Method for encasement of tray-borne goods into container
By using mobile tracks and lidar during the packing process to identify container defective parts and adjust the cargo position, the problem of collision between cargo and defective parts in the prior art is solved, and a safer and more efficient packing process is achieved.
Patent Information
- Application Number
- CN202411638416.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-17
- Publication Date
- 2025-06-24
AI Technical Summary
The prior art is difficult to effectively identify and avoid defective parts of the container during the packing process, which leads to easy collision with defective parts when loading, resulting in improper packing and container damage.
Through the combination of moving tracks and lidar, the inside of the container is scanned and defective parts are identified, the cargo is placed to avoid direct contact with the defective parts, and the cargo position of the previous row of goods is adjusted before the defective parts to ensure that the goods will not collide with the defective parts during the cargoing process.
It realizes the accurate identification of container defect parts during the packing process, and avoids direct contact between goods and defect parts by adjusting the placement position, reduces the risk of collision during the packing process and protects the integrity of the container.
Smart Images
Figure CN120191870A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent loading equipment, and particularly to a method for loading pallet-bearing goods into a container. Background Art
[0002] For container stuffing, a small rotary forklift is usually used to enter the interior of the container. The forklift obtains goods at the position of the rear door of the container, and then after turning around inside the container, stacks the goods in the container. Such a rotary forklift usually requires manual operation. For the process of loading and stacking goods, it generally mainly relies on the visual observation of the human eye. However, the light in the container is very poor, and lighting equipment needs to be used for illumination. Still, the multi-point light sources of the lighting equipment will still produce shadows, and it is easy for operators to make misjudgments.
[0003] At the same time, due to the limited turning space inside the container, operators need to be extremely careful when operating the rotary forklift to avoid collision damage to the inner wall of the container. Every year, a considerable number of containers are damaged due to the stuffing operation of the rotary forklift.
[0004] The applicant has previously applied for patents related to loading machines. However, during the stuffing process, the system default box structure is still used. But for some damaged box structures, it is impossible to perceive in advance, so deviations are still inevitable during the stuffing process. Summary of the Invention
[0005] In order to solve the problems existing in the prior art, the present invention provides a method for loading pallet-bearing goods into a container, which solves the problem of intelligent loading of pallets carrying steel drums in the container, especially the problem of avoiding loading at the defective parts of the container.
[0006] To achieve the above object, the following technical solutions are provided:
[0007] A method for loading pallet-bearing goods into a container, comprising:
[0008] Step A: The moving track enters the container through the rear door of the container;
[0009] Step B: Detect the distance between the two side walls of the container through the distance sensors on both sides of the front end of the moving track to determine the center line of the container bottom plate;
[0010] Step C: The moving track advances into the container along the center line of the container bottom plate; at the same time, the rail forklift at the front end of the moving track turns on the lidar and performs circumferential scanning through a plane perpendicular to the center line of the container bottom plate to obtain the polar coordinate data of each scanning cycle during the forward movement of the rail forklift;
[0011] Step D: Model based on the scanning results, identify the defective part X in the model, determine whether there are other defects in the cross-section corresponding to the defective part X, estimate the concave depth of all defective parts, and determine that the sum of the concave depths of this cross-section is less than the lateral loading allowance D, where D is the sum of the clearances between the goods and the two side panels of the container after filling a row of goods laterally;
[0012] Step E: Coordinately adjust the stacking positions Hm of a row of goods corresponding to the defective part and the stacking position Hm-1 of the row of goods before the defective part, so that the two rows of goods are laterally shifted away from the defective part to obtain new stacking positions Hm’ and Hm-1’ respectively;
[0013] Step F: The rail forklift stacks the goods in the order of the new stacking positions H1, H2……Hm-1’, Hm’……Hn.
[0014] When multiple rows of goods are stacked vertically in the lateral cross-section area of a stacking position, compare and judge the sum of the concave depths of the defective parts on both sides of each row of goods with the lateral loading allowance D.
[0015] In step D, identifying the defective part X includes:
[0016] D10: Obtain the polar coordinate data collected by the lidar;
[0017] D20: Filter the collected polar coordinate data and then convert it into rectangular coordinate data;
[0018] D30: Select four extreme value coordinates from the filtered polar coordinate data according to the algorithm, determine them as the coordinates of the four top corner points of the container, determine the four box walls according to the principle of pairwise proximity of the four extreme value coordinates, use two box walls with slopes tending to be vertical as the side walls, and then determine the data collection points corresponding to each side wall and their corresponding rectangular coordinate data, and determine the straight line equation of the side wall according to the two top corner points of each side wall;
[0019] D40: Mark the rectangular coordinate data converted in step D20: Compare the rectangular coordinate data with the corresponding straight line equation. When the distance between a continuous segment of coordinate data in the rectangular coordinate data and the straight line equation is greater than the threshold, it is judged as an abnormal point, otherwise it is judged as a normal point;
[0020] D50: Model and display the rectangular coordinate data marked in step D40 according to normal points and abnormal points, and calibrate the abnormal point area and the normal point area with different colors. Among them, the abnormal point area is the defective part of the box wall, and at the same time mark the range of each defective part and the maximum value of the concavity. This maximum value is the concave depth of the defective part.
[0021] In step D50, the maximum concave value is the distance between the rectangular coordinate data of all the data in the concave area and the corresponding straight-line equation, then the absolute value is taken, the maximum value among them is selected, and the rectangular coordinate data corresponding to the maximum value is determined.
[0022] Although it is possible to have the case of the outer convexity of the box wall, considering that in actual use, the container is almost always stressed externally and deformed inwardly, so in fact, the case of outward convexity basically does not exist, and in the implementation process, only the way of taking the absolute value is needed.
[0023] The x-axis of the rectangular coordinate system is the horizontal axis and is perpendicular to the side plate of the container, the y-axis is the vertical axis, and the z-axis is the horizontal axis and is parallel to the side plate of the container.
[0024] In step C, the coordinates of the scanned defect points are converted from polar coordinate values (r kl , θ kl ) to rectangular coordinates (x kl , y kl ). When the rectangular coordinates (x kl , y kl ) coincide with the coordinates of the side plate of the container scanned by the distance sensor, the corresponding z-axis coordinate z kl is recorded;
[0025] When the distance sensor in step B advances to the coordinate z kl , the x-axis coordinates x kl of the point with the coordinate z kn are compared with x kl :
[0026] When the comparison value |(x kn- - x kl ) / x kl | < 0.05, the defect point is confirmed, and when calculating the midline of the container bottom plate, this defect point is ignored;
[0027] When the comparison value |(x kn- - x kl ) / x kl | ≥ 0.05, the difference between x kn and x k0 is compared again. If the difference is less than the preset value, it is not recognized as a defect point, and the midline of the container bottom plate is calculated continuously according to x kn , otherwise the defect point is confirmed, and when calculating the midline of the container bottom plate, this defect point is ignored.
[0028] Compared with the prior art, the advantages of the present invention are as follows:
[0029] The method provided by the present invention for packing pallet-bearing goods into a container can not only identify the defective parts of the container before packing, but also adjust the corresponding stacking positions for packing and the stacking positions corresponding to the defective parts at the same time according to the defective parts. In this way, not only can the goods avoid being squeezed against the defective parts during stacking, but also the goods can avoid colliding with the defective parts during the stacking of the previous row of goods in front of the defective parts. Description of the Drawings
[0030] Figure 1 is a schematic structural diagram of a loading machine;
[0031] Figure 2 is a schematic diagram of the scanning imaging of a lidar;
[0032] Figure 3 is a schematic diagram of lidar scanning and modeling;
[0033] Figure 4 is a schematic cross-sectional view of the goods stacked in a conventional container;
[0034] Figure 5 is a schematic cross-sectional view of the goods stacked in a container with a defective part (the schematic diagram of stacking in the normal order conflicts);
[0035] Figure 6 is a schematic cross-sectional view of the goods stacked in a container with a defective part (after adjusting the stacking position);
[0036] Figure 7 is a schematic horizontal view of the goods stacked in a conventional container;
[0037] Figure 8 is a schematic horizontal view of the goods stacked in a container with a defective part (the schematic diagram of stacking in the normal order conflicts);
[0038] Figure 9 is a schematic horizontal view of the goods stacked in a container with a defective part (after adjusting the stacking position);
[0039] Figure 10 is a schematic diagram of a lidar scanning a container in four quadrants with a polar axis.
[0040] In the figure:
[0041] 10 - base; 11 - lifting platform; 20 - fixed track; 30 - conveyor roller path; 40 - mobile crane; 50 - mobile support mechanism; 60 - mobile track; 70 - mobile shuttle car; 80 - rail forklift; 90 - pallet and the steel drums thereon. Detailed Embodiment
[0042] 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. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention usually described and illustrated in the drawings here can be arranged and designed in various different configurations.
[0043] Embodiment
[0044] Combined with Figure 1 As shown, a base station 10 is provided. The height of the base station 10 is flush with the height of the bottom plate of the container truck. A lift table 11 capable of lifting is provided at the front end of the base station 10. The lift table 11 is mainly used to support the universal wheels at the front end of the moving track 60, so that the universal wheels can enter the container by means of the lift table 11 and the turning plate at its front end.
[0045] The fixed track 20 is fixed on the base station 10 and arranged longitudinally along the base station to the front end of the base station. The lift table 11 is located inside the fixed track 20.
[0046] The mobile crane 40 is movably installed on the fixed track; the rear end of the moving track 60 is rollably connected to the inner rail of the fixed track 20 through a moving support mechanism 50, and the conveying roller path 30 is placed at the rear end of the fixed track 20 for receiving goods transported by external equipment; the mobile shuttle car 70 is rollably connected in the moving track and can reciprocate back and forth along the moving track; the mobile crane 40 can lift the goods on the conveying roller path 30 and then place them on the mobile shuttle car 70. The mobile shuttle car 70 transports the goods to the front end along the moving track, and then the rearward moving rail forklift 80 forks up the goods from the rear and stacks them forward.
[0047] Combined with Figures 2 - 9 As shown, an embodiment of the present invention provides a method for packing pallet-bearing goods into a container, including:
[0048] Step A: The moving track enters the container through the rear door of the container.
[0049] Step B: Detect the distance between the two side walls of the container through the distance sensors on both sides at the front end of the moving track to determine the center line of the container bottom plate.
[0050] Step C: The moving track advances towards the interior of the container along the midline of the container floor; meanwhile, the rail-mounted forklift at the front end of the moving track turns on the lidar and performs circumferential scanning through a plane perpendicular to the midline of the container floor to obtain the polar coordinate data for each scanning cycle during the advancement of the rail-mounted forklift. The lidar is usually located at the centerline of the entire container, that is, when the moving track advances along the midline, the height of the lidar is at half of the container height. As the rail-mounted forklift advances, the lidar rotates and scans. Since the distance the rail-mounted forklift advances when the lidar rotates one week is relatively short, it can be considered that the z-axis coordinates are the same when the lidar rotates one week, that is, the z-axis coordinates within the same acquisition cycle are the same. Then there is only a set of two data, the polar axis radius and the polar axis angle.
[0051] Step D: Model based on the scanning results, identify the defective part X in the model, determine whether there are other defects in the cross-section corresponding to the defective part X, estimate the concave depth of all defective parts, and determine that the sum of the concave depths of this cross-section is less than the lateral loading allowance D. D is the sum of the gaps between the goods and the two side plates of the container after a row of goods is fully loaded laterally (see Figure 4 shown).
[0052] Step E: Refer to Figure 8 , and perform collaborative adjustment on the stacking positions Hm of a row of goods corresponding to the defective part and the stacking position Hm-1 of the row of goods before the defective part, so that the two rows of goods are laterally shifted away from the defective part to obtain the new stacking positions Hm’ and Hm-1’ as shown in Figure 9 respectively; in the technical solution of the present invention, it is creatively found that during the loading process using a rail-mounted forklift, the defective part X not only affects the stacking position of the entire row of goods corresponding to itself (such as Figure 8 ), but also has an impact on the stacking of the row of goods in front of the defective part X during the process of using the rail-mounted forklift to lift and stack the goods. Since the stacking process is to move the goods from the back to the front as shown in Figure 7 , the row of goods in front of the defective part X will collide and rub with the defective part X when the goods on the rail-mounted forklift are lifted and stacked due to the existence of the defective part X, resulting in the stacking being unable to proceed. Therefore, in the technical solution of the present invention, it is proposed to also perform collaborative adjustment on the stacking position of the row of goods in front, and perform collaborative adjustment on the stacking positions of the two rows of goods. As shown in Figure 9 , the problem of collision between the goods and the defective part X during the stacking process can be avoided;
[0053] Step F: The rail-mounted forklift stacks the goods in the order of the new stacking positions H1, H2... Hm-1’, Hm’... Hn.
[0054] When stacking multiple rows of goods vertically within the horizontal cross-sectional area of a stacking position, compare and judge the sum of the concave depths of the defective parts on both sides of each row of goods with the horizontal loading allowance D.
[0055] In step D, identify the defective part X, including:
[0056] D10. Obtain the polar coordinate data collected by the lidar, where the polar coordinate data is centered on the lidar;
[0057] D20. Filter the collected polar coordinate data (the filtering method can use conventional filtering methods), and then convert it into Cartesian coordinate data; that is, convert the polar coordinates (r kl , θ kl , z kl ) into Cartesian coordinates (x kl , y kl , z kl );
[0058] D30. Select four extreme value coordinates from the filtered polar coordinate data according to the algorithm, determine them as the coordinates of the four top corner points of the container, and determine the four box walls according to the principle that the four extreme value coordinates are pairwise close. Then, take two box walls with slopes tending to be vertical as the side walls, and further determine the corresponding data acquisition points and their corresponding Cartesian coordinate data for each side wall, and determine the straight line equation of the side wall according to the two top corner points of each side wall; the specific algorithm is, as Figure 10 shown, with the lidar as the center, form four quadrants on the x-axis and y-axis. Take a point with the largest polar axis radius in each quadrant as the top corner point of the container. Construct the straight line equation from the Cartesian coordinates corresponding to the extreme value coordinates in adjacent two quadrants, and then obtain the set of coordinate values corresponding to each side of the container;
[0059] D40. Mark the Cartesian coordinate data after conversion in step D20: Compare the Cartesian coordinate data with the corresponding straight line equation. When the distance between a continuous segment of coordinate data in the Cartesian coordinate data and the straight line equation is greater than the threshold, it is judged as an abnormal point, otherwise it is judged as a normal point;
[0060] D50. Model and display the Cartesian coordinate data marked in step D40 according to normal points and abnormal points, and calibrate the abnormal point area and the normal point area with different colors. Among them, the abnormal point area is the defective part of the box wall. At the same time, mark the range of each defective part and the maximum concave value, and this maximum value is the concave depth of the defective part.
[0061] In step D50, the maximum concave value is calculated as the distance between the Cartesian coordinate data of all the data in the concave area and the corresponding straight line equation, then take the absolute value, and select the maximum value among them, and determine the Cartesian coordinate data corresponding to this maximum value.
[0062] Although it is possible for the outer wall of the box to bulge, considering that in actual use, the container is almost always stressed externally and deformed inwardly, so in fact, the situation of outward bulging basically does not exist. During the implementation process, only the absolute value method needs to be selected.
[0063] The x-axis of the rectangular coordinate system is the horizontal axis and is perpendicular to the side plate of the container, the y-axis is the vertical axis, and the z-axis is the horizontal axis and is parallel to the side plate of the container.
[0064] In step C, the coordinates of the detected defect points are converted from polar coordinate values (r kl , θ kl ) to rectangular coordinates (x kl , y kl ). When the rectangular coordinates (x kl , y kl ) coincide with the coordinates of the side plate of the container scanned by the distance sensor, the corresponding z-axis coordinate z kl is recorded;
[0065] When the distance sensor in step B advances to the coordinate z kl , the x-axis coordinate x kl of the point with the coordinate z kn is compared with x kl :
[0066] When the comparison value |(x kn - x kl ) / x kl | < 0.05, the defect point is confirmed, and when calculating the midline of the container bottom plate, this defect point is ignored;
[0067] When the comparison value |(x kn - x kl ) / x kl | ≥ 0.05, the difference between x kn and x k0 is compared again. If the difference is less than the preset value, it is not recognized as a defect point, and the midline of the container bottom plate is calculated continuously according to x kn , otherwise the defect point is confirmed, and when calculating the midline of the container bottom plate, this defect point is ignored.
[0068] Compared with the prior art, the advantages of the present invention are as follows:
[0069] The method provided by the present invention for packing pallet-bearing goods into a container can not only identify the defective parts of the container before packing, but also adjust the corresponding stacking positions for packing and the stacking positions corresponding to the defective parts simultaneously according to the defective parts. In this way, not only can the goods avoid being squeezed against the defective parts during stacking, but also the goods can avoid colliding with the defective parts during the stacking of the previous row of goods before the defective parts.
[0070] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.
Claims
1. A method for packing pallet-borne goods into a container, characterized in that: include: Step A, the moving track enters the container through the rear door of the container; Step B, detecting the distance between the two side walls of the container by using distance sensors on both sides of the front end of the mobile track to determine the center line of the container bottom plate; Step C, the moving track moves forward along the center line of the container floor toward the inside of the container; at the same time, the rail forklift at the front end of the moving track turns on the laser radar, performs circumferential scanning through a plane perpendicular to the center line of the container floor, and obtains polar coordinate data of each scanning cycle during the moving process of the rail forklift; Step D: Modeling is performed based on the scanning results, and the defective part X in the model is identified, and it is determined whether the section corresponding to the defective part X has other defects, and the concave depths of all defective parts are estimated to determine whether the sum of the concave depths of the section is less than the transverse loading margin D, where D is the sum of the gaps between the cargo and the two side plates of the container after a row of cargo is filled transversely; Step E: coordinately adjust the stacking position Hm of a row of goods corresponding to the defective part and the stacking position Hm-1 of a row of goods before the defective part, so that the two rows of goods are laterally shifted away from the defective part, and obtain new stacking positions Hm' and Hm-1' respectively; Step F: The rail forklift stacks the goods in the order of the new stacking positions H1, H2...Hm-1', Hm'...Hn.
2. The method according to claim 1, characterized in that When multiple rows of goods are stacked up and down in the transverse cross-sectional area of a stacking position, the sum of the concave depths of the defective parts on both sides of each row of goods is compared with the transverse loading margin D.
3. The method according to claim 1, characterized in that In step D, identifying the defective part X includes: D10, obtain polar coordinate data collected by the laser radar; D20, filtering the collected polar coordinate data, and then converting it into rectangular coordinate data; D30. Select four extreme value coordinates in the polar coordinate data after filtering according to the algorithm, and determine them as the coordinates of the four vertex points of the container, and determine the four container walls according to the principle of being close to each other based on the four extreme value coordinates, and take two of the container walls with vertical slopes as side walls, and then determine the data collection points corresponding to each side wall and its corresponding rectangular coordinate data, and determine the straight line equation of the side wall according to the two vertex points of each side wall; D40, marking the rectangular coordinate data converted in step D20: comparing the rectangular coordinate data with the corresponding straight line equation, when the distance between a section of continuous coordinate data in the rectangular coordinate data and the straight line equation is greater than a threshold, it is judged as an abnormal point, otherwise it is judged as a normal point; D50, the rectangular coordinate data marked in step D40 are modeled and displayed according to normal points and abnormal points, and the abnormal point area and the normal point area are calibrated with different colors, where the abnormal point area is the defective part of the box wall, and the range and the maximum value of the concave of each defective part are marked at the same time, and the maximum value is the concave depth of the defective part. In step D50, the concave maximum value is to calculate the distance between the rectangular coordinate data of all data of the concave area and the corresponding straight line equation, then take the absolute value, select the maximum value therein, and determine the rectangular coordinate data corresponding to the maximum value.
4. The method according to claim 1, characterized in that: The x-axis of the rectangular coordinate system is a horizontal axis and is perpendicular to the container side panel, the y-axis is a vertical axis, and the z-axis is a horizontal axis and is parallel to the container side panel.
5. The method according to claim 1, characterized in that In step C, the scanned defect point coordinates are converted from polar coordinates (r kl ,θ kl ) is converted to rectangular coordinates (x kl ,y kl ), the rectangular coordinate (x kl ,y kl ) coincides with the container side panel coordinates scanned by the distance sensor, record the corresponding z-axis coordinate z kl ; The distance sensor in step B advances to coordinate z kl When the coordinate z kl The x-coordinate of the point kn With x kl For comparison: When the comparison value|(x kn- x kl ) / x kl |<0.05, the defect point is confirmed and ignored when calculating the center line of the container bottom plate; When the comparison value|(x kn- x kl ) / x kl |≥0.05, then compare x again kn and x k0 If the difference is less than the preset value, it is not considered a defect point and continues to be kn Calculate the center line of the container bottom plate, otherwise confirm the defect point and ignore the defect point when determining the center line calculation of the container bottom plate.