Tray fork taking method and device, computer device and storage medium

Abstract

This invention relates to the field of cargo loading and unloading technology, and discloses a pallet forklift method, apparatus, computer equipment, and storage medium. The method includes: real-time acquisition of point cloud data of the area where the pallet and the forks on the forklift are located; determining a target adjustment pose for the forks based on the point cloud data; when the forks are adjusted to the target adjustment pose, obtaining the relative height of the front fork hole of the pallet relative to the forks and the pallet's pitch angle based on the point cloud data; obtaining a first height corresponding to the rear fork hole of the pallet based on the pallet's pitch angle; and adjusting the fork height based on the relative height and the first height when the forklift lifts the pallet. By adjusting the fork height based on the relative height of the front fork hole of the pallet relative to the forks and the corresponding height of the rear fork hole when the forklift lifts the pallet, the forks can pass through the front and rear fork holes of the pallet without collision, improving operational efficiency and reducing equipment wear.

Description

Technical Field

[0001] This invention relates to the field of cargo loading and unloading technology, specifically to pallet forklift methods, devices, computer equipment, and storage media. Background Technology

[0002] With the rapid growth in demand for intelligent logistics, forklifts, as core equipment in logistics handling, are becoming increasingly intelligent. Currently, unmanned forklifts only need to detect the position of the pallet fork holes and pick up the pallet according to a fixed fork height to complete the operation.

[0003] However, in real-world scenarios, if the ground has a discontinuous slope, the height of the front and rear fork holes of the pallet relative to the forks will be inconsistent. This can lead to collisions and friction between the forks and the rear fork holes of the pallet during operation, which seriously affects the efficiency of pallet picking. Summary of the Invention

[0004] In view of this, the present invention provides a pallet picking method, apparatus, computer equipment and storage medium to solve the problem of collision and friction between the forks and the rear fork holes of the pallet when performing pallet picking operations on uneven ground in related technologies.

[0005] In a first aspect, the present invention provides a pallet forklift method, the method comprising:

[0006] Real-time acquisition of point cloud data of the area where the pallet and the forks on the forklift are located;

[0007] Based on point cloud data, determine the target adjustment pose of the forks;

[0008] When the fork position is adjusted to the target adjustment position, the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet are obtained according to the point cloud data.

[0009] Based on the pitch angle of the pallet, obtain the first height corresponding to the rear fork hole of the pallet;

[0010] When the forklift is picking up the pallet, the height of the forks is adjusted according to the relative height and the first height.

[0011] Therefore, when a forklift picks up a pallet, the height of the forks is adjusted according to the relative height of the front fork hole of the pallet to the forks and the corresponding height of the rear fork hole. This allows the forks to pass through the front and rear fork holes of the pallet without collision, improving work efficiency, reducing equipment wear, and extending the service life of the equipment.

[0012] In one alternative implementation, when the forklift is picking up a pallet, the height of the forks is adjusted based on the relative height and a first height, including:

[0013] Before the forklift forks pass through the front fork holes of the pallet, adjust the fork height to the second height according to the relative height;

[0014] After the forklift forks pass through the front fork holes of the pallet, the height of the forks is dynamically adjusted based on the second height and the first height.

[0015] Therefore, during pallet picking operations, the fork height is first adjusted to the second height so that the forks can safely enter the front fork holes of the pallet, ensuring that the forks do not rub against the upper and lower edges of the fork holes. Then, the fork height is adjusted during the fork travel process before the forks pass through the rear fork holes, thereby avoiding friction with the front and rear fork holes of the pallet throughout the entire process and solving the problem of collision between the forks and fork holes caused by ground undulations.

[0016] In one optional implementation, the height of the forks is dynamically adjusted based on the second height and the first height, including:

[0017] Based on the second height and the first height, the height difference between the front fork hole and the rear fork hole of the pallet is obtained;

[0018] The height of the forks is dynamically adjusted linearly based on the height difference.

[0019] Therefore, the height difference between the front and rear fork holes of the pallet is obtained based on the height of the upper edge of the rear fork hole (i.e., the first height). During the fork travel, the fork height is linearly adjusted from the second height to the first height to avoid collision and friction between the fork and the rear fork hole of the pallet.

[0020] In one optional implementation, the relative height of the front fork opening of the pallet relative to the forks and the pitch angle of the pallet are obtained based on point cloud data, including:

[0021] Cluster the point cloud data to obtain ground point cloud data, pallet point cloud data, and fork point cloud data;

[0022] The pitch angle of the pallet is obtained based on the ground point cloud data and the pallet point cloud data;

[0023] Based on the pallet point cloud data, the first point cloud coordinates corresponding to the front fork holes of the pallet are obtained;

[0024] Based on the fork point cloud data, obtain the second point cloud coordinates corresponding to the fork;

[0025] Based on the coordinates of the first and second point cloud, the relative height of the front fork hole of the pallet relative to the forks is obtained.

[0026] Therefore, obtaining the relative height of the front fork hole of the pallet relative to the fork based on point cloud data can avoid affecting the accuracy of the relative height when the tip deforms later.

[0027] In one optional implementation, the pitch angle of the pallet is obtained based on ground point cloud data and pallet point cloud data, including:

[0028] Based on the ground point cloud data, determine the first plane in which the ground is located;

[0029] Based on the pallet point cloud data, determine the second plane in which the pallet is located;

[0030] The pitch angle of the pallet relative to the forks is obtained based on the first plane and the second plane.

[0031] Therefore, by analyzing radar and laser point cloud data, the planes on which the ground and the pallet are located can be determined, thereby accurately obtaining the pitch angle of the pallet relative to the forks.

[0032] In one optional implementation, the first point cloud coordinates corresponding to the front fork holes of the pallet are obtained based on the pallet point cloud data, including:

[0033] Based on the pallet point cloud data, obtain the crossbar point cloud data corresponding to the upper edge crossbar of the front fork hole;

[0034] Based on the crossbar point cloud data, the first point cloud coordinates corresponding to the front fork holes of the pallet are obtained.

[0035] Therefore, by analyzing radar laser point cloud data, the coordinates of the first point cloud corresponding to the front fork hole of the pallet can be accurately obtained, reducing measurement errors.

[0036] In one optional implementation, determining the target adjustment pose corresponding to the fork based on the point cloud data includes:

[0037] Based on the point cloud data, determine the pose information of the tray;

[0038] Based on the pallet's position information, determine the target adjustment position of the forks on the forklift.

[0039] Therefore, the fork pose can be initially adjusted by the obtained target adjustment pose, so as to facilitate subsequent detection of the relative height between the pallet and the fork and the pallet pitch angle.

[0040] In a second aspect, the present invention provides a pallet forklift device, the device comprising:

[0041] The acquisition module is used to acquire point cloud data of the area where the pallet and the forks on the forklift are located in real time;

[0042] The first processing module is used to determine the target adjustment pose of the fork based on the point cloud data.

[0043] The second processing module is used to obtain the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet based on the point cloud data when the fork pose is adjusted to the target adjustment pose.

[0044] The third processing module is used to obtain the first height corresponding to the rear fork hole of the pallet based on the pallet's pitch angle;

[0045] The control module is used to adjust the height of the forks based on the relative height and the first height when the forklift is picking up the pallet.

[0046] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the pallet fork lifting method of the first aspect or any corresponding embodiment described above.

[0047] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the pallet fork lifting method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a schematic flowchart of a pallet forklift method according to an embodiment of the present invention;

[0050] Figure 2 This is a flowchart illustrating another pallet forklift method according to an embodiment of the present invention;

[0051] Figure 3 This is a flowchart illustrating another pallet forklift method according to an embodiment of the present invention;

[0052] Figure 4A This is an application scenario diagram of the pallet forklift method according to an embodiment of the present invention;

[0053] Figure 4B This is a flowchart illustrating another pallet forklift method according to an embodiment of the present invention;

[0054] Figure 4C This is a schematic diagram of the secondary detection process in the pallet forklift method according to an embodiment of the present invention;

[0055] Figure 5 This is a structural block diagram of a pallet fork lifting device according to an embodiment of the present invention;

[0056] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Normally, during the pallet picking operation, an unmanned forklift only needs to detect the position of the pallet fork holes and pick up the pallet at a fixed fork height to complete the operation. However, in real-world scenarios, if the ground on which the pallet is located has a discontinuous slope, i.e., the ground is uneven, the height of the front and rear fork holes of the pallet relative to the forks will be inconsistent. In this case, the forks may enter the front fork hole of the pallet without collision, but may collide and rub against the rear fork hole.

[0059] Currently, most unmanned forklift operation scenarios only focus on the lateral and longitudinal errors of the forks during operation. However, when the ground slope is uneven and the height redundancy of the fork holes is small, how to make the forks pass through the front and rear fork holes of the pallet without collision becomes a critical problem that urgently needs to be solved.

[0060] Therefore, this invention provides a pallet picking solution. When a forklift picks up a pallet, the height of the forks is adjusted to avoid collision and friction between the forks and the front and rear fork holes of the pallet, thereby improving the efficiency of pallet picking.

[0061] According to an embodiment of the present invention, a pallet forklift method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0062] This embodiment provides a pallet forklift method, which can be used for in-vehicle computer equipment or electronic equipment, such as in-vehicle computers, vehicle controllers, etc. Figure 1 This is a flowchart of a pallet forklift method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0063] Step S101: Real-time acquisition of point cloud data of the area where the pallet and the forks on the forklift are located.

[0064] Specifically, the area where the pallet and the forks on the forklift are located is a three-dimensional space area. This area can include the ground where the forklift is located, the forks, and the pallet. After the forklift travels to the preset pallet detection preparation point, the point cloud data of this area can be obtained by the LiDAR installed on the vehicle.

[0065] It should be noted that the aforementioned preset pallet detection preparation points can be selected according to the actual scenario, as long as the acquired point cloud data includes the fork tips, the ground where the forklift is located, and the point cloud reflected by the pallet. For example, to ensure that the point cloud data is sufficiently rich, the preset pallet detection preparation point can be 5-10m away from the pallet, but this invention is not limited to this.

[0066] Step S102: Determine the target adjustment pose of the fork based on the point cloud data.

[0067] Specifically, the target adjustment posture of the forks corresponds to the position of the pallet. This target adjustment posture is used to provide guidance for the initial adjustment of the fork posture, thereby accurately picking up the pallet.

[0068] Step S103: When the fork position is adjusted to the target adjustment position, the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet are obtained according to the point cloud data.

[0069] Specifically, the relative height D of the front fork hole of the pallet to the fork refers to the difference between the height of the upper edge of the front fork hole of the pallet and the height of the upper surface of the fork, and the pitch angle of the pallet refers to the slope angle between the ground where the pallet is located and the ground where the forklift is located.

[0070] Step S104: Based on the pitch angle of the pallet, obtain the first height corresponding to the rear fork hole of the pallet.

[0071] Because the ground where the pallet is located is uneven, the LiDAR can only scan the front fork holes of the pallet and cannot scan the rear fork holes. Therefore, the height of the upper edge of the rear fork holes of the pallet can be obtained by the pitch angle of the pallet, thereby providing guidance for adjusting the fork height.

[0072] Step S105: When the forklift is picking up the pallet, the height of the forks is adjusted according to the relative height and the first height.

[0073] Specifically, during operation, the fork height is adjusted based on the difference between the height of the upper edge of the front fork hole of the pallet and the height of the upper surface of the fork, as well as the height of the upper edge of the rear fork hole of the pallet, so that the fork can pass through the front and rear fork holes of the pallet without collision.

[0074] The pallet picking method provided in this embodiment acquires point cloud data of the area where the pallet and the forks on the forklift are located in real time. Based on the acquired point cloud data, the target adjustment posture of the forks is determined to make preliminary adjustments to the posture of the forks. Furthermore, the relative height of the front fork hole of the pallet relative to the forks and the pitch angle of the pallet are obtained according to the point cloud data to obtain the height corresponding to the rear fork hole of the pallet.

[0075] Therefore, when a forklift picks up a pallet, the height of the forks is adjusted according to the relative height of the front fork hole of the pallet to the forks and the corresponding height of the rear fork hole. This allows the forks to pass through the front and rear fork holes of the pallet without collision, improving work efficiency, reducing equipment wear, and extending the service life of the equipment.

[0076] This embodiment provides a pallet forklift method, which can be used for in-vehicle computer equipment or electronic equipment, such as in-vehicle computers, vehicle controllers, etc. Figure 2 This is a flowchart of a pallet forklift method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0077] Step S201: Acquire point cloud data in real time of the area where the pallet and the forks on the forklift are located. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0078] Step S202: Determine the target adjustment pose of the fork based on the point cloud data.

[0079] Specifically, step S202 includes:

[0080] Step S2021: Determine the pose information of the pallet based on the point cloud data.

[0081] Specifically, point cloud data can be input into a preset pallet detection module to obtain the pallet pose information (x, y, yaw) in the global (map) coordinate system output by the pallet detection module.

[0082] Where x represents the horizontal coordinate of the pallet in the global coordinate system, y represents the vertical coordinate of the pallet in the global coordinate system, and yaw represents the yaw angle of the pallet in the global coordinate system.

[0083] It should be noted that the working principle of the preset pallet detection module, which outputs pose information based on point cloud data, can be found in the relevant descriptions of existing technologies, and will not be repeated here.

[0084] Step S2022: Determine the target adjustment pose of the forks on the forklift based on the pallet pose information.

[0085] Specifically, the position of the pallet is determined based on the pallet's pose information (x, y, yaw) in the global (map) coordinate system, so as to make preliminary adjustments to the pose of the forks, thereby accurately picking up the pallet.

[0086] For example, based on the pallet's pose information (x, y, yaw), the fork's posture is adjusted so that the lateral center points of the two forks on the forklift are aligned with the lateral center points of the two front fork holes on the pallet, the forks are perpendicular to the pallet, and the longitudinal distance between the fork tips and the pallet is 5-10 cm. This distance is designed to minimize the longitudinal distance between the forklift and the pallet, allowing for the detection of the height difference and pallet pitch angle before the forks enter the pallet. At this point, the difference in ground undulation between the forklift and the pallet is considered to be minimal. Furthermore, since the physical position of the fork holes on the pallet is fixed, an initial fork height value can be set to make preliminary adjustments to the fork height. It should be noted that the longitudinal distance between the fork tips and the pallet, as well as the initial fork height value, can be selected based on human experience and the actual scenario; this invention is not limited to these limitations.

[0087] Step S203: When the fork pose is adjusted to the target adjustment pose, the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet are obtained based on the point cloud data. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0088] Step S204: Based on the pallet's pitch angle, obtain the first height corresponding to the rear fork hole of the pallet. For details, please refer to [link / reference]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0089] Step S205: When the forklift is picking up the pallet, the height of the forks is adjusted according to the relative height and the first height.

[0090] Specifically, step S205 includes:

[0091] Step S2051: Before the forklift forks pass through the front fork holes of the pallet, adjust the height of the forks to the second height according to the relative height.

[0092] If the unevenness of the ground in the area where pallet forklift operations are performed is within certain limits, then there exists a safe range of forklift height [D1, D2]. As long as the relative height D ∈ [D1, D2], it can be guaranteed that the forks can pass through the front forkholes of the pallet without collision. It should be noted that this range of forklift height [D1, D2] can be selected based on human experience and the specifications of the front forkholes, as long as it ensures that the forks do not collide or rub against each other when passing through the front forkholes.

[0093] Specifically, if the obtained relative height D is not within the preset front hole fork take-off height range [D1,D2], the fork height is adjusted to the second height based on the initial height value of the fork, so that the difference between the height of the upper edge of the front side fork hole of the pallet and the height of the upper surface of the fork is within the front hole fork take-off height range [D1,D2], so as to ensure that the fork can safely pass through the front side fork hole of the pallet.

[0094] Step S2052: After the forklift forks pass through the front fork holes of the pallet, the height of the forks is dynamically adjusted according to the second height and the first height.

[0095] Specifically, during the movement of the forks after they pass through the front fork holes, the fork height is adjusted from the second height to the first height to avoid friction with the rear fork holes of the pallet.

[0096] Therefore, during pallet picking operations, the fork height is first adjusted to the second height so that the forks can safely enter the front fork holes of the pallet, ensuring that the forks do not rub against the upper and lower edges of the fork holes. Then, the fork height is adjusted during the fork travel process before the forks pass through the rear fork holes, thereby avoiding friction with the front and rear fork holes of the pallet throughout the entire process and solving the problem of collision between the forks and fork holes caused by ground undulations.

[0097] In some optional implementations, step S2052 above includes:

[0098] Step a1: Based on the second height and the first height, obtain the height difference between the front fork hole and the rear fork hole of the pallet.

[0099] Step a2: Based on the height difference, the height of the forks is dynamically adjusted linearly.

[0100] Specifically, after the fork height is adjusted to the second height, the height of the fork tip corresponds to the height of the front fork hole of the pallet. Thus, the height difference between the front and rear fork holes of the pallet can be obtained based on the height of the upper edge of the rear fork hole (i.e., the first height). During the fork travel, the fork height is linearly adjusted from the second height to the first height to avoid collision and friction.

[0101] The pallet picking method provided in this embodiment acquires point cloud data of the area where the pallet and the forks on the forklift are located in real time. This allows for the determination of the relative height of the front fork hole of the pallet with respect to the forks, the pallet's pitch angle, and the corresponding height of the rear fork hole. Therefore, when the forklift picks up the pallet, the fork height is adjusted to a second height, ensuring the forks safely enter the front fork hole of the pallet first, preventing friction between the forks and the upper and lower edges of the fork hole. Then, the fork height is adjusted during the fork's movement before it passes through the rear fork hole. This process avoids friction with the front and rear fork holes of the pallet throughout the entire process, improving operational efficiency, reducing equipment wear, and extending the equipment's service life.

[0102] This embodiment provides a pallet fork lifting method, which can be used in the aforementioned mobile terminals, such as mobile phones and tablets. Figure 3 This is a flowchart of a pallet forklift method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0103] Step S301: Acquire point cloud data in real time of the area where the pallet and the forks on the forklift are located. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0104] Step S302: Determine the target adjustment pose of the fork based on the point cloud data. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0105] Step S303: When the fork position is adjusted to the target adjustment position, the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet are obtained according to the point cloud data.

[0106] Specifically, step S303 includes:

[0107] Step S3031: Cluster the point cloud data to obtain ground point cloud data, pallet point cloud data, and fork point cloud data.

[0108] Specifically, point cloud data can be extracted from the area where the forks and pallet are located. This point cloud data can then be clustered to remove noisy point clouds other than those belonging to the forks, thus accurately extracting the point cloud data corresponding to the tips of the left and right forks on the forklift—that is, the fork point cloud data. Similarly, point cloud data within this area can be clustered to obtain ground point cloud data and pallet point cloud data, which will not be elaborated upon here.

[0109] Step S3032: Obtain the pitch angle of the pallet based on the ground point cloud data and the pallet point cloud data.

[0110] In some optional implementations, step S3032 above includes:

[0111] Step b1: Determine the first plane where the ground is located based on the ground point cloud data.

[0112] Step b2: Determine the second plane where the pallet is located based on the pallet point cloud data.

[0113] Step b3: Based on the first plane and the second plane, obtain the pitch angle of the pallet relative to the forks.

[0114] Specifically, based on ground point cloud data, the first plane equation of the forklift's location (i.e., the first plane) can be fitted using existing point cloud plane fitting algorithms, and the second plane equation of the pallet's location can be fitted using pallet point cloud data, thereby obtaining the slope angle (i.e., the pallet's pitch angle relative to the forks) between the pallet's location plane and the forklift's location plane.

[0115] Therefore, by analyzing radar and laser point cloud data, the planes on which the ground and the pallet are located can be determined, thereby accurately obtaining the pitch angle of the pallet relative to the forks.

[0116] Step S3033: Based on the pallet point cloud data, obtain the first point cloud coordinates corresponding to the front fork holes of the pallet.

[0117] In some optional implementations, step S3033 above includes:

[0118] Step c1: Based on the pallet point cloud data, obtain the crossbar point cloud data corresponding to the upper edge crossbar of the front fork hole.

[0119] Step c2: Based on the crossbar point cloud data, obtain the first point cloud coordinates corresponding to the front fork holes of the pallet.

[0120] Specifically, after determining the first plane where the ground is located, the point cloud containing the crossbar along the upper edge of the front fork hole is extracted from the pallet point cloud data within a preset height range above the ground. The extracted point cloud is further clustered, and noise points are removed. The crossbar point cloud data is then finely segmented to obtain the first point cloud coordinates of the upper edge of the two front fork holes on the pallet in the radar coordinate system.

[0121] Step S3034: Obtain the second point cloud coordinates corresponding to the fork based on the fork point cloud data.

[0122] Specifically, based on the point cloud data corresponding to the tips of the left and right forks on the forklift, the second point cloud coordinates of the tips of the two forks on the forklift in the radar coordinate system are obtained.

[0123] Therefore, obtaining the actual height of the fork tip based on point cloud data, rather than calibrating the height of the fork tip in the radar coordinate system, can avoid affecting the accuracy of the relative height when the tip deforms later.

[0124] Step S3035: Based on the first point cloud coordinates and the second point cloud coordinates, obtain the relative height of the front fork hole of the pallet relative to the fork.

[0125] Specifically, the difference between the coordinates of the first point cloud and the coordinates of the second point cloud is calculated to obtain the relative height of the front fork hole of the pallet relative to the fork.

[0126] Therefore, obtaining the relative height of the front fork hole of the pallet relative to the fork based on point cloud data can avoid affecting the accuracy of the relative height when the tip deforms later.

[0127] Step S304: Based on the pallet's pitch angle, obtain the first height corresponding to the rear fork hole of the pallet. For details, please refer to [link / reference]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0128] Step S305: When the forklift is picking up the pallet, adjust the height of the forks according to the relative height and the first height. For details, please refer to [link to details]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0129] The pallet picking method provided in this embodiment acquires point cloud data of the area where the pallet and the forks on the forklift are located in real time. This allows for precise determination of the relative height of the front fork holes of the pallet relative to the forks, the pallet's pitch angle, and the height of the rear fork holes, avoiding the impact of later tip deformation on measurement accuracy. Consequently, the fork height is precisely adjusted during fork travel, preventing friction with the front and rear fork holes of the pallet throughout the process.

[0130] The pallet forklift method provided by the present invention will be described in detail below with reference to a specific embodiment. Figure 4A This is an application scenario diagram of the pallet forklift method provided according to an embodiment of the present invention, such as... Figure 4A As shown, when the ground has a discontinuous slope, there are height differences between the front fork opening of the pallet and the forklift, and between the front and rear fork openings of the pallet. Figure 4B As shown, the pallet forklift method specifically includes the following steps:

[0131] 1. When the forklift travels to the pallet detection preparation point, it performs the first pallet pose detection: After the forklift travels to the preset pallet detection preparation point, it sends a command to the pallet detection module. The pallet detection module outputs the pallet pose (x, y, yaw) in the global (map) coordinate system. This means that the forklift can know the location of the pallet at this time, thereby adjusting its own pose and accurately picking up the pallet.

[0132] 2. Adjust the fork height to the preset value: Since the position of the fork holes on the pallet is actually fixed, it is only because of the undulation of the ground that the front and rear fork holes are not on the same horizontal plane, with one fork hole higher than the other. Therefore, the preset fork height can be set according to the physical fork hole height of the pallet.

[0133] 3. Adjust the forklift's position and move it until the fork tips are 5-10cm in front of the pallet's front fork holes: Based on the pallet position (x, y, yaw) output from the first pallet detection, the forklift can be precisely adjusted to ensure that the lateral center points of the two forks are aligned with the lateral center points of the pallet's front fork holes, the forks are perpendicular to the pallet, but the longitudinal distance between the fork tips and the pallet is approximately 5-10cm. This distance is to minimize the longitudinal distance between the forklift and the pallet. Before the forks enter the pallet, the height difference between the two and the pallet's pitch angle are detected. At this point, the difference in the undulation of the ground on both sides is considered to be minimal.

[0134] 4. Secondary detection of the relative height between the upper edge of the front fork hole and the upper surface of the fork: This secondary detection is relative to the first detection. The first detection obtains the pallet's pose (x, y, yaw), while this detection obtains the pallet's pitch angle and relative height. The relative height can be obtained by calculating the difference in Z-coordinate between the upper surface of the fork and the upper edge of the front fork hole of the pallet in the radar coordinate system.

[0135] This process involves the precise extraction of these two parts of the point cloud, noise filtering, such as... Figure 4C As shown, point clouds of the forks within a candidate region can be extracted, clustered, and noisy point clouds (excluding forks) can be removed. This allows for the accurate extraction of point cloud data corresponding to the tips of the left and right forks on the forklift, and the calculation of the Z-coordinates of the two fork tips in the radar coordinate system. See again. Figure 4C As shown, ground point clouds are accurately extracted, and the planar equation of the ground is fitted. Within a preset height range above the ground, a pre-selected point cloud containing the crossbars along the upper edges of the fork openings is extracted. Through clustering and noise point removal, the crossbar point cloud is finely segmented, thereby calculating the Z-coordinates of the point cloud along the upper edges of the two fork openings. Furthermore, based on the Z-coordinates of the two fork tips and the upper edges of the two fork openings in the radar coordinate system, the relative heights of the two fork tips from the upper edges of the fork openings are obtained.

[0136] 5. Extract the tray plane to obtain the pitch angle: See again Figure 4A As shown, the plane where the forklift is currently located is taken as the horizontal plane, that is, the plane without undulations. The purpose of calculating the pitch angle is to obtain the angle difference between the plane where the pallet is located and the plane of the forklift, so as to calculate the height of the upper edge of the rear fork hole of the pallet, because the lidar can only illuminate the front fork hole, and the rear fork hole is blocked.

[0137] 6. Calculate the height of the upper edge of the rear fork hole.

[0138] 7. Secondary adjustment of fork height: The secondary adjustment is relative to the initial set height value. First, ensure that the fork safely enters the front hole and that the fork does not rub against the upper and lower edges of the front fork hole. Then, according to the trigonometric function relationship, adjust the fork height during the fork's travel process before it passes through the rear fork hole. This avoids friction with the front and rear fork holes throughout the process and solves the problem of collision between the fork and fork hole caused by ground undulations.

[0139] 8. Adjust the fork height according to the height difference between the upper edges of the front and rear fork holes and the longitudinal distance.

[0140] 9. Forking complete.

[0141] The pallet picking method provided in this embodiment of the invention can detect the height D before the forks enter the fork holes, perform a secondary height adjustment to make D fall within a safe range, and then calculate the height of the upper edge of the rear fork holes based on the pallet's pitch angle. During the picking process, the fork height is linearly and dynamically adjusted according to the safe height range and the height of the upper edge of the rear fork holes to complete the picking.

[0142] This embodiment also provides a pallet forklift device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0143] This embodiment provides a pallet forklift device, such as Figure 5 As shown, it includes:

[0144] The acquisition module 501 is used to acquire point cloud data of the area where the pallet and the forks on the forklift are located in real time.

[0145] The first processing module 502 is used to determine the target adjustment pose of the fork based on the point cloud data.

[0146] The second processing module 503 is used to obtain the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet based on the point cloud data when the fork pose is adjusted to the target adjustment pose.

[0147] The third processing module 504 is used to obtain the first height corresponding to the rear fork hole of the pallet based on the pitch angle of the pallet;

[0148] The control module 505 is used to adjust the height of the forks based on the relative height and the first height when the forklift is picking up the pallet.

[0149] In some alternative implementations, the first processing module 502 includes:

[0150] The first processing unit is used to determine the pose information of the tray based on the point cloud data.

[0151] The second processing unit is used to determine the target adjustment posture of the forks on the forklift based on the pallet's posture information.

[0152] In some alternative implementations, the second processing module 503 includes:

[0153] The third processing unit is used to cluster the point cloud data to obtain ground point cloud data, pallet point cloud data and fork point cloud data;

[0154] The fourth processing unit is used to obtain the pitch angle of the pallet based on the ground point cloud data and the pallet point cloud data;

[0155] The fifth processing unit is used to obtain the first point cloud coordinates corresponding to the front fork holes of the pallet based on the pallet point cloud data.

[0156] The sixth processing unit is used to obtain the second point cloud coordinates corresponding to the fork based on the fork point cloud data;

[0157] The seventh processing unit is used to obtain the relative height of the front fork hole of the pallet relative to the fork based on the first point cloud coordinates and the second point cloud coordinates.

[0158] In some alternative implementations, the fourth processing unit includes:

[0159] The first processing subunit is used to determine the first plane where the ground is located based on the ground point cloud data;

[0160] The second processing subunit is used to determine the second plane where the pallet is located based on the pallet point cloud data;

[0161] The third processing subunit is used to obtain the pitch angle of the pallet relative to the forks based on the first plane and the second plane.

[0162] In some alternative implementations, the fifth processing unit includes:

[0163] The fourth processing subunit is used to obtain the crossbar point cloud data corresponding to the upper edge crossbar of the front fork hole based on the pallet point cloud data.

[0164] The fifth processing subunit is used to obtain the first point cloud coordinates corresponding to the front fork holes of the pallet based on the crossbar point cloud data.

[0165] In some alternative implementations, the third processing module 504 includes:

[0166] The eighth processing unit is used to adjust the height of the forks to a second height based on the relative height before the forks of the forklift pass through the front fork holes of the pallet;

[0167] The ninth processing unit is used to dynamically adjust the height of the forks based on the second height and the first height after the forks of the forklift pass through the front fork holes of the pallet.

[0168] In some optional implementations, the ninth processing unit includes:

[0169] The sixth processing subunit is used to obtain the height difference between the front fork hole and the rear fork hole of the pallet based on the second height and the first height;

[0170] The seventh processing subunit is used to linearly and dynamically adjust the height of the forks based on the height difference.

[0171] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0172] In this embodiment, the pallet fork lifting device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0173] This invention also provides a computer device having the above-described features. Figure 5 The pallet forklift device shown.

[0174] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0175] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0176] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0177] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0178] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0179] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0180] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0181] Although embodiments of the 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 invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for picking up a pallet with a forklift, characterized in that, The method includes: Real-time acquisition of point cloud data of the area where the pallet and the forks on the forklift are located; Based on the point cloud data, determine the target adjustment pose corresponding to the fork; When the fork is adjusted to the target adjustment pose, the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet are obtained according to the point cloud data. Based on the pitch angle of the pallet, the first height corresponding to the rear fork hole of the pallet is obtained; When the forklift picks up the pallet, the height of the forks is adjusted according to the relative height and the first height; The pitch angle of the tray is obtained based on the point cloud data, including: Cluster the point cloud data to obtain ground point cloud data, pallet point cloud data, and fork point cloud data; Based on the ground point cloud data, determine the first plane in which the ground is located; Based on the pallet point cloud data, determine the second plane in which the pallet is located; The pitch angle of the pallet relative to the forks is obtained based on the first plane and the second plane.

2. The pallet forklift method according to claim 1, characterized in that, When the forklift is picking up the pallet, adjusting the height of the forks according to the relative height and the first height includes: Before the forks of the forklift pass through the front fork holes of the pallet, the height of the forks is adjusted to a second height according to the relative height; After the forks of the forklift pass through the front fork holes of the pallet, the height of the forks is dynamically adjusted according to the second height and the first height.

3. The pallet forklift method according to claim 2, characterized in that, The step of dynamically adjusting the height of the forks based on the second height and the first height includes: The height difference between the front fork hole and the rear fork hole of the pallet is obtained based on the second height and the first height; The height of the forks is linearly and dynamically adjusted based on the height difference.

4. The pallet forklift method according to claim 1, characterized in that, The relative height of the front fork opening of the pallet relative to the forks is obtained based on the point cloud data, including: Based on the pallet point cloud data, the first point cloud coordinates corresponding to the front fork holes of the pallet are obtained; Based on the fork point cloud data, the second point cloud coordinates corresponding to the fork are obtained; The relative height of the front fork hole of the pallet with respect to the forks is obtained based on the first point cloud coordinates and the second point cloud coordinates.

5. The pallet forklift method according to claim 4, characterized in that, The step of obtaining the first point cloud coordinates corresponding to the front fork hole of the pallet based on the pallet point cloud data includes: Based on the pallet point cloud data, obtain the crossbar point cloud data corresponding to the upper edge crossbar of the front fork hole; Based on the crossbar point cloud data, the first point cloud coordinates corresponding to the front fork holes of the pallet are obtained.

6. The pallet forklift method according to claim 1, characterized in that, Determining the target adjustment pose corresponding to the fork based on the point cloud data includes: Based on the point cloud data, determine the pose information of the tray; Based on the pallet's pose information, the target adjustment pose corresponding to the forks on the forklift is determined.

7. A pallet forklift device, characterized in that, The device includes: The acquisition module is used to acquire point cloud data of the area where the pallet and the forks on the forklift are located in real time; The first processing module is used to determine the target adjustment pose corresponding to the fork based on the point cloud data; The second processing module is used to obtain the relative height of the front fork hole of the pallet relative to the fork and the pitch angle of the pallet based on the point cloud data when the fork's pose is adjusted to the target adjustment pose. The third processing module is used to obtain the first height corresponding to the rear fork hole of the pallet based on the pitch angle of the pallet; The control module is used to adjust the height of the forks according to the relative height and the first height when the forklift is picking up the pallet; The second processing module is also used for: Cluster the point cloud data to obtain ground point cloud data, pallet point cloud data, and fork point cloud data; Based on the ground point cloud data, determine the first plane in which the ground is located; Based on the pallet point cloud data, determine the second plane in which the pallet is located; The pitch angle of the pallet relative to the forks is obtained based on the first plane and the second plane.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the pallet fork lifting method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the pallet forklift method according to any one of claims 1 to 6.

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