A method for multi-specification multi-variety bagged mixed loading

Abstract

This invention discloses a method for loading mixed bagged products of multiple specifications and varieties onto a truck, comprising: Step S1, based on order information, identifying the types and specifications of materials to be loaded, and obtaining their corresponding dimensional information; Step S2, based on 3D radar scanning of the truck compartment data, obtaining the length, width, and height data of the truck compartment; Step S3, according to the material dimensional information, simulating the allocation of the truck compartment space, stacking the materials, and obtaining the stacking height and number of columns for each specification of material; Step S4, using the obtained stacking height and number of columns for each specification of material, iteratively applying the Levenberg-Marquardt algorithm to obtain a solution that satisfies a set minimum error range; Step S5, finally, determining the truck compartment allocation result based on the obtained solution with the set minimum error range, and then calculating the robot's movement path. This invention ensures the consistency of loading height, the stack is stable and not easily collapsed, and disassembly is also more convenient.

Description

Technical Field

[0001] This invention relates to a method for loading mixed bagged products of multiple specifications and varieties onto a vehicle. Background Technology

[0002] For current robotic bagging and loading systems, typical palletizing solutions heavily rely on previous experience with manual loading, simulating manual loading experience by loading in three horizontal rows or horizontal rows sandwiching some vertical rows.

[0003] Therefore, when faced with mixed packaging of multiple specifications, human experience cannot be reused, and mixed packaging of multiple specifications of bags cannot be promoted. Thus, when faced with such mixed packaging operations of multiple specifications, multiple varieties, and different bag types, the robot bag loading may be unable to load the truck.

[0004] Therefore, a method for loading mixed bags of multiple specifications and varieties is provided. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method for loading mixed bagged products of multiple specifications and varieties onto trucks, ensuring consistent loading height, stable stacking that is not prone to collapse, and easier disassembly.

[0006] The technical solution to achieve the above objectives is:

[0007] A method for loading mixed bagged products of multiple specifications and varieties onto a vehicle, comprising:

[0008] Step S1: Based on the order information, identify the types and specifications of materials to be loaded onto the vehicle, and obtain their corresponding size information based on the types and specifications of the materials.

[0009] Step S2: Based on the 3D radar scan data of the truck body to be loaded, obtain the length, width, and height data of the truck body to be loaded;

[0010] Step S3: Based on the material size information, simulate the allocation of the carriage space, stack the materials, and obtain the stacking height and number of columns for each specification of material;

[0011] Step S4: Based on the obtained stacking height and number of columns of each material specification, use the Levenberg-Marquardt algorithm to iterate and obtain a solution that meets the set minimum error range.

[0012] Step S5: Finally, the car allocation result is determined based on the solution with the set minimum error range, and then the robot's movement path is calculated.

[0013] Preferably, in step S3, the materials are stacked according to a set of unequal relationships, and the total length of all package sizes after stacking cannot exceed the total length of the carriage, that is:

[0014] ;

[0015] In the formula, For the train commander, For the first The width of the package for each specification of material. For the first The number of columns for each specification of material. This represents the total quantity of materials by specification. To set the minimum error range.

[0016] Preferably, in step S3, the materials are stacked at approximately the same height, resulting in a set of equations:

[0017] ;

[0018] In the formula, For the first Stacking height of various material specifications For the first The package length of the material of this specification, For the first The bottom area of ​​the material of this specification. For the first The number of packages of materials of a certain specification.

[0019] Preferably, in step S4, the Levenberg-Marquardt algorithm is used iteratively based on the obtained stacking height and number of columns of each material specification to obtain a solution that satisfies the set minimum error range, i.e.:

[0020] If we define the specifications and quantity of the materials Let the number of columns for the first material be... The number of columns for the second type of material is The number of columns for the third type of material is The height of the material is The base area of ​​the first material is The volume of the first material is The base area of ​​the second material is The volume of the second material is The base area of ​​the third material is The volume of the third material is ,get:

[0021] ;

[0022] Define residual vector :

[0023] ;

[0024] in, For the parameter vector:

[0025] ;

[0026] For parameter vectors Minimize the sum of squared residuals:

[0027] ;

[0028] Using the Jacobian matrix to pair the parameter vector Perform partial derivatives:

[0029] ;

[0030] The obtained Jacobian matrix is ​​then iterated using the Levenberg-Marquardt algorithm:

[0031] ;

[0032] In the formula, For the number of iterations, Step size, This is the transpose of the Jacobian matrix. It is a diagonal matrix. The residual vector;

[0033] When the parameter change is small enough and the residual is small enough, the iteration number is reached, that is:

[0034] ;

[0035] In the formula, This is the error value.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention abstracts the mixed packaging of multiple specifications and varieties of bags into a packing problem, and proposes a method of dividing the volume to calculate the space. The height of the stacks with similar loading quantities of each specification will be smoother and there will be no unevenness. The method adds new loading stack types, and the robot bag loading can support more stack types and can be applied to more scenarios and support more business environments. By dividing the volume, the consistency of loading height can be guaranteed, the stack is stable and not easy to collapse, and disassembly is also more convenient. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 This is a flowchart of a method for loading mixed bags of multiple specifications and varieties onto trucks according to the present invention. Detailed Implementation

[0039] 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, and 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.

[0040] like Figure 1 As shown, a method for loading mixed bagged products of multiple specifications and varieties onto a vehicle includes:

[0041] Step S1: Based on the order information, identify the types and specifications of materials to be loaded onto the vehicle, and obtain their corresponding size information based on the types and specifications of the materials.

[0042] Step S2: Based on the 3D radar scan data of the truck body to be loaded, obtain the length, width, and height data of the truck body to be loaded.

[0043] Step S3: Based on the material size information, simulate the allocation of the carriage space, stack the materials, and obtain the stacking height and number of columns for each specification of material.

[0044] In this embodiment, the materials are stacked according to a set of unequal relationships, and the total length of all package sizes stacked cannot exceed the total length of the carriage, that is:

[0045] ;

[0046] In the formula, For the train commander, For the first The width of the package for each specification of material. For the first The number of columns for each specification of material. This represents the total quantity of materials by specification. To set the minimum error range.

[0047] In a specific embodiment, if the material is a mixture of 3 specifications, then:

[0048] .

[0049] In this embodiment, the materials are stacked at approximately the same height, resulting in a system of equations:

[0050] ;

[0051] In the formula, For the first Stacking height of various material specifications For the first The package length of the material of this specification, For the first The bottom area of ​​the material of this specification. For the first The number of packages of materials of a certain specification.

[0052] In a specific embodiment, if the material is a mixture of 3 specifications, then:

[0053] ;

[0054] ;

[0055] .

[0056] Step S4: Based on the obtained stacking height and number of columns of each material specification, use the Levenberg-Marquardt algorithm iteratively to obtain a solution that meets the set minimum error range.

[0057] In this embodiment, the Levenberg-Marquardt algorithm is used iteratively based on the obtained stacking height and number of columns of each material specification to obtain a solution that meets the set minimum error range, i.e.:

[0058] If we define the specifications and quantity of the materials Let the number of columns for the first material be... The number of columns for the second type of material is The number of columns for the third type of material is The height of the material is The base area of ​​the first material is The volume of the first material is The base area of ​​the second material is The volume of the second material is The base area of ​​the third material is The volume of the third material is ,get:

[0059] ;

[0060] Define residual vector :

[0061] ;

[0062] in, For the parameter vector:

[0063] ;

[0064] For parameter vectors Minimize the sum of squared residuals:

[0065] ;

[0066] Using the Jacobian matrix to pair the parameter vector Perform partial derivatives:

[0067] ;

[0068] The obtained Jacobian matrix is ​​then iterated using the Levenberg-Marquardt algorithm:

[0069] ;

[0070] In the formula, For the number of iterations, Step size, This is the transpose of the Jacobian matrix. It is a diagonal matrix. The residual vector;

[0071] When the parameter change is small enough and the residual is small enough, the iteration number is reached, that is:

[0072] ;

[0073] In the formula, This is the error value, a custom value.

[0074] Step S5: Finally, the car allocation result is determined based on the solution with the set minimum error range, and then the robot's movement path is calculated.

[0075] In the embodiment, for cases where the quantity of each specification varies greatly, such as a truck carrying 400 bags, or a specification carrying only 20 or 40 bags, a flat-laying method is adopted, in which the remaining specifications are laid flat on top of the specification, thereby ensuring the consistency of the loading height.

[0076] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for loading mixed bagged products of multiple specifications and varieties onto a vehicle, characterized in that, include: Step S1: Based on the order information, identify the types and specifications of materials to be loaded onto the vehicle, and obtain their corresponding size information based on the types and specifications of the materials. Step S2: Based on the 3D radar scan data of the truck body to be loaded, obtain the length, width, and height data of the truck body to be loaded; Step S3: Based on the material size information, simulate the allocation of the carriage space, stack the materials, and obtain the stacking height and number of columns for each specification of material; Step S4: Based on the obtained stacking height and number of columns of each material specification, use the Levenberg-Marquardt algorithm to iterate and obtain a solution that meets the set minimum error range. Step S5: Finally, the car allocation result is determined based on the solution with the set minimum error range, and then the robot's movement path is calculated.

2. The method for loading mixed bagged products of multiple specifications and varieties onto a vehicle according to claim 1, characterized in that, In step S3, the materials are stacked according to a set of unequal relationships, and the total length of all package sizes after stacking cannot exceed the total length of the carriage, that is: ； In the formula, For the train commander, For the first The width of the package for each specification of material. For the first The number of columns for each specification of material. This represents the total quantity of materials by specification. To set the minimum error range.

3. The method for multi-specification and multi-variety bagged mixed loading according to claim 2, characterized in that, In step S3, the materials are stacked at approximately the same height, resulting in a set of equations: ； In the formula, For the first Stacking height of various material specifications For the first The package length of the material of this specification, For the first The bottom area of ​​the material of this specification. For the first The number of packages of materials of a certain specification.

4. The method for loading mixed bagged products of multiple specifications and varieties onto a vehicle according to claim 1, characterized in that, In step S4, the Levenberg-Marquardt algorithm is used iteratively based on the obtained stacking height and number of columns of each material specification to obtain a solution that meets the set minimum error range, i.e.: If we define the specifications and quantity of the materials Let the number of columns for the first material be... The number of columns for the second type of material is The number of columns for the third type of material is The height of the material is The base area of ​​the first material is The volume of the first material is The base area of ​​the second material is The volume of the second material is The base area of ​​the third material is The volume of the third material is ,get: ； Define residual vector : ； wherein is a parameter vector: ； minimizing the sum of squared residuals with respect to the parameter vector minimizing the sum of squared residuals with respect to the parameter vector ； Using the Jacobian matrix for the parameter vector Take the partial derivative: ； The Levenberg-Marquardt algorithm is then used to iterate over the obtained Jacobian matrix. ； wherein is the iteration number, is the step size, is the transpose of the Jacobian matrix, is a diagonal matrix, is the residual vector; When the parameter change is small enough and the residual is small enough, the iteration number is reached, that is: ； In the formula, This is the error value.

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