate internal positioning when unmanned forklifts enter long cargo containers. These problems all pose obstacles to automatic loading and unloading at the platform. For the above reasons, the loading and unloading processes in most large factories are currently completed manually. Therefore, the present invention designs a mature automatic loading and unloading solution based on intelligent unmanned forklifts, which can adapt to different vehicle models, ensure the safe and orderly execution of tasks by intelligent unmanned forklifts, reduce or even eliminate the need for factory employees to participate, achieve one-key automatic loading and unloading, and achieve a lower cargo damage rate and a higher intelligent management level.

[0063] According to an embodiment of the present invention, an embodiment of an automatic loading and unloading method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0064] In this embodiment, an automatic loading and unloading method is provided, which can be executed by devices such as servers, terminals, and mobile terminals. Figure 1 is a flowchart of the automatic loading and unloading method according to an embodiment of the present invention, as Figure 1 shown, and this process includes the following steps:

[0065] Step S101, obtain the point cloud data set inside the cargo container collected by the first lidar device during the descending process, where the lidar device is suspended above the platform by a lifting device, and the lifting device drives the lidar device to move up and down.

[0066] Referring to Figure 2 shown, it is a schematic diagram of the position setting of the first lidar device, which may include a multi-line lidar device and a set of mechanical structures that can automatically lift, that is, a lifting device, and this automatic lifting device can drive the multi-line lidar device to move up and down.

[0067] Generally, the first lidar device is initially located at the initial position, that is, the position where the first lidar device is located when it is at the uppermost position within the liftable range of the automatic lifting device. After the cargo container is successfully docked with the platform docking device, the lifting device drives the first lidar device to move downward. At the same time, the first lidar device starts lidar scanning. When the point cloud data inside the cargo container is collected, a point cloud data set corresponding to the descending height is generated and stored in the system for future use.

[0068] Step S102: Based on the point cloud data set, determine the dimensions of the cargo container, where the dimensions of the cargo container include the length and width of the container. Specifically, machine learning algorithms can be used to identify and measure the dimensions of the container in the point cloud. The length of the container can be 12m, and the width can be 2.4m, etc.

[0069] In this embodiment, the length and width data of the docked cargo container can be automatically measured based on the point cloud data set, which can provide a basis for calculating the target pose of loading and unloading goods inside the cargo container. Since the cargo container generally contains two rows or a single row of goods, it is necessary to know at which target pose the loading and unloading are performed to ensure the success rate of loading and unloading.

[0070] Step S103: Obtain the dimensions of the pallet for placing goods. For example, the pallet can be a square pallet with a length and width of 1m each.

[0071] Step S104: Based on the dimensions of the cargo container and the dimensions of the pallet, determine the first target position of the driverless forklift. The first target position is the position where the driverless forklift is located when loading or unloading goods inside the cargo container.

[0072] It can be referred to Figure 3 As shown, a coordinate system of the cargo container can be established with a corner of the container as the origin of the coordinate system. Based on the known dimensions of the cargo container and the dimensions of the pallet, the positions of the cargo container and the pallet in the coordinate system can be determined. Then, the pose of each target point for loading and unloading each pallet can be known, that is, the first target position where the driverless forklift will move to can be determined.

[0073] Step S105: Send the first target position to the driverless forklift. When the driverless forklift moves to the first target position, it performs the loading or unloading task.

[0074] The automatic loading and unloading method provided in this embodiment can automatically complete the automatic loading and unloading after the truck docks at the platform, and can adapt to various cargo containers with different lengths, widths, heights, and internal environments. It can enable the intelligent driverless forklift to perform tasks safely and orderly, effectively improve the loading and unloading efficiency, reduce or even eliminate the need for factory employees to participate, achieve one-key automatic loading and unloading, and achieve a lower cargo damage rate and a higher intelligent management level.

[0075] In some alternative embodiments, the method further includes:

[0076] Step S201: Based on the point cloud data set, determine the vertical distance and the offset angle between the first lidar device and one inner wall of the box.

[0077] Since the point cloud data set includes data points of the inner wall and the bottom surface of the box, some reference points can be selected on the inner wall of the box, and the vertical distance can be determined through these points. Then, a reference plane parallel to the inner wall of the box, such as the bottom surface of the box or the roof of the vehicle, can be selected as the reference plane. The offset angle of the lidar relative to the reference plane can be determined through the data points of the inner wall, the bottom surface or the roof of the box.

[0078] Step S202: Based on the vertical distance and the offset angle, determine the docking deviation of the freight container. The docking deviation can be the deviation between the middle position of the bottom surface of the freight container box and the middle position of the platform docking plate.

[0079] Step S203: Determine the loading arrangement or the unloading arrangement of the freight container. Among them, the loading arrangement or the unloading arrangement can be an arrangement of two rows on both sides and a single row in the middle, that is, two rows of goods can be placed on both sides of the freight container, or a single row of goods can be placed in the middle of the freight container.

[0080] Step S204: Based on the docking deviation, the loading arrangement or the unloading arrangement, determine the second target position for the driverless forklift to enter the freight container. The second target position is the position for the driverless forklift to enter the freight container, and specifically, the target position before the driverless forklift enters the freight container can be adjusted according to the requirements of the docking deviation and the arrangement of the goods in the freight container.

[0081] Step S205: Send the second target position to the driverless forklift, and the driverless forklift enters the freight container from the second target position.

[0082] Generally, there will definitely be a certain parking error when a truck driver docks with the platform, and it is very difficult to accurately park the cargo container in the exact middle of the platform. When there is a large parking deviation, if the driverless forklift still follows a fixed route for loading or unloading goods, especially for loading or unloading at the door of the cargo container, the probability of loading and unloading failure will increase. For loading and unloading at the front part (near the truck head position) of the cargo container, the lateral deviation between the driverless forklift and the route can be slowly adjusted after the driverless forklift enters the cargo container. However, for loading and unloading at the tail part (near the cargo door position) of the cargo container, on the docking plate, that is, before the driverless forklift enters the cargo container, a large lateral deviation needs to be avoided. Otherwise, due to being too close to the first target position and having insufficient deviation correction distance, the loading and unloading will fail. Therefore, it is necessary to adjust the route before entering the cargo container according to different docking errors, so that the lateral deviation between the driverless forklift and the position where goods need to be loaded and unloaded before entering the cargo container is as small as possible, in order to ensure the success rate of loading and unloading.

[0083] In this embodiment, the target position before entering the cargo container can be adaptively adjusted according to the docking deviation, which can effectively improve the success rate and working efficiency of the driverless forklift for loading and unloading goods.

[0084] In some optional implementation manners, the method further includes:

[0085] Step S301, obtain the descending height of the first lidar device, where the descending height is the descending height of the first lidar device when it first collects the point cloud data set.

[0086] Step S302, obtain the length of the docking plate and the angle between the docking plate and the ground when the platform docking device docks with the cargo container.

[0087] Step S303, determine the height of the cargo container body based on the descending height, the length of the docking plate, and the angle.

[0088] Step S304, adjust the height of the second lidar device on the driverless forklift according to the height of the cargo container body. The second lidar device is used to scan the object data in front of the driverless forklift during operation.

[0089] In this embodiment, not only can the length and width of the cargo container be determined according to the first lidar device, but also the height of the cargo container can be determined according to the first lidar device. First, the angle between the automatically adjustable docking plate and the ground after the docking plate on the platform is docked with the truck and the extended length of the docking plate can be obtained. According to this angle and this length, the height difference between the bottom surface of the cargo container body and the docked platform ground can be obtained. At the same time, during the automatic descent process of the first lidar device from the initial position, it will continuously detect whether the point cloud features of the cargo container can be found in the acquired point cloud data. If not, it means that the first lidar device has not descended to the position where it can scan the point cloud data inside the cargo container, and it will continue to descend until the corresponding point cloud features of the cargo container can be found. Then it is considered that the first lidar device has descended in place and the descent stops. At this time, the descent height of the lifting device is known. Combining with the initial position, the descent height of the first lidar device can be obtained, and then the approximate height of the carriage can be calculated.

[0090] Furthermore, both the first lidar device and the unmanned forklift body are connected to the scheduling device, and the scheduling device can communicate with the first lidar device and the unmanned forklift body. The determined height data of the cargo container will be notified to the background scheduling device, and the scheduling device will notify the connected intelligent unmanned forklift before performing automatic loading and unloading. The bracket of the second lidar device installed on the unmanned forklift can move up and down, and the unmanned forklift will adaptively adjust the height of the installed second lidar device according to the height of the cargo container.

[0091] The purpose of adjusting the height of the second lidar device on the unmanned forklift is to enable the unmanned forklift to enter the cargo container while ensuring that the laser scan will not be blocked by the goods inside the cargo container after entering the cargo container, and the laser can scan the entire contour data inside the cargo container, including both sides and the bottom of the cargo container. It only needs to adjust the height of the laser on the forklift to a height where the forklift can enter the cargo container without hitting the upper part of the cargo container and higher than the height of the goods inside the cargo container. According to this, the height of the second lidar device will not affect the internal positioning of the unmanned forklift after entering the cargo container.

[0092] In this embodiment, the unmanned forklift can adaptively adjust the height of the lidar at the top of the unmanned forklift according to the measured height of the cargo container, ensuring that the forklift will not be unable to enter the cargo container due to the internal height of different cargo containers, and will not cause the internal positioning of the carriage to fail due to the height of the goods. Thus, the success rate of picking and placing goods and the working efficiency can be effectively guaranteed.

[0093] In some alternative embodiments, when the driverless forklift performs the loading task, the above step S104, that is, determining the first target position of the driverless forklift based on the box size and the pallet size, includes:

[0094] Step a1, determining the loading arrangement of the cargo container, and the arrangement includes: two rows on both sides and a single row in the middle.

[0095] Step a2, determining the current unloading sequence number of the driverless forklift.

[0096] Step a3, determining the first target position of the driverless forklift based on the loading arrangement, the current unloading sequence number, the box size, and the pallet size.

[0097] Refer to Figure 3 As shown in the figure, the following is an example. For example, when the cargo container needs to be loaded, it is preset to unload goods from left to right at the positions on both sides of the cargo container. Suppose after the driverless forklift picks up goods from the warehouse storage area, the center of the rear wheels of the driverless forklift coincides with the center position of the pallet. The length of the pallet is Lp, the width is Wp, the length of the cargo container is Lc, the width is Wc, and the unloading sequence number for unloading goods into the cargo container is i. Then the target pose sent to the driverless forklift on the left side is successively: (Lp / 2 + Lp×i, (Wc - 2×Wp) / 3 + Wp / 2). The situation on the right side is similar. Before picking up goods from the cargo container, a first target position at the bottom of the cargo container can be sent to the driverless forklift first. As the driverless forklift enters the cargo container, the vision camera of the driverless forklift body will detect the pallet, and then start to take over the fork-lifting process.

[0098] In this embodiment, by implementing the determination of the first target position of the driverless forklift according to the loading arrangement of the cargo container and the current unloading sequence number, the intelligent driverless forklift can perform tasks safely and orderly, effectively improving the loading efficiency, reducing or even eliminating the need for factory employees to participate, achieving one-key automatic loading, and achieving a lower cargo damage rate and a higher intelligent management level.

[0099] In some alternative embodiments, when the driverless forklift performs the unloading task, the above step S104, that is, determining the first target position of the driverless forklift based on the box size and the pallet size, includes:

[0100] Step b1, determining the unloading arrangement of the cargo container.

[0101] Step b2, determining the current picking-up sequence number of the driverless forklift.

[0102] Step b3, determining the first target position of the driverless forklift based on the unloading arrangement, the current picking-up sequence number, the box size, and the pallet size.

[0103] Specifically, the specific example of the above loading task can be referred to. In this embodiment, determining the first target position of the driverless forklift according to the unloading arrangement of the cargo container and the current picking sequence number can enable the intelligent driverless forklift to execute tasks safely and orderly, effectively improve the picking efficiency, reduce or even eliminate the need for factory employees to participate, achieve one-key automatic picking, and achieve a lower cargo damage rate and a higher intelligent management level.

[0104] In some alternative embodiments, the method further includes:

[0105] Step S401: Based on the point cloud dataset, determine the bottom surface points and non-bottom surface points of the box body;

[0106] Step S402: Based on the bottom surface points of the box body, determine the flatness of the bottom surface of the box body. Specifically, the point cloud within a specific area can be defined, and an extraction algorithm can be used to judge the bottom surface points and non-bottom surface points, and the flatness of the bottom surface of the box body can be judged according to the threshold.

[0107] Step S403: Based on the flatness of the bottom surface, determine the running speed of the driverless forklift inside the cargo container. If the flatness of the bottom surface of the box body reaches a relatively flat threshold, the speed for the driverless forklift to enter the cargo box for operation can be sent at more than 0.6, and the actual sent speed is linearly related to the flatness. The higher the flatness, the greater the speed, and the maximum can be 0.8 m / s. The lower the flatness, the smaller the speed, and the minimum can be 0.3 m / s.

[0108] Step S404: Based on the non-bottom surface points of the box body, judge whether there are foreign objects inside the cargo container.

[0109] Step S405: In the case of the existence of foreign objects, issue an alarm prompt.

[0110] Data processing can be performed through algorithms such as classification and clustering. If it is found that there are large foreign objects, the backend system will alarm and notify relevant personnel to enter the cargo box to handle the foreign objects.

[0111] Due to the different truck suppliers in the factory, different compartments being docked, and the same compartment having different degrees of newness or oldness, there are significant differences in the flatness of the bottom surface inside the compartment. Some cargo containers are relatively new and have good flatness, while some cargo containers have been in use for a long time. Due to long-term use, the bottom surface of the cargo container is uneven, even wavy. If the driverless forklift walks inside the compartment at a fixed speed of 0.6 m / s - 0.8 m / s at this time, the forklift will experience significant jolts, and even large up-and-down and left-and-right shakes. At this time, due to inertia, inaccurate control or control lag may occur during the process. Coupled with the inaccurate positioning caused by the up-and-down and left-and-right shakes of the forklift, there may be a safety problem of hitting the wall of the cargo container. However, the forklift cannot drive inside the cargo container at a speed of 0.2 m / s - 0.4 m / s for safety reasons. At this time, the overall efficiency of automatic loading and unloading will be affected because some cargo containers are relatively long, possibly more than 17 meters long. Therefore, it is necessary to automatically detect the flatness of the bottom surface of the cargo container based on the scanned point cloud data at this time, and adaptively adjust the speed of the driverless forklift for loading and unloading operations inside the cargo container according to the flatness of the bottom surface of the cargo container. If a large foreign object is found inside the cargo container, the back-end system will alarm and notify relevant personnel for handling.

[0112] In this embodiment, it is possible to automatically detect the flatness of the bottom surface of the cargo container, realize the self-regulation of the running speed of the driverless forklift inside the cargo container to simultaneously meet the running efficiency and safety of the system, and can also detect foreign objects in the internal environment of the cargo container to ensure safety.

[0113] In some alternative embodiments, the method further includes:

[0114] Step S501, determining whether there is a situation where two or more driverless forklifts pass through the same first target position at the same time.

[0115] Step S502, in the case where multiple driverless forklifts pass through the same first target position at the same time, lock the paths of the driverless forklifts within the preset area and issue a stop command.

[0116] The main functions of the background scheduling device are to generate the routes of the driverless forklifts and to schedule and control the driverless forklifts. Considering the loading and unloading efficiency of automatic loading and unloading, the efficiency of a single forklift for loading and unloading is relatively low. In most cases, two or more forklifts are used for automatic loading and unloading operations simultaneously. However, there is a risk of collision when multiple driverless forklifts operate simultaneously. The scheduling algorithm in the background scheduling device will control the forklifts based on the poses reported by multiple driverless forklifts. Once it is found that one forklift and other forklifts need to pass through the same area or the same point, the current forklift or other forklifts will be locked in the area in advance, and a stop instruction will be sent to the forklift. The forklift will be released when there is no risk of collision between the forklifts, ensuring safety even when multiple forklifts operate simultaneously.

[0117] Route generation is achieved after the background scheduling device is connected to systems such as the warehouse management system in the factory. It can obtain the location of the goods to be loaded or the location where the goods should be placed during unloading. After obtaining the specific locations for picking up and placing the goods, the background scheduling device will generate corresponding paths for the driverless forklifts that receive the command tasks according to the target points to be reached, and the driverless forklifts just follow the paths.

[0118] This embodiment can support the safe operation of multiple driverless forklifts simultaneously, enabling the automatic loading and unloading system to have a high operating efficiency.

[0119] The following presents a complete loading and unloading process:

[0120] The process of loading goods into the carriage in the automatic loading and unloading system is as follows: Detect that the goods container has docked in place; Start the lidar device to scan. The lidar device starts to descend until it detects the carriage features, obtains the point cloud data inside the carriage, and then stops scanning. After the scanning is completed, the lidar device returns to its original position; Calculate the length, width, and height of the goods container, and send the corresponding instruction to adjust the lidar height to the corresponding driverless forklift according to the calculated height of the container body to adjust the lidar height of the driverless forklift; After receiving the instruction to place the goods into the carriage, the driverless forklift navigates to the position where the goods are to be placed in the designated temporary storage area; Judge the lateral shift and angular deviation between the identified pallet and the current position reached, and generate a fixed trajectory according to the deviation to make the picking deviation smaller; After the driverless forklift picks up the goods, the scheduling device generates a corresponding second target position before entering the goods container based on data such as the docking deviation of the goods container; After reaching the second target position, start entering the cargo box to deliver the goods to the specified first target position in the cargo box. After placing the goods, navigate to the middle of the cargo box, and then exit the cargo box. If there is another task, continue to repeat the above process.

[0121] The process of unloading goods from the carriage in the automatic loading and unloading system is as follows: Detect that the cargo container has docked in place; Start the lidar device to scan. The lidar device starts to descend until it detects the carriage features, obtains the point cloud data inside the carriage, and then stops scanning. After the scanning is completed, the lidar device returns to its original position; Calculate the length, width, and height of the cargo container, and send the corresponding adjustment laser height command to the corresponding unmanned forklift according to the calculated height of the container body to adjust the laser height of the unmanned forklift; According to the calculated deviation of the truck docking and whether the outermost row of the cargo box is in the middle or two rows are on both sides, send the corresponding target trajectory and second target position before entering the cargo box to the unmanned forklift; The unmanned forklift completes the path following action according to the trajectory sent by the dispatching device and reaches the specified second target position in front of the cargo box; According to the first target position inside the carriage sent by the dispatching device, the unmanned forklift starts to enter the cargo box and starts real-time detection of the loading pallet, and keeps walking into the cargo box; Determine whether a pallet is detected; In the case of detecting a pallet, obtain the pose of the detected pallet in the camera coordinate system, calculate the angle and lateral deviation between the forklift and the pallet, and complete path planning according to the deviation to correct the deviation and fork the pallet; After the pallet is forked, exit the cargo box and navigate to the specified unloading point; If there are still pallets not unloaded in the carriage, continue to repeat the above process.

[0122] The automatic loading and unloading method provided in this embodiment can automatically complete automatic loading and unloading after the truck docks at the platform, and can adapt to cargo containers with a variety of different lengths, widths, heights, and different internal environments. It can enable intelligent unmanned forklifts to execute tasks safely and orderly, effectively improve the loading and unloading efficiency, reduce or even eliminate the need for factory employees to participate, achieve one-key automatic loading and unloading, and achieve a lower cargo damage rate and a higher intelligent management level.

[0123] In this embodiment, an automatic loading and unloading system is also provided. This system is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function.

[0124] This embodiment provides an automatic loading and unloading system, which is applicable to the automatic loading and unloading method in any of the above-mentioned embodiments. This system is as Figure 4 shown and includes:

[0125] A lifting pan-tilt device, which includes a lifting device and a first lidar device. The first lidar device is suspended above the platform through the lifting device, and the lifting device drives the lidar device to move up and down to detect the point cloud data inside the cargo container through the first lidar device. The first lidar device can be a multi-line lidar, and the lifting device can be a set of automatic lifting mechanical structures.

[0126] An unmanned forklift, including a forklift body and a forklift control module.

[0127] The background scheduling device, also known as the RCS (robot control system) scheduling module, is communicatively connected to the lifting pan-tilt device and the forklift control module. The background scheduling device is also communicatively connected to the platform docking device. The background scheduling device is used to execute the automatic loading and unloading method in any of the above embodiments.

[0128] The background scheduling device can assist in determining the length, width, and height of the cargo container, determining the deviation of the truck docking, the flatness of the bottom surface of the cargo container, and foreign object detection.

[0129] The functions of the driverless forklift mainly include: global real-time positioning, vision module, real-time path following. Global real-time positioning means that the intelligent driverless forklift can accurately locate its own pose in the pre-built map in the entire operation area. In this way, the background scheduling device can obtain the real-time pose of each forklift in real time, and can manage the scheduling according to the pose of each forklift, which can avoid collisions between different forklifts; the vision module mainly includes pallet recognition and obstacle recognition. Pallet recognition is mainly used in two links. One is when the driverless forklift forks up the goods in the temporary storage area and puts them into the cargo container. Because the placement of the goods in the temporary storage area has errors, whether it is manually placed or placed by the intelligent forklift, there are certain errors. Therefore, before forking, the pallet recognition camera will first recognize the pallet in front to determine the relative lateral shift and angular deviation between the pallet and the forklift. If the deviation is small, it can be directly forked. If there is a certain deviation, then the driverless forklift will generate a fixed trajectory and drive along this trajectory to fork the goods so that the deviation after forking is small. Otherwise, if the deviation is large, when putting the goods into the cargo container, it may hit the wall of the cargo container or cause the problem of stacked pallets. The second link that needs to use is when picking up goods from the cargo container. The forklift walks into the cargo container until the camera recognizes the pallet. At this time, it will judge the relative pose relationship between the pallet and the forklift to determine the forking method. If the deviation is small and the deviation correction distance is sufficient, then it can be directly forked. If the angle or lateral shift distance is larger and the deviation correction distance is insufficient, then first adjust the direction of the forklift, move forward to make the lateral shift distance smaller, then adjust the direction in place again, and then directly fork the goods. Real-time path following mainly follows the trajectory route issued by the background scheduling device. This route is a route pre-set according to the actual environment, and can complete the automatic loading and unloading task with a shorter distance and a shorter time.

[0130] This embodiment can dynamically detect the pallet pose and complete the deviation correction forking, so that the deviation of the pallet on the fork is small, ensuring the accuracy of the final placement of the goods during loading and unloading.

[0131] Refer to Figure 5As shown, in some alternative embodiments, the background scheduling device includes:

[0132] A point cloud data acquisition module 601, configured to acquire a point cloud data set inside a cargo container collected by a first lidar device during its descent. The lidar device is suspended above the platform by a lifting device, and the lifting device drives the lidar device to move up and down;

[0133] A box dimension determination module 602, configured to determine the box dimensions of the cargo container based on the point cloud data set. The box dimensions include the box length and the box width;

[0134] A pallet dimension acquisition module 603, configured to acquire the dimensions of a pallet for placing goods;

[0135] A first target position determination module 604, configured to determine a first target position of the driverless forklift based on the box dimensions and the pallet dimensions. The first target position is the position where the driverless forklift is located when loading or unloading goods in the cargo container;

[0136] An instruction sending module 605, configured to send the first target position to the driverless forklift. When the driverless forklift runs to the first target position, it performs the loading or unloading task.

[0137] The background scheduling device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0138] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0139] The embodiment of the present invention further provides a computer device having the above-mentioned Figure 5 shown background scheduling device.

[0140] Please refer to Figure 6 , Figure 6 is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 6 In [the figure], a processor 10 is taken as an example.

[0141] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0142] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0143] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0145] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0146] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0147] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An automatic loading and unloading method, characterized in that, The method includes: Obtaining a point cloud data set inside a cargo container collected by a first lidar device during its descent. The lidar device is suspended above the platform by a lifting device, and the lifting device drives the lidar device to move up and down; Based on the point cloud data set, determining the box size of the cargo container, where the box size includes the box length and the box width; Obtaining the size of a pallet for placing goods; Based on the box size and the pallet size, determining a first target position of the driverless forklift, where the first target position is the position where the driverless forklift is located when loading or unloading goods in the cargo container; Sending the first target position to the driverless forklift. When the driverless forklift runs to the first target position, it performs the loading or unloading task; The method further includes: Based on the point cloud data set, determining the vertical distance and the offset angle between the first lidar device and one side inner wall of the box; Based on the vertical distance and the offset angle, determining the docking deviation of the cargo container; Determining the loading arrangement or the unloading arrangement of the cargo container; Based on the docking deviation, the loading arrangement or the unloading arrangement, determining a second target position for the driverless forklift to enter the cargo container; Sending the second target position to the driverless forklift, and the driverless forklift enters the cargo container from the second target position.

2. The method according to claim 1, wherein It further includes: Obtaining the descent height of the first lidar device, where the descent height is the descent height of the first lidar device when it first collects the point cloud data set; Obtaining the length of the docking plate and the angle between the docking plate and the ground when the platform docking device docks with the cargo container; Based on the descent height, the length of the docking plate, and the angle, determining the box height of the cargo container; According to the box height, adjusting the height of a second lidar device on the driverless forklift, where the second lidar device is used to scan object data in front of the running driverless forklift.

3. The method according to claim 1, characterized in that When the driverless forklift performs the loading task, the determining the first target position of the driverless forklift based on the box size and the pallet size includes: Determining the loading arrangement of the cargo container; Determining the current goods placement serial number of the driverless forklift; Based on the loading arrangement, the current goods placement serial number, the box size, and the pallet size, determining the first target position of the driverless forklift.

4. The method according to claim 1, characterized in that It further includes: Based on the point cloud data set, determining the box bottom points and the non-box bottom points of the box; Based on the box bottom points, determining the bottom flatness of the box; Based on the bottom flatness, determining the running speed of the driverless forklift inside the cargo container; Based on the non-box bottom points of the box, determining whether there are foreign objects inside the cargo container; In the case of the existence of foreign objects, sending out an alarm prompt.

5. The method according to claim 1, wherein It further includes: Judging whether there is a situation where two or more of the driverless forklifts pass through the same first target position simultaneously; In the case where multiple unmanned forklifts pass through the same first target position simultaneously, path lock the unmanned forklifts within a preset area and issue a stop instruction.

6. An automatic loading and unloading system, characterized in that, The system includes: A lifting pan-tilt device, which includes a lifting device and a first lidar device. The first lidar device is suspended above the platform through the lifting device, and the lifting device drives the lidar device to move up and down to detect the point cloud data inside the cargo container through the first lidar device; An unmanned forklift, including a forklift body and a forklift control module; A background scheduling device, which is communicatively connected to the lifting pan-tilt device and the forklift control module. The background scheduling device is also communicatively connected to a platform docking device, and the background scheduling device is used to execute the automatic loading and unloading method according to any one of claims 1-5.

7. The system according to claim 6, wherein The background scheduling device includes: A point cloud data acquisition module, which is used to acquire the point cloud data set inside the cargo container collected by the first lidar device during the descending process. Among them, the lidar device is suspended above the platform through a lifting device, and the lifting device drives the lidar device to move up and down; A box size determination module, which is used to determine the box size of the cargo container based on the point cloud data set. The box size includes the box length and the box width; A pallet size acquisition module, which is used to acquire the size of the pallet for placing goods; A first target position determination module, which is used to determine the first target position of the unmanned forklift based on the box size and the pallet size. The first target position is the position where the unmanned forklift is located when loading or unloading goods in the cargo container; An instruction sending module, which is used to send the first target position to the unmanned forklift. When the unmanned forklift runs to the first target position, it executes the loading or unloading task.

8. A computer device, characterized in that, It includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the automatic loading and unloading method according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the automatic loading and unloading method according to any one of claims 1-5.

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