Method and equipment for setting and adjusting grabbing load of manipulator

Abstract

The invention provides a manipulator grabbing load setting and adjusting method and equipment. The setting method comprises the steps that force feedback data generated when a manipulator grabs and moves a grabbed object is collected; according to the force feedback data at the multiple moments, the sliding distance of the grabbed object relative to the robot arm is predicted; judging whether the sliding distance exceeds a first distance threshold value or not; when the sliding distance exceeds the first distance threshold value, determining a load increment according to the sliding distance; and setting a target load by using the load increment and the current load.

Description

Technical Field

[0001] The present invention relates to the field of robot arm control, and in particular to a method and device for setting and adjusting a gripping load of a robot arm. Background Art

[0002] Robotic arms can be used in a variety of industries. Currently, the automated grasping strategies of robotic arms are usually designed for some fixed application scenarios, so that they can grasp specific objects. In some application scenarios, the grasped objects may be highly diverse. If the grasping strategy is not adaptable enough, it may fail to grasp the target object or damage the target object during grasping.

[0003] The existing adaptive grasping strategy is to monitor whether the object slides in real time after grasping it, and increase the load until the object stops sliding if it slides. In actual application, it is found that the strategy is not adaptable enough. On the one hand, the timing of increasing the load is unreasonable, that is, when the object has already slipped, adjusting the load cannot effectively prevent it from sliding; on the other hand, it is difficult to preset the rate of increase of the load. Too low a rate of increase cannot prevent sliding, while too high a rate of increase may damage the object. Summary of the invention

[0004] In view of this, the present application provides a method for setting a gripping load of a robot arm, comprising: Collect force feedback data when the robot grasps and moves the grasped object; Predicting the sliding distance of the grasped object relative to the robot hand based on the force feedback data at multiple moments; Determining whether the sliding distance exceeds a first distance threshold; When the sliding distance exceeds the first distance threshold, determining a load increase amount according to the sliding distance; The target load is set using the load increase amount and the current load.

[0005] Optionally, the force feedback data includes pressure data collected by an electronic skin disposed at a contact portion between the robot hand and the grasped object, and driving force data of each driving device of the robot hand.

[0006] Optionally, the pressure data is array data collected by electronic skins respectively arranged on the palm and multiple fingers of the robotic hand.

[0007] Optionally, predicting the sliding distance of the grasped object relative to the robot hand according to the force feedback data at multiple moments includes: Predicting the pressure feature vector at the next moment according to the array data at multiple moments by a time series prediction module; Extracting a vertical driving force component from the driving force data, and using a time series prediction module to predict a driving force feature vector at a next moment based on the vertical driving force component at multiple moments; Connecting the pressure characteristic vector and the driving force characteristic vector to obtain a global characteristic vector; The sliding distance is obtained according to the global feature vector.

[0008] Optionally, determining whether the sliding distance exceeds a first distance threshold includes: Determining whether the sliding distance exceeds a second distance threshold; When the sliding distance does not exceed the second distance threshold, determining whether the sliding distance exceeds the first distance threshold; When the sliding distance exceeds the second distance threshold, the robot arm moves downward, puts the grasped object back to the carrying plane, and prepares to grasp it again.

[0009] Optionally, setting a target load using the load increase and the current load includes: Calculating the load increase and the sum of the loads of the current load setting; Determining whether the total load exceeds the load upper limit; When the total load does not exceed the upper load limit, the total load is set as the target load; When the total load exceeds the upper load limit, the upper load limit is set as the target load.

[0010] The present application also provides a method for adjusting a gripping load of a robot arm, comprising: In the process of the robot arm grasping and moving the grasped object, a first adjustment operation is triggered according to a first cycle, and a second adjustment operation is triggered according to a second cycle. The first adjustment operation includes judging whether the grasped object slides based on force feedback data at a current moment and force feedback data at a previous moment, and increasing the load when the grasped object slides. The second adjustment operation includes setting a target load using the above method, and increasing the load until the target load is reached.

[0011] Optionally, the second period is greater than the first period.

[0012] Optionally, after the second adjustment operation is triggered, before the current load reaches the target load, triggering of the first adjustment operation is paused.

[0013] Optionally, the load increase amount when the first adjustment operation is triggered is a fixed amount, and the fixed amount is smaller than the load increase amount in the second adjustment operation.

[0014] Optionally, the force feedback data in the first adjustment operation includes the sum of pressure data collected by the electronic skin disposed at the contact portion between the robot hand and the grasped object, and a vertical component extracted from the driving force data of the specific driving device.

[0015] Optionally, judging whether the grasped object slides according to the force feedback data at the current moment and the force feedback data at the previous moment includes: Calculating the difference between the total pressure according to the sum of the pressure data at the current moment and the previous moment, and calculating the difference between the vertical components according to the vertical driving force components at the current moment and the previous moment; Determining whether any one of the difference in total pressure and the difference in vertical component exceeds a corresponding set threshold; When either the difference in the total pressure or the difference in the vertical component exceeds a corresponding set threshold, it is determined that the grasped object has slipped.

[0016] Accordingly, the present application provides a robot gripping control device, comprising: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor performs the above method.

[0017] The robot gripping load setting method and device provided in the present application use feedback data at multiple times to predict the sliding distance of an object relative to the robot arm, and can obtain a target load suitable for the current grasped object before the object actually slides, thereby effectively stopping the sliding trend. In addition, this scheme sets the load increment based on the predicted sliding distance, so that the size of the target load is reasonable, avoiding damage to the object while stopping sliding.

[0018] According to the robot arm gripping load adjustment method and device provided in the present application, two different adjustment operations are used to adjust the robot arm load. Through the first adjustment operation with a faster response speed, it is ensured that the load is increased in time when the grasped object slides. Through the second adjustment operation with a higher accuracy, the sliding distance is predicted and a suitable target load is set, thereby stopping the sliding trend. This solution takes into account both response speed and accuracy, and improves the adaptability of the robot arm in gripping objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1is a flow chart of a method for setting a gripping load of a robot arm in an embodiment of the present invention; Figure 2 Schematic diagram of the neural network model architecture in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] The process of a robot grasping an object can be divided into four stages, namely the positioning stage, the loading stage, the grasping stage and the placement stage. The positioning stage refers to the robot determining the location of the grasped object, moving, approaching until it touches the grasped object; the loading stage refers to adjusting the load of the robot after the robot touches the grasped object, and determining the load value that can be grasped without destroying the grasped object; the grasping stage refers to grasping the object and moving it (moving upward, moving horizontally above the target position); the placement stage refers to placing the object at the target position.

[0024] In order to prevent the grasped object from slipping from the robot hand during the grasping stage, the load needs to be further adjusted. For this stage, this embodiment provides a method for setting the grasping load of the robot hand, which can be executed by an electronic device such as a computer or a server, such as Figure 1 The following operations are shown: S31A, collecting force feedback data when the robot grasps and moves the grasped object. The force feedback data can specifically be the pressure or pressure at the contact point between the robot and the grasped object, as well as the force of the driving device that changes the load. This method can collect force feedback data at a certain period, and the value of the force feedback data will change with the longitudinal or lateral movement of the robot.

[0025] S32A, predicting the sliding distance of the grasped object relative to the robot hand based on the force feedback data at multiple moments. The multiple moments may include the current acquisition moment and one or more acquisition moments before it. For example, the initial moment of entering the grasping phase is t0, and the current acquisition moment is t i , then multiple moments can be t0~t i (i.e. all moments from the initial moment to the current moment), or t i-n、 t i (i.e. the current moment and the n moments before it), or t j ~ti (t j refers to the last time the load was changed or set).

[0026] The sliding distance can be calculated using a pre-established mathematical model, for example, the corresponding sliding distance can be calculated based on the changing trend and rate of change of force feedback data at multiple moments; or a neural network model suitable for processing sequence data such as LSTM (Long Short-Term Memory) can be used, with force feedback data at multiple moments as input to output the sliding distance.

[0027] It should be noted that the sliding distance obtained in step S32A refers to the distance that the grasped object is expected to slide, rather than the distance that has already slid before.

[0028] S33A, determining whether the sliding distance exceeds a first distance threshold. When the sliding distance exceeds the first distance threshold, it indicates that the grasped object is expected to slide over a certain distance under the grip of the current load, thereby posing a risk of slipping from the robot hand, and thus step S34A is executed, otherwise the load is kept unchanged and the process returns to step S31A.

[0029] In an optional embodiment, step S33A further includes determining whether the sliding distance exceeds a second distance threshold (the second distance threshold is greater than the first distance threshold, for example, the first distance threshold is 2 mm and the second distance threshold is 7 mm); when the sliding distance does not exceed the second distance threshold, determining whether the sliding distance exceeds the first distance threshold; when the sliding distance exceeds the second distance threshold, it indicates that the sliding distance of the grasped object under the current load is expected to be large, and even if the load is increased, it may not be possible to stop the object from sliding. In this case, the robot arm can be moved downward to place the grasped object back on the load-bearing plane to prepare for re-grasping.

[0030] S34A, determining the load increase according to the sliding distance. A correspondence between the sliding distance and the load increase can be established in advance, for example, different sliding distance intervals correspond to different load increases, and if the sliding distance is in a certain interval, the interval corresponds to the load increase; or a mathematical calculation model and a neural network model are used to calculate the load increase according to the sliding distance.

[0031] S35A, using the load increase and the current load to set the target load. For example, the target load = the current load + the load increase. The load can be understood as a characteristic value related to the force, which can characterize the gripping force of the robot hand on the grasped object. The characteristic value can be one or more, specifically, it can be part of the force feedback data, or a value calculated based on the force feedback data.

[0032] The method for setting the gripping load of a robot arm provided in an embodiment of the present invention utilizes feedback data at multiple moments to predict the sliding distance of an object relative to the robot arm, and can obtain a target load suitable for the current grasped object before the object actually slides, thereby effectively stopping the sliding trend. In addition, this scheme sets the load increment based on the predicted sliding distance, so that the size of the target load is reasonable, thereby preventing sliding while avoiding damage to the object.

[0033] In one embodiment, in order to further prevent the grasped object from being damaged, the sum of the current load and the load increase (the total load) can be calculated first to determine whether the total load exceeds the upper load limit. The upper load limit means that the grasped object is likely to be damaged above this load (which can be called the damage load). The upper load limit is a preset value, which can be calculated by a set algorithm during the loading stage or can be set manually. When the total load does not exceed the upper load limit, the total load is set to the target load; when the total load exceeds the upper load limit, the upper load limit is set to the target load.

[0034] In one embodiment, the force feedback data includes pressure data collected by the electronic skin disposed at the contact part between the robot hand and the grasped object, and driving force data of each driving device of the robot hand. Further, the pressure data is array data collected by the electronic skin disposed at the palm and multiple fingers of the robot hand.

[0035] The contact parts between the robot hand and the grasped object and the driving device for changing the load depend on the structure of the robot hand. Taking the humanoid mobile robot hand as an example, the contact parts are the surfaces of five fingers and one palm. The driving device for changing the load can be a device for driving the movement of finger joints, and specifically, each movable joint can correspond to an independent driving device.

[0036] For the humanoid robot hand, the pressure data collected by the electronic skin refers to the force (pressure or intensity) exerted by the grasped object on the surface of the fingers and palm. The driving force data refers to the force borne by the driving device.

[0037] As an example, The first electronic skin Line The pressure data is recorded as ; Driving force data .

[0038] According to the above example, the size of the electronic skin at different positions in the robot hand or the number of measurement points therein may be different, so the number of rows and columns of the array is different.

[0039] Furthermore, regarding the load (current load and target load), assuming that each of the five fingers is provided with one piece of electronic skin and the palm is provided with one piece of electronic skin, the load can be the maximum value of all array data, that is, max ( The load may also be the smaller of the maximum value in the array data collected by the electronic skin of the thumb and the maximum value in the array data collected by the electronic skin of other fingers. The maximum value in the array data of the thumb is recorded as max ( ), the maximum value in the array data of the other four fingers is recorded as max ( , , , ), then the load is min (max ( ), max( , ,, )).

[0040] like Figure 2 As shown, in one embodiment, step S32A uses a neural network model to predict the sliding distance, which specifically includes the following processing: The time series prediction module predicts the pressure feature vector at the next moment based on the array data at multiple moments. Specifically, several ConvLSTM (convolutional long short-term memory networks) are used to transform the pressure data of each piece of electronic skin into a Normalized and used as input data for several consecutive ConvLSTM networks. This layer extracts spatial features in each time step, captures dynamic changes between time steps, outputs new features and normalizes them. Then, through the Flatten layer, the multidimensional data is flattened into a one-dimensional vector (the last time step is taken and flattened to obtain the pressure feature vector). The above layers process each piece of electronic skin separately, and the parameters of the layers are shared.

[0041] The vertical driving force component is extracted from the driving force data, and the driving force feature vector of the next moment is predicted based on the vertical driving force component at multiple moments through the time series prediction module. The vertical driving force component in is used as the input data of several LSTM layers, and the driving force feature vector is output after being transformed by several LSTM layers.

[0042] The pressure eigenvector and the driving force eigenvector are connected to obtain the global eigenvector.

[0043] The sliding distance is obtained according to the global feature vector. Specifically, MLP (Multi-Layer Perceptron) is used, and the global feature vector is used as the input data of MLP to output the sliding distance.

[0044] After obtaining the target load through the above steps S31A~S35A, the operation of adjusting the current load of the robot arm until the target load is reached is referred to as the second adjustment operation in this application; in one embodiment, another load adjustment operation based on threshold comparison is also used to adjust the load in the grasping stage.

[0045] This embodiment provides a load adjustment method based on threshold comparison (referred to as a first adjustment operation in this application), which can be executed by an electronic device such as a computer or a server, including: S31B, judging whether the grasped object is sliding according to the force feedback data at the current moment and the force feedback data at the previous moment, and executing step S32B when the grasped object is sliding, otherwise continuing to detect. Different from S32A, S31B only gives a yes or no judgment result without calculating the sliding distance.

[0046] Regarding the force feedback data of this embodiment, the same data as in the above step S31A can be used, including the pressure data collected by the electronic skin set at the contact part between the robot hand and the grasped object, and the driving force data of each driving device of the robot hand. Further, the pressure data is array data collected by the electronic skin set at the palm and multiple fingers of the robot hand.

[0047] In order to improve the calculation efficiency, the force feedback data used in this embodiment is the sum of the pressure data collected by each electronic skin and the vertical component extracted from the driving force data of the specific driving device. As a preferred embodiment, step S31B includes: calculating the difference in total pressure according to the sum of the pressure data at the current moment and the previous moment, and calculating the difference in vertical component according to the vertical driving force component at the current moment and the previous moment; judging whether any one of the difference in total pressure and the difference in vertical component exceeds the corresponding set threshold; when any one of the difference in total pressure and the difference in vertical component exceeds the corresponding set threshold, judging that the grasped object has slipped.

[0048] S32B, increase the load, the load increase amount is fixed.

[0049] Based on the above-mentioned first adjustment operation and second adjustment operation, this embodiment provides a method for adjusting the gripping load of a robot arm, which can be executed by an electronic device such as a computer or a server, including: In the process of the robot arm grasping and moving the grasped object, the first adjustment operation is triggered according to the first cycle, and the second adjustment operation is triggered according to the second cycle. The first adjustment operation is detailed in steps S31B~S32B, and the second adjustment operation is detailed in steps S31A~S35A to set the target load, and then adjust the current load until the target load is reached.

[0050] The triggering periods of the two different adjustment operations are different, so as to ensure that they are not triggered at the same time step. Preferably, the second period is greater than the first period. The load increase amount in the first adjustment operation is a fixed amount, and the load increase amount in the second adjustment operation is a non-fixed amount. In a preferred embodiment, the fixed amount is configured to be less than the minimum value of the non-fixed amount, for example, different orders of magnitude can be used.

[0051] It can be seen that when the first adjustment operation is triggered and it is determined that the load needs to be increased, a smaller increase will be applied; when the second adjustment operation is triggered and it is determined that the load needs to be increased, a relatively large and non-fixed increase will be applied. Further, after the second adjustment operation is triggered, before the current load reaches the target load, the triggering of the first adjustment operation is suspended.

[0052] For example, the initial load is 1.0N, the collection period of force feedback data is 10ms, the first period is 20ms, and the second period is 100ms. Two data sets M1 and M2 are set, M1 is used to store the force feedback data B required for the second adjustment operation (the pressure of each electronic skin, the vertical component of the driving force of each driving device), and M2 is used to store the force feedback data A required for the first adjustment operation (the total pressure, the vertical component of the specific driving device). The initial moment of entering the grasping stage is recorded as t0. The example of adjusting the load is as follows: t0+10ms: The force feedback data A of the first time step is added to the data set M1.

[0053] t0+20ms: Trigger the first adjustment operation. At this time, the data set M2 is empty, the load is not adjusted, and the force feedback data B is calculated based on the force feedback data A of the first time step and stored in M2.

[0054] t0+30ms: The force feedback data A of the second time step is added to M1.

[0055] t0+40ms: Trigger the first adjustment operation. Calculate the force feedback data B stored in M2 and the force feedback data B calculated from the second time step data, calculate the difference in total pressure and vertical component, and determine whether sliding occurs (steps S31B~S32B). Assuming that no sliding occurs, the load is not adjusted, and the force feedback data B calculated from the second time step data covers M2. The above calculation can be completed within a few milliseconds.

[0056] … t0+80ms: Trigger the first adjustment operation. Calculate the force feedback data B stored in M2 and the force feedback data B calculated from the 4th time step data, calculate the difference in total pressure and vertical component, and determine whether sliding occurs (steps S31B~S32B). Assuming that sliding occurs, increase the load to 1.02N (the increase is a fixed value of 0.02N). Overwrite M2 with the force feedback data B calculated from the 4th time step data.

[0057] t0+90ms: The force feedback data A of the fifth time step is added to M1.

[0058] t0+100ms: Trigger the first adjustment operation. Assume that the load is increased to 1.04 N. Overwrite M2 with the force feedback data B calculated from the fifth time step data.

[0059] t0+105ms: trigger the second adjustment operation. In an independent thread, set the target load according to the force feedback data A from t0 to the triggering moment. At this time, the target load is determined using steps S31A~S35A. In actual application, since this calculation process takes a certain amount of time, the first adjustment operation is still triggered in the next several time steps until the result is obtained. It should be noted that the current load in step S35A refers to the load when the second adjustment operation is triggered, that is, 1.04N.

[0060] t0+110ms: The force feedback data A of the sixth time step is added to M1.

[0061] t0+120ms: Trigger the first adjustment operation. Assume that the load is increased to 1.06 N. Overwrite M2 with the force feedback data B calculated from the 6th time step data.

[0062] t0+130ms: The force feedback data A of the 7th time step is added to M1.

[0063] t0+140ms: Trigger the first adjustment operation. Assume that the load is increased to 1.08 N. Overwrite M2 with the force feedback data B calculated from the 7th time step data.

[0064] t0+145ms: Steps S31A~S35A are completed and the output sliding distance is 3mm, which is greater than the first threshold of 2mm but less than the second threshold of 7mm. The load increase is determined to be 0.2N, and the target load = the load when the second adjustment operation is triggered (t0+105ms) 1.04N+0.2N=1.24N. The current load is 1.08N, and the target load is 1.24N. The load is increased until the target load is reached. Before the adjustment is completed, the first adjustment operation is no longer triggered.

[0065] t0+150ms: The force feedback data A of the 8th time step is added to M1.

[0066] t0+170ms: The force feedback data A of the 9th time step is added to M1.

[0067] The above examples are only for illustrating how to use two adjustment operations to control the load at the same time. The specific values ​​of the load value, cycle value, etc. used in the examples are only for reference, and are not intended to limit the values ​​of the relevant data.

[0068] According to the robot arm grasping load adjustment method provided by an embodiment of the present invention, two different adjustment operations are used to adjust the robot arm load. Through the first adjustment operation with a faster response speed, it is ensured that the load is increased in time when a sliding event occurs in the grasped object. Through the second adjustment operation with a higher accuracy, the sliding distance is predicted and a suitable target load is set, thereby stopping the sliding trend. This solution takes into account both response speed and accuracy, and improves the adaptability of the robot arm to grasp objects.

[0069] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for setting a gripping load of a robot, characterized in that: include: Collect force feedback data when the robot grasps and moves the grasped object; Predicting the sliding distance of the grasped object relative to the robot hand based on the force feedback data at multiple moments; Determining whether the sliding distance exceeds a first distance threshold; When the sliding distance exceeds the first distance threshold, determining a load increase amount according to the sliding distance; The target load is set using the load increase amount and the current load.

2. The method according to claim 1, characterized in that The force feedback data includes pressure data collected by the electronic skin disposed at the contact portion between the robot hand and the grasped object, and driving force data of each driving device of the robot hand.

3. The method according to claim 2, characterized in that The pressure data is array data collected by electronic skins respectively arranged on the palm and multiple fingers of the robot hand.

4. The method according to claim 3, characterized in that Predicting the sliding distance of the grasped object relative to the robot hand according to the force feedback data at multiple moments, including: Predicting the pressure feature vector at the next moment according to the array data at multiple moments by a time series prediction module; Extracting a vertical driving force component from the driving force data, and using a time series prediction module to predict a driving force feature vector at a next moment based on the vertical driving force component at multiple moments; Connecting the pressure characteristic vector and the driving force characteristic vector to obtain a global characteristic vector; The sliding distance is obtained according to the global feature vector.

5. The method according to claim 1, characterized in that Determining whether the sliding distance exceeds a first distance threshold includes: Determining whether the sliding distance exceeds a second distance threshold; When the sliding distance does not exceed the second distance threshold, determining whether the sliding distance exceeds the first distance threshold; When the sliding distance exceeds the second distance threshold, the robot arm moves downward, puts the grasped object back to the carrying plane, and prepares to grasp it again.

6. The method according to claim 1, characterized in that The target load is set using the load increase amount and the current load, including: Calculating the load increase and the sum of the loads of the current load setting; Determining whether the total load exceeds the load upper limit; When the total load does not exceed the upper load limit, the total load is set as the target load; When the total load exceeds the upper load limit, the upper load limit is set as the target load.

7. A method for adjusting the gripping load of a robot arm, characterized in that: include: During the process of the robot arm grasping and moving the grasped object, a first adjustment operation is triggered according to a first cycle, and a second adjustment operation is triggered according to a second cycle. The first adjustment operation includes judging whether the grasped object slides based on force feedback data at a current moment and force feedback data at a previous moment, and increasing the load when the grasped object slides. The second adjustment operation includes setting a target load using the method described in any one of claims 1 to 6, and increasing the load until the target load is reached.

8. The method according to claim 7, characterized in that The second period is greater than the first period.

9. The method according to claim 8, characterized in that After the second adjustment operation is triggered, before the current load reaches the target load, triggering of the first adjustment operation is suspended.

10. The method according to claim 7, characterized in that The load increase amount when the first adjustment operation is triggered is a fixed amount, and the fixed amount is smaller than the load increase amount in the second adjustment operation.

11. The method according to claim 7, characterized in that The force feedback data in the first adjustment operation includes the sum of pressure data collected by the electronic skin disposed at the contact portion between the robot hand and the grasped object, and the vertical component extracted from the driving force data of the specific driving device.

12. The method according to claim 11, characterized in that Determine whether the grasped object is sliding based on the force feedback data at the current moment and the force feedback data at the previous moment, including: Calculating the difference between the total pressure according to the sum of the pressure data at the current moment and the previous moment, and calculating the difference between the vertical components according to the vertical driving force components at the current moment and the previous moment; Determining whether any one of the difference in total pressure and the difference in vertical component exceeds a corresponding set threshold; When either the difference in the total pressure or the difference in the vertical component exceeds a corresponding set threshold, it is determined that the grasped object has slipped.

13. A robot gripping control device, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to enable the processor to perform the method according to any one of claims 1 to 12.

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