Automatic tray carrying, storing, taking and checking method and device based on unmanned forklift and storage medium

Abstract

The invention discloses an automatic tray carrying, storing, taking and checking method and device based on an unmanned forklift and a storage medium. The automatic tray carrying, storing, taking and checking method comprises the steps that path planning is conducted according to the current position, the target tray position in a warehouse and the target tray position and the target position; after the position of the target tray is reached, the target tray is determined according to the recognized current environment and the posture of the tray; forking the target tray according to the height of the goods shelf; the target tray is detected, and if the target tray is qualified, the target tray is carried to a target position; and after carrying is completed, the trays at the target position are checked. According to the unmanned forklift, carrying, storing and taking are completed in a full-automatic mode, and unmanned operation is achieved; by using the unmanned forklift, the carrying capacity can be improved, and the working efficiency is improved; the unmanned forklift is based on the laser radar, has perfect 3D obstacle avoidance capability, does not crack up a door frame, and has no equipment maintenance cost; the unmanned forklift forks goods on a 9-meter high-position goods shelf in a high-precision mode based on a laser radar and a depth camera, and no safety risk exists. The checking speed is improved, the project progress and the inventory accuracy are powerfully guaranteed, and the method is particularly suitable for warehouse scenes with many elevated warehouses.

Description

Technical Field

[0001] The present invention relates to the field of unmanned forklifts, and in particular to a method, device and storage medium for automatically transporting, storing and counting pallets based on unmanned forklifts. Background Art

[0002] Pallets and forklifts are common logistics equipment in warehouses, playing a vital role in the storage, handling, and transportation of goods. A pallet is a horizontal platform used for the assembly, stacking, handling, and transportation of goods. Flatbed pallets are the most basic form, typically consisting of two layers of planar structures connected by legs or longitudinal beams. Heavy, packaged goods are placed on the pallet, which is then handled and retrieved by floor trolleys or forklifts. Compared to handling and retrieving individual items, this significantly improves warehouse efficiency.

[0003] Forklifts are manually driven and can handle pallet transport (plane movement) and storage and retrieval (loading, unloading, and three-dimensional movement). Forklift operators must undergo training and obtain a forklift license before they can operate a forklift. Tractors are manually pulled and can only handle pallet transport (plane movement). However, there are the following problems:

[0004] 1. Labor costs remain high;

[0005] 2. The ground cattle rely on pure manual operation, the workload of manual handling is large, the manual stacking is highly arbitrary, the efficiency of picking and placing goods is low, and it is prone to errors;

[0006] 3. Manually driven forklifts may damage the door frame, resulting in high repair costs;

[0007] 4. There are risks in forking goods from high shelves. Improper operation of forklift operators can easily lead to serious safety accidents.

[0008] 5. Manual inventory is inefficient and prone to errors.

[0009] Therefore, there is an urgent need to provide a method, device and storage medium for automatic pallet handling, access and inventory based on an unmanned forklift. Summary of the Invention

[0010] In order to solve the above problems, the technical solution of the present invention provides a method, device and storage medium for automatic pallet handling, retrieval and inventory based on an unmanned forklift, which can achieve the problem.

[0011] According to a first embodiment of the technical solution of the present invention, a method for automatic pallet handling, storage and inventory based on an unmanned forklift is provided, which is characterized by comprising:

[0012] S1, plan the path based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location;

[0013] S2. After reaching the target pallet position, determine the target pallet based on the identified current environment and the pallet's posture;

[0014] S3, pick up the target pallet according to the shelf height;

[0015] S4, inspect the target pallet, and if it passes the inspection, move the target pallet to the target location;

[0016] S5. Repeat steps S1 to S4 until the transport is completed, and then take inventory of the pallets at the target location.

[0017] In the above solution, the unmanned forklift is equipped with a laser radar, and multiple reflectors are provided in the warehouse. Step S1 includes:

[0018] S11, determining the current position of the unmanned forklift as the coordinate origin;

[0019] S12, receiving laser information, determining the position of the reflector according to the laser information and determining the relative coordinates of the reflector position;

[0020] S13. A map is constructed based on the coordinate origin and relative coordinates, and the unmanned forklift starts moving;

[0021] S14, generating point cloud data through laser radar and matching it with the map;

[0022] S15, identifying the difference area between the point cloud data and the map and marking it as an obstacle area;

[0023] S16. Update the map by marking the obstacle area.

[0024] In the above solution, the unmanned forklift is equipped with a depth camera, and step S2 includes:

[0025] S21. Acquire image information of the current pallet using the depth camera, and obtain identification information of the pallet based on the image information of the pallet;

[0026] S22, determining the center coordinates of the pallet based on the identification information;

[0027] S23, extracting pallet point cloud data from the point cloud image according to the pallet identification information, and determining the pallet heading angle according to the pallet point cloud data;

[0028] S24. Obtaining pallet position data according to the pallet center point coordinates and the pallet heading angle.

[0029] In the above solution, step S3 includes:

[0030] S31, acquiring image information of the pallet and shelf using the depth camera;

[0031] S32, converting the image information into point cloud information;

[0032] S33. Determine the spatial position information of the pallet and the shelf based on the point cloud information;

[0033] S34. Determine the distance difference between the pallet and the forklift based on the spatial position information, adjust the forklift fork arm, and complete the pickup.

[0034] In the above solution, step S4 includes:

[0035] S41, scanning the pallet in all directions;

[0036] S42, segmenting the threshold according to the type and material of the pallet, identifying the damaged area and marking it;

[0037] S43. Quantify each damaged area and establish a risk warning mechanism based on the quantification results.

[0038] In the above solution, the inventory of the pallets at the target location in step S5 includes:

[0039] S51. Digitally model the entire warehouse, divide it into multiple inventory areas, and number all materials that need to be counted;

[0040] S52. A corresponding RFID tag is provided on each material;

[0041] S53. Scan the RFID tag by an unmanned forklift to perform inventory.

[0042] In the above solution, in step S53, the unmanned forklift performs inventory along the automatically planned path.

[0043] According to a second embodiment of the technical solution of the present invention, there is provided an automatic pallet handling, retrieval and inventory device based on an unmanned forklift, comprising:

[0044] A navigation module is used to plan paths based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location;

[0045] A search module is used to determine the target pallet based on the identified current environment and the posture of the pallet after reaching the target pallet position;

[0046] Access module, used to fork the target pallet according to the shelf height;

[0047] The detection module is used to detect the target pallet and move the target pallet to the target location if it passes the inspection;

[0048] The inventory module is used to inventory the pallets at the target location.

[0049] According to a third aspect of the technical solution of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement any one of the methods described above.

[0050] According to a fourth aspect of the technical solution of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed, the method as described in any one of the above solutions is implemented.

[0051] Beneficial effects of the present invention:

[0052] The present invention discloses a method, device and storage medium for automatic handling, storage and retrieval of pallets based on an unmanned forklift. The unmanned forklift completes the handling and storage and retrieval fully automatically without human operation. The use of an unmanned forklift can increase the handling volume and improve work efficiency. The unmanned forklift is based on a laser radar and has a complete 3D obstacle avoidance capability, will not damage door frames, and has no equipment maintenance costs. The unmanned forklift uses a laser radar and a depth camera to achieve high-precision forking of goods on 9-meter high shelves without safety risks. The inventory counting speed is improved, which effectively guarantees the project progress and inventory accuracy, and is particularly suitable for warehouse scenarios with a large number of high-bay warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0054] Figure 1 This is a flow chart of the method for automatic pallet handling, storage, retrieval and inventory based on an unmanned forklift disclosed in the present invention;

[0055] Figure 2 This is a structural block diagram of the automatic pallet handling, retrieval and inventory device for an unmanned forklift disclosed in the present invention.

[0056] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0058] The terms "first," "second," and the like in the description and claims of the present disclosure are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present disclosure described herein can, for example, be implemented in orders other than those illustrated or described herein.

[0059] In addition, the terms "comprises" and "having" and any variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, product or apparatus.

[0060] Multiple includes two or more.

[0061] It should be understood that the term "and / or" as used in this disclosure simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0062] like Figure 1 As shown, an embodiment of the technical solution of the present invention provides a method for automatically transporting, storing, accessing and counting pallets based on an unmanned forklift, comprising:

[0063] S1, plan the path based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location;

[0064] S2. After reaching the target pallet position, determine the target pallet based on the identified current environment and the pallet's posture;

[0065] S3, pick up the target pallet according to the shelf height;

[0066] S4, inspect the target pallet, and if it passes the inspection, move the target pallet to the target location;

[0067] S5. Repeat steps S1 to S4 until the transport is completed, and then take inventory of the pallets at the target location.

[0068] In a specific embodiment, an unmanned forklift is equipped with a laser radar, and a plurality of reflective panels are provided in the warehouse. Step S1 includes:

[0069] S11, determining the current position of the unmanned forklift as the coordinate origin;

[0070] S12, receiving laser information, determining the position of the reflector according to the laser information and determining the relative coordinates of the reflector position;

[0071] S13. A map is constructed based on the coordinate origin and relative coordinates, and the unmanned forklift starts moving;

[0072] S14, generating point cloud data through laser radar and matching it with the map;

[0073] S15, identifying the difference area between the point cloud data and the map and marking it as an obstacle area;

[0074] S16. Update the map by marking the obstacle area.

[0075] In this embodiment, the coordinate system is initialized after scanning a reflective plate in the warehouse. Laser ranging is used to determine the coordinates of the reflective plate based on the intensity and duration of laser reflection. The lidar continuously scans the environment, including 360° horizontal scanning and ±15° vertical scanning, generating point cloud data at a rate of no less than 300,000 points per second. Furthermore, this embodiment can also be combined with historical navigation data analysis for continuous optimization.

[0076] Since unmanned forklifts can identify and mark obstacle areas through laser radar scanning and have obstacle avoidance functions, they will not damage door frames or hit obstacles, reducing equipment maintenance costs.

[0077] In a specific embodiment, the unmanned forklift is provided with a depth camera, and step S2 includes:

[0078] S21. Acquire image information of the current pallet using the depth camera, and obtain identification information of the pallet based on the image information of the pallet;

[0079] S22, determining the center coordinates of the pallet based on the identification information;

[0080] S23, extracting pallet point cloud data from the point cloud image according to the pallet identification information, and determining the pallet heading angle according to the pallet point cloud data;

[0081] S24. Obtaining pallet position data according to the pallet center point coordinates and the pallet heading angle.

[0082] In this embodiment, a depth camera captures an RGB image of the pallet. After determining the pallet's point cloud data and heading angle, the unmanned forklift's processing unit fuses the pallet's center coordinates with the heading angle to construct a complete description of the pallet's pose. Furthermore, a pallet pose database is established to accumulate prior knowledge for specific scenarios, improving recognition speed and accuracy in similar scenarios. This enables high-precision and robust positioning of various pallet poses, enhancing the universality and robustness of pallet pose detection.

[0083] In a specific embodiment, step S3 includes:

[0084] S31, acquiring image information of the pallet and shelf using the depth camera;

[0085] S32, converting the image information into point cloud information;

[0086] S33. Determine the spatial position information of the pallet and the shelf based on the point cloud information;

[0087] S34. Determine the distance difference between the pallet and the forklift based on the spatial position information, adjust the forklift fork arm, and complete the pickup.

[0088] In this embodiment, a precise coordinate transformation relationship between the camera and forklift is established through a combined calibration method using a high-precision calibration plate and structured light projection. A nonlinear optimization calibration algorithm is used to reduce image errors caused by lens distortion and temperature drift. Key plane and edge features of the shelf and pallet are extracted from point cloud data, and a simplified geometric model is constructed. The RANSAC plane fitting and edge detection algorithms are applied to accurately extract the spatial positions of the shelf front surface and the pallet load surface. The least squares method is used to calculate inter-surface distances, and distance measurement is achieved by combining geometric constraints. Based on the calculated distance difference and incorporating a gantry dynamics model, a feedforward controller is designed to adjust the forklift's posture. Furthermore, the principles of unmanned forklift delivery and retrieval are similar and will not be elaborated on here.

[0089] In a specific embodiment, step S4 includes:

[0090] S41, scanning the pallet in all directions;

[0091] S42, segmenting the threshold according to the type and material of the pallet, identifying the damaged area and marking it;

[0092] S43. Quantify each damaged area and establish a risk warning mechanism based on the quantification results.

[0093] In this example, each damaged area needs to be accurately quantified, including geometric features such as area, perimeter, minimum bounding rectangle, eccentricity, and compactness; directional features such as principal axis direction, directional consistency, and angular distribution; positional features such as the positional relationship relative to the key pallet structures (fork, bearing surface, and support blocks); and depth features. Time-of-flight depth data is used to calculate damage depth, volume, and contour fluctuation. Feature normalization and weighted fusion are then performed to establish a damage feature vector.

[0094] Specifically, based on the damage type, location, size and severity, the pallet comprehensive risk score is calculated including:

[0095] Safety level (0-30 points): can be used normally;

[0096] Warning level (31-60 points): Can be used but it is recommended to be overhauled in the near future;

[0097] Dangerous level (61-80 points): limited use, reduced load requirements;

[0098] Prohibited level (81-100 points): Do not use and must be replaced immediately.

[0099] Furthermore, a damage report can be generated, including visual damage location markings, severity quantification, and treatment recommendations. Based on the inspection results, the forklift can choose to continue handling or replace it.

[0100] In a specific embodiment, the inventorying of the pallets at the target location in step S5 includes:

[0101] S51. Digitally model the entire warehouse, divide it into multiple inventory areas, and number all materials that need to be counted;

[0102] S52. A corresponding RFID tag is provided on each material;

[0103] S53. Scan the RFID tag by an unmanned forklift to perform inventory.

[0104] In this embodiment, the entire warehouse is first digitally modeled, and the three-dimensional space is accurately divided into multiple inventory areas. Each area is assigned a unique identifier; each material is assigned a composite code containing an area code, shelf code, layer code, material type code and a unique serial number; the complete material data (including material code, location coordinates, storage time, batch information, etc.) is encrypted and written into the RFID tag to ensure that each RFID tag faces the warehouse channel, that is, the tag is within the visual range. The unmanned forklift is equipped with an RFID reader and RFID antenna, and performs automatic inventory according to a pre-planned route.

[0105] Furthermore, during inventory taking, a hierarchical tag reading strategy is implemented.

[0106] Specifically, high-level shelves (6-9 meters from the ground) are mainly covered by 45° antennas; middle-level shelves (3-6 meters from the ground) are covered collaboratively by 45° and 90° antennas; low-level shelves (0-3 meters from the ground) are mainly covered by 90° antennas.

[0107] After receiving the inventory data, the algorithm compares it with the inventory, identifies various discrepancies, and automatically analyzes the causes of the discrepancies:

[0108] Missing materials: materials that exist in the system but are not scanned;

[0109] Excess materials: Materials that are scanned but do not exist in the system;

[0110] Position deviation: materials whose actual position does not match the system record;

[0111] Operation error classification: such as misplacement, missed scan and other human factors;

[0112] System error classification: such as data synchronization failure, duplicate records and other system factors;

[0113] Suspected anomaly classification: Situations that may involve missing inventory or unauthorized movement.

[0114] like Figure 2 As shown, according to the second embodiment of the technical solution of the present invention, there is provided an automatic pallet handling, retrieval and inventory device based on an unmanned forklift, comprising:

[0115] A navigation module is used to plan paths based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location;

[0116] A search module is used to determine the target pallet based on the identified current environment and the posture of the pallet after reaching the target pallet position;

[0117] Access module, used to fork the target pallet according to the shelf height;

[0118] The detection module is used to detect the target pallet and move the target pallet to the target location if it passes the inspection;

[0119] The inventory module is used to inventory the pallets at the target location.

[0120] According to a third aspect of the technical solution of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement any one of the methods described above.

[0121] According to a fourth aspect of the technical solution of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed, the method as described in any one of the above solutions is implemented.

[0122] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0123] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that the above implementation method can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0125] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A method for automatic pallet handling, storage and inventory based on an unmanned forklift, characterized in that: include: S1, plan the path based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location; S2. After reaching the target pallet position, determine the target pallet based on the identified current environment and the pallet's posture; S3, pick up the target pallet according to the shelf height; S4, inspect the target pallet, and if it passes the inspection, move the target pallet to the target location; S5. Repeat steps S1 to S4 until the transport is completed, and then take inventory of the pallets at the target location.

2. The method for automatic pallet handling, storage and inventory based on an unmanned forklift according to claim 1 is characterized in that: The unmanned forklift is equipped with a laser radar, and there are multiple reflectors in the warehouse. Step S1 includes: S11, determining the current position of the unmanned forklift as the coordinate origin; S12, receiving laser information, determining the position of the reflector according to the laser information and determining the relative coordinates of the reflector position; S13. A map is constructed based on the coordinate origin and relative coordinates, and the unmanned forklift starts moving; S14, generating point cloud data through laser radar and matching it with the map; S15, identifying the difference area between the point cloud data and the map and marking it as an obstacle area; S16. Update the map by marking the obstacle area.

3. The method for automatic pallet handling, storage and inventory based on an unmanned forklift according to claim 1 is characterized in that: The unmanned forklift is equipped with a depth camera, and step S2 includes: S21. Acquire image information of the current pallet using the depth camera, and obtain identification information of the pallet based on the image information of the pallet; S22, determining the center coordinates of the pallet based on the identification information; S23, extracting pallet point cloud data from the point cloud image according to the pallet identification information, and determining the pallet heading angle according to the pallet point cloud data; S24. Obtaining pallet position data according to the pallet center point coordinates and the pallet heading angle.

4. The method for automatic pallet handling, storage and inventory based on an unmanned forklift according to claim 3 is characterized in that: Step S3 includes: S31, acquiring image information of the pallet and shelf using the depth camera; S32, converting the image information into point cloud information; S33. Determine the spatial position information of the pallet and the shelf based on the point cloud information; S34. Determine the distance difference between the pallet and the forklift based on the spatial position information, adjust the forklift fork arm, and complete the pickup.

5. The method for automatic pallet handling, storage and inventory based on an unmanned forklift according to claim 4 is characterized in that: Step S4 includes: S41, scanning the pallet in all directions; S42, segmenting the threshold according to the type and material of the pallet, identifying the damaged area and marking it; S43. Quantify each damaged area and establish a risk warning mechanism based on the quantification results.

6. The method for automatic pallet handling, storage and inventorying based on an unmanned forklift according to claim 5 is characterized in that: In step S5, the inventory of the pallets at the target location includes: S51. Digitally model the entire warehouse, divide it into multiple inventory areas, and number all materials that need to be counted; S52. A corresponding RFID tag is provided on each material; S53. Scan the RFID tag by an unmanned forklift to perform inventory.

7. The method for automatic pallet handling, storage and inventorying based on an unmanned forklift according to claim 6 is characterized in that: In step S53, the unmanned forklift performs inventory along the automatically planned path.

8. An automatic pallet handling, storage and inventory device based on an unmanned forklift, characterized in that: include: A navigation module is used to plan paths based on the current location and the target pallet location in the warehouse, as well as the target pallet location and the target location; A search module is used to determine the target pallet based on the identified current environment and the posture of the pallet after reaching the target pallet position; Access module, used to fork the target pallet according to the shelf height; The detection module is used to detect the target pallet and move the target pallet to the target location if it passes the inspection; The inventory module is used to inventory the pallets at the target location.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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