A palletizing robot motion trajectory planning method and system and a storage medium

Abstract

This invention relates to a motion trajectory planning method, system, and storage medium for a palletizing robot. The method includes: T1. Starting the palletizing robot, acquiring joint displacement variable data and offset distance data in real time based on a first displacement sensor installed at the robot's joints, acquiring joint torsion angle data in real time based on an angle sensor installed at the robot's joints, and acquiring link length data in real time based on a second displacement sensor installed at the robot's links; T2. Transforming the joint displacement variable data, offset distance data, joint torsion angle data, and link length data into a unified Cartesian coordinate space for data synthesis processing, and outputting the transformed synthesized data. This invention not only improves the working efficiency of the palletizing robot but also further enhances the safety and stability of goods transportation through the optimization of the palletizing robot's motion trajectory.

Description

Technical Field

[0001] This invention relates to the field of palletizing robot control technology, and in particular to a method, system and storage medium for planning the motion trajectory of a palletizing robot. Background Technology

[0002] With the continuous development of my country's economy and the rapid advancement of science and technology, robots are widely used in industries such as palletizing, gluing, spot welding, arc welding, spraying, handling, and measurement. Many factors, including the types of packaging, factory environment, and customer demands, have made palletizing a challenging task in packaging plants. To overcome these difficulties, all aspects of palletizing equipment are being developed and improved, from robotic arms to the software that operates them. The market demand for flexibility is constantly growing, a trend that has impacted many aspects of packaging, including the later stages of the production line. Retail customers, especially influential ones like large supermarkets, often require customized random pallets, but they have to customize each pallet individually, with only occasional repetitions in the pallet format. Moreover, efficient production of such random pallets is quite difficult.

[0003] Palletizing robots are a product of the organic combination of mechanics and computer programs, providing higher production efficiency for modern manufacturing. Palletizing robots are widely used in the palletizing industry, significantly saving labor and space. They are flexible, precise, fast, efficient, and highly stable, resulting in high operational efficiency. However, optimizing the motion trajectory of palletizing robots when handling goods remains a problem that urgently needs improvement. Summary of the Invention

[0004] In view of the above problems, the present invention provides a motion trajectory planning method, system and storage medium for palletizing robots, which not only improves the working efficiency of palletizing robots, but also further improves the safety and stability of cargo transportation by optimizing the motion trajectory of palletizing robots.

[0005] To achieve the above and other related objectives, the present invention provides the following technical solution:

[0006] A method for planning the motion trajectory of a palletizing robot, the method comprising:

[0007] T1. The palletizing robot starts up. The first displacement sensor set at the joint of the robot obtains joint displacement variable data and offset distance data in real time. The angle sensor set at the joint of the robot obtains joint torsion angle data in real time. The second displacement sensor set at the link of the robot obtains link length data in real time.

[0008] T2. Transform the joint displacement variable data, offset distance data, joint torsion angle data, and link length data into a unified Cartesian coordinate space for data synthesis processing, and output the transformed comprehensive data information;

[0009] T3. Based on the transformed comprehensive data information, the motion trajectory of the palletizing robot is optimized using a fusion optimization trajectory algorithm, and the adjustment and control data information of the palletizing robot is output.

[0010] Furthermore, in step T3, the fusion optimization trajectory algorithm includes:

[0011] T31. Based on the transformed comprehensive data information, construct a first motion trajectory optimization function.

[0012] ,

[0013] Where x1 is the joint displacement variable value, x2 is the offset distance, x3 is the joint torsion angle, x4 is the link length, x5 is the joint type, and α1, α2, α3, α4, α5 and α6 are constant coefficients;

[0014] T32. Based on the first motion trajectory optimization function f, output the first motion optimization trajectory data information;

[0015] T33. Based on the link length data and the working time of the palletizing robot, obtain the working speed and acceleration of the palletizing robot, and then construct a second motion trajectory optimization function.

[0016] ,

[0017] Where v is the working speed of the palletizing robot, a is the working acceleration of the palletizing robot, k is a constant factor, and β1, β2, β3 and β4 are constant parameters;

[0018] T34. Based on the second motion trajectory optimization function, output the second motion optimization trajectory data information;

[0019] T35. Based on the first motion optimization trajectory data information and the second motion optimization trajectory data information, a fusion cost function H is adopted.

[0020] H = γ1S1 + γ2S2,

[0021] Where S1 is the first optimized motion trajectory, S2 is the second optimized motion trajectory, γ1 and γ2 are cost factors, and the output is the adjustment and control data information of the palletizing robot.

[0022] Furthermore, in step T31, the joint type x5,

[0023] .

[0024] Furthermore, in step T33, the constant factor k takes values ​​in the range of (0,1).

[0025] Furthermore, in step T35, the cost factors γ1 and γ2 satisfy the following conditions:

[0026] .

[0027] Furthermore, in step T33, the working speed v and the working acceleration a of the palletizing robot are...

[0028] ,

[0029] Where r1 is the initial length of the link, r2 is the length of the link's motion, and t is the time it takes for the link to move from r1 to r2;

[0030] ,

[0031] Where v1 is the velocity of the link at time t1, v2 is the velocity of the link at time t2, and L = t2 - t1.

[0032] Furthermore, the palletizing robot adjustment and control data information is obtained by deriving corresponding palletizing robot adjustment parameters and control parameters based on the optimized palletizing robot motion trajectory.

[0033] To achieve the above and other related objectives, the present invention also provides a motion trajectory planning system for a palletizing robot, the system comprising:

[0034] The data acquisition module includes a displacement sensor and an angle sensor, which are used to acquire displacement data and angle data of the corresponding moving parts of the palletizing robot;

[0035] The data processing module obtains the motion optimization trajectory data information of the palletizing robot based on the fusion optimization trajectory algorithm;

[0036] The data parsing module is used to parse the motion optimization trajectory data of the palletizing robot and output the adjustment and control parameters of the palletizing robot.

[0037] Furthermore, the data acquisition module is connected to the data processing module, and the data processing module is connected to the data parsing module.

[0038] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the described palletizing robot motion trajectory planning methods.

[0039] The present invention has the following positive effects:

[0040] 1. This invention optimizes the motion trajectory of the palletizing robot by integrating and optimizing trajectory algorithms, which not only improves the working efficiency of the palletizing robot, but also reduces its energy consumption.

[0041] 2. By installing displacement sensors and angle sensors at the joints and links of the palletizing robot, this invention can not only acquire real-time status data of the palletizing robot, but also serve as reference data when the palletizing robot malfunctions, facilitating routine inspections. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0043] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0044] Figure 3 This is a schematic diagram of the palletizing robot of the present invention.

[0045] The labels in the diagram are as follows: 1—First displacement sensor, 2—Second displacement sensor, 3—Angle sensor, 4—Cargo. Detailed Implementation

[0046] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0047] Example 1: As Figure 1 As shown, a motion trajectory planning method for a palletizing robot includes:

[0048] T1. The palletizing robot starts up. The first displacement sensor set at the joint of the robot obtains joint displacement variable data and offset distance data in real time. The angle sensor set at the joint of the robot obtains joint torsion angle data in real time. The second displacement sensor set at the link of the robot obtains link length data in real time.

[0049] T2. Transform the joint displacement variable data, offset distance data, joint torsion angle data, and link length data into a unified Cartesian coordinate space for data synthesis processing, and output the transformed comprehensive data information;

[0050] T3. Based on the transformed comprehensive data information, the motion trajectory of the palletizing robot is optimized using a fusion optimization trajectory algorithm, and the adjustment and control data information of the palletizing robot is output.

[0051] In this embodiment, in step T3, the fusion optimization trajectory algorithm includes:

[0052] T31. Based on the transformed comprehensive data information, construct a first motion trajectory optimization function.

[0053] ,

[0054] Where x1 is the joint displacement variable value, x2 is the offset distance, x3 is the joint torsion angle, x4 is the link length, x5 is the joint type, and α1, α2, α3, α4, α5 and α6 are constant coefficients;

[0055] T32. Based on the first motion trajectory optimization function f, output the first motion optimization trajectory data information;

[0056] T33. Based on the link length data and the working time of the palletizing robot, obtain the working speed and acceleration of the palletizing robot, and then construct a second motion trajectory optimization function.

[0057] ,

[0058] Where v is the working speed of the palletizing robot, a is the working acceleration of the palletizing robot, k is a constant factor, and β1, β2, β3 and β4 are constant parameters;

[0059] T34. Based on the second motion trajectory optimization function, output the second motion optimization trajectory data information;

[0060] T35. Based on the first motion optimization trajectory data information and the second motion optimization trajectory data information, a fusion cost function H is adopted.

[0061] H = γ1S1 + γ2S2,

[0062] Where S1 is the first optimized motion trajectory, S2 is the second optimized motion trajectory, γ1 and γ2 are cost factors, and the output is the adjustment and control data information of the palletizing robot.

[0063] In this embodiment, in step T31, the joint type x5,

[0064] .

[0065] In this embodiment, in step T33, the constant factor k takes values ​​in the range of (0, 1).

[0066] In this embodiment, in step T35, the conditions satisfied by the cost factors γ1 and γ2 are as follows:

[0067] .

[0068] In this embodiment, in step T33, the working speed v and the working acceleration a of the palletizing robot are...

[0069] ,

[0070] Where r1 is the initial length of the link, r2 is the length of the link's motion, and t is the time it takes for the link to move from r1 to r2;

[0071] ,

[0072] Where v1 is the velocity of the link at time t1, v2 is the velocity of the link at time t2, and L = t2 - t1.

[0073] In this embodiment, the palletizing robot adjustment and control data information is obtained by calculating the corresponding palletizing robot adjustment parameters and control parameters based on the optimized palletizing robot motion trajectory.

[0074] Example 2: Based on the motion trajectory planning method for a palletizing robot in Example 1, the present invention will be further explained and described below.

[0075] like Figure 2 or Figure 3 As shown, the present invention provides a motion trajectory planning system for a palletizing robot, the system comprising:

[0076] The data acquisition module includes a displacement sensor and an angle sensor, which are used to acquire displacement data and angle data of the corresponding moving parts of the palletizing robot;

[0077] The data processing module obtains the motion optimization trajectory data information of the palletizing robot based on the fusion optimization trajectory algorithm;

[0078] The data parsing module is used to parse the motion optimization trajectory data of the palletizing robot and output the adjustment and control parameters of the palletizing robot.

[0079] In this embodiment, the data acquisition module is connected to the data processing module, and the data processing module is connected to the data parsing module.

[0080] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the described palletizing robot motion trajectory planning methods.

[0081] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0082] In summary, this invention not only improves the working efficiency of palletizing robots, but also further enhances the safety and stability of cargo transportation by optimizing the movement trajectory of the palletizing robots.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for planning the motion trajectory of a palletizing robot, characterized in that, The method includes: T1. The palletizing robot starts up. The first displacement sensor set at the joint of the robot obtains joint displacement variable data and offset distance data in real time. The angle sensor set at the joint of the robot obtains joint torsion angle data in real time. The second displacement sensor set at the link of the robot obtains link length data in real time. T2. Transform the joint displacement variable data, offset distance data, joint torsion angle data, and link length data into a unified Cartesian coordinate space for data synthesis processing, and output the transformed comprehensive data information; T3. Based on the transformed comprehensive data information, a fusion optimization trajectory algorithm is used to optimize the motion trajectory of the palletizing robot, and the adjustment and control data information of the palletizing robot is output. In step T3, the fusion optimization trajectory algorithm includes: T31. Based on the transformed comprehensive data information, construct a first motion trajectory optimization function. ， Where x1 is the joint displacement variable value, x2 is the offset distance, x3 is the joint torsion angle, x4 is the link length, x5 is the joint type, and α1, α2, α3, α4, α5 and α6 are constant coefficients; T32. Based on the first motion trajectory optimization function f, output the first motion optimization trajectory data information; T33. Based on the link length data and the working time of the palletizing robot, obtain the working speed and acceleration of the palletizing robot, and then construct a second motion trajectory optimization function. , Where v is the working speed of the palletizing robot, a is the working acceleration of the palletizing robot, k is a constant factor, and β1, β2, β3 and β4 are constant parameters; T34. Based on the second motion trajectory optimization function, output the second motion optimization trajectory data information; T35. Based on the first motion optimization trajectory data information and the second motion optimization trajectory data information, a fusion cost function H is adopted. H = γ1S1 + γ2S2, Where S1 is the first optimized motion trajectory, S2 is the second optimized motion trajectory, γ1 and γ2 are cost factors, and the output is the adjustment and control data information of the palletizing robot.

2. The motion trajectory planning method for a palletizing robot according to claim 1, characterized in that, In step T31, the joint type x5, 。 3. The motion trajectory planning method for a palletizing robot according to claim 1, characterized in that, In step T33, the constant factor k takes values ​​in the range of (0, 1).

4. The motion trajectory planning method for a palletizing robot according to claim 1, characterized in that, In step T35, the cost factors γ1 and γ2 satisfy the following conditions: 。 5. The motion trajectory planning method for a palletizing robot according to claim 1, characterized in that, In step T33, the working speed v and the working acceleration a of the palletizing robot are... ， Where r1 is the initial length of the link, r2 is the length of the link's motion, and t is the time it takes for the link to move from r1 to r2; ， Where v1 is the velocity of the link at time t1, v2 is the velocity of the link at time t2, and L = t2 - t1.

6. The motion trajectory planning method for a palletizing robot according to claim 1, characterized in that: The palletizing robot adjustment and control data information is obtained by deriving the corresponding palletizing robot adjustment parameters and control parameters based on the optimized palletizing robot motion trajectory.

7. A motion trajectory planning system for a palletizing robot, characterized in that, The system is used to implement the motion trajectory planning method for the palletizing robot according to any one of claims 1-6, the system comprising: The data acquisition module includes a displacement sensor and an angle sensor, which are used to acquire displacement data and angle data of the corresponding moving parts of the palletizing robot; The data processing module obtains the motion optimization trajectory data information of the palletizing robot based on the fusion optimization trajectory algorithm; The data parsing module is used to parse the motion optimization trajectory data of the palletizing robot and output the adjustment and control parameters of the palletizing robot.

8. The motion trajectory planning system for a palletizing robot according to claim 7, characterized in that: The data acquisition module is connected to the data processing module, and the data processing module is connected to the data parsing module.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is programmed or configured to perform the motion trajectory planning method for the palletizing robot according to any one of claims 1 to 6.

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