Indirect parameter adaptive six-axis robot arm collision detection method, system and device

Abstract

The present invention relates to an indirect parameter adaptive six-axis robot arm collision detection method, system and device. The collision detection method comprises the following steps: S1: obtaining a physical model of a six-axis robot arm with an unknown load. S2: collecting motion data of each joint of the six-axis robot arm, and calculating a load estimation value based on the motion data. S3: inputting the load estimation value and the motion data into an external force observer model to obtain an external force estimation value. S4: judging whether the external force estimation values ​​of all joints are less than a preset external force threshold value. If the external force estimation value of any joint exceeds the external force threshold value, it is deemed that a collision has occurred. Otherwise, it is deemed that no collision has occurred. The present invention can effectively estimate the size of the load, reduce the influence of load changes on external force estimation, make the external force estimation value more accurate, improve the accuracy and robustness of the collision detection method, and has a strong practical application value.

Description

Technical Field

[0001] The present invention relates to a six-axis robotic arm, and in particular to an indirect parameter adaptive six-axis robotic arm collision detection method, an indirect parameter adaptive six-axis robotic arm collision detection system, an indirect parameter adaptive six-axis robotic arm collision detection device, and an indirect parameter adaptive six-axis robotic arm control method. Background Art

[0002] As an important direction for the intelligent development of industrial robotic arms, human-machine collaboration combines the dexterity of humans with the advantages of high-speed and high-precision repetition of robotic arms, greatly improving the adaptability and intelligence of industrial robotic arms. In the process of human-machine collaboration, the robotic arm and humans work together in the same space. When the robotic arm collides with humans, it will not only affect work efficiency, but also have serious consequences for the life safety of the operator in serious cases. In order to ensure the safety of humans and robotic arms, the robotic arm needs to have collision detection capabilities.

[0003] At present, in order to reduce the complexity of the system and the hardware cost, the collision detection method based on the dynamic model is often used. However, with the expansion of the application field of the six-axis industrial robot, the end load of the six-axis robot can change at any time with the change of the task. The change of load will lead to a large model error, causing the existing collision detection method based on the fixed dynamic model to have the problem of missing or false positives. Therefore, how to effectively eliminate the influence of load changes and thus improve the accuracy and robustness of the collision detection algorithm is a problem worth studying. Summary of the invention

[0004] Based on this, it is necessary to provide an indirect parameter adaptive six-axis robotic arm collision detection method, system and device to address the problem that the existing six-axis robotic arm has low collision detection accuracy due to load changes.

[0005] The present invention is implemented by the following technical solution: An indirect parameter adaptive six-axis robot arm collision detection method comprises the following steps:

[0006] S1: Obtain the physical model of a six-axis robot with an unknown load.

[0007] S2: Collect the motion data of each joint of the six-axis robot arm, and calculate the load estimation value of the six-axis robot arm based on the motion data. The specific calculation method of the load estimation value is as follows:

[0008] S21: Construct an adaptive function. Adaptive function τ L Expressed as:

[0009]

[0010] In the formula, is the adaptive regression matrix, and ε is the model prediction error.

[0011] S22: Calculate the adaptive law matrix. The adaptive law matrix is ​​calculated using the following formula:

[0012]

[0013] Where u is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L (t) is the maximum eigenvalue of .

[0014] S23: Construct an indirect parameter adaptive law based on generalized momentum, take motion data as input, and calculate the load estimation value in the indirect parameter adaptive law. The load estimation value is calculated by the following formula:

[0015]

[0016] In the formula, is the upper bound of the adaptive law estimation rate, is the saturation function, is the projection mapping function, and They are closed sets interior and boundaries. for The unit normal vector pointing outward when .

[0017] S3: Obtain an external force observer model, input the load estimation value and motion data into the external force observer model, and obtain an external force estimation value.

[0018] S4: Determine whether the estimated external force value of each joint is less than a preset external force threshold value. If the estimated external force value of any joint exceeds the external force threshold value, it is considered that a collision has occurred. Otherwise, it is considered that no collision has occurred.

[0019] The above collision detection method constructs an indirect parameter adaptive law based on generalized momentum, collects the motion data of each joint in real time, effectively estimates the load size, and calculates the external force estimation value using the external force observer model. By estimating the load, the impact of load changes on the external force estimation can be reduced, making the external force estimation value more accurate. At the same time, setting a smaller external force threshold range can effectively realize collision detection, improve the accuracy and robustness of the collision detection method, and has strong practical application value.

[0020] In one embodiment, the physical model of the six-axis robot arm is expressed as:

[0021]

[0022] Where τ is the joint driving torque, q, and Represent the joint angle, angular velocity and angular acceleration respectively, M(q,β m ,β L ) is the inertia matrix of the robot arm, is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, β m is the system parameter of the robot body, β L is a load-related parameter, β f is the friction related parameter, τ e is the collision torque.

[0023] In one embodiment, the adaptive function is constructed as follows:

[0024] S211: Define generalized momentum as: By linearizing the parameters of the generalized momentum, we obtain:

[0025]

[0026] In the formula, and They represent the system parameter set β of the manipulator obtained by generalized momentum. m and load parameter set β L The corresponding regression matrix.

[0027] S212: Derivative the generalized momentum and linearize the parameters to obtain:

[0028]

[0029] In the formula, and They represent the system parameter set β of the manipulator obtained by the derivative of generalized momentum. m , load parameter set β L and friction parameter β f The corresponding regression matrix.

[0030] S213: Order β L The estimated value of and defines the model prediction error used for estimation. The model prediction error is expressed as:

[0031]

[0032] In the formula, τ f , They are τ, Filter The output value after They are Filter The output value after.

[0033] S214: Construct an adaptive function based on generalized momentum according to the model prediction error.

[0034] In one embodiment, the external force observer model is expressed as:

[0035]

[0036] In the formula, is the load estimate of the generalized momentum, r is the observed value of the external torque, K 1 , K 2 and K 3 are all positive definite diagonal gain matrices of the external force observer.

[0037] In one embodiment, the external force threshold is set to:

[0038] r mi =τ max i +τ mari

[0039] In the formula, r mi is the external force threshold corresponding to joint i, τ maxi is the maximum disturbance torque when joint i moves freely without collision, τ mari For safety margin.

[0040] The present invention also provides an indirect parameter adaptive six-axis mechanical arm collision detection system, the detection system comprises an acquisition module, a load estimation module, an external force observer and a collision signal generation module.

[0041] The acquisition module is used to collect the motion data of the six-axis robotic arm.

[0042] The load estimation module is used to: 1. Define the generalized momentum, derive the generalized momentum and linearize the parameters to obtain the regression matrix. 2. Define the model prediction error for load estimation, and then construct an adaptive function based on the model prediction error and the regression matrix. Adaptive function τ L Expressed as:

[0043]

[0044] In the formula, is the regression matrix, and ε is the model prediction error.

[0045] 2. Calculate the adaptive law matrix. Adaptive law matrix Γ L The calculation is done using the following formula:

[0046]

[0047] Where u is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L (t) is the maximum eigenvalue of .

[0048] 3. Calculate the load estimate. The load estimate is calculated using the following formula:

[0049]

[0050] In the formula, is the upper bound of the adaptive law estimation rate, is the saturation function, is the projection mapping function.

[0051] The external force observer is used to calculate the external force estimate based on the motion data and the load estimate. The external force observer is expressed as:

[0052]

[0053] In the formula, is the load estimate of the generalized momentum, C T , G, F f They are G(q,β m ,β L ), Abbreviation of is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, r is the observed value of the external torque, K 1 , K 2 and K 3 are all positive definite diagonal gain matrices of the external force observer.

[0054] The collision signal generating module is used to determine whether the external force estimation value exceeds a preset external force threshold value, and if so, generates a collision signal.

[0055] In one embodiment, the motion data includes the actual angle, actual angular velocity and driving torque of each joint of the six-axis robot arm.

[0056] The present invention also provides an indirect parameter adaptive six-axis robotic arm collision detection device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. The functional modules in the collision detection device are deployed in the manner of any of the above-mentioned indirect parameter adaptive six-axis robotic arm collision detection systems. When the processor executes the computer program, the steps of any of the above-mentioned indirect parameter adaptive six-axis robotic arm collision detection methods are implemented to realize indirect parameter adaptive six-axis robotic arm collision detection.

[0057] The present invention also provides a control method for an indirect parameter adaptive six-axis robotic arm, which uses the above-mentioned indirect parameter adaptive six-axis robotic arm collision detection method to detect whether the six-axis robotic arm collides, and controls the six-axis robotic arm to stop running when the six-axis robotic arm collides.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The collision detection method of the present invention collects the motion data of each joint in real time by constructing an indirect parameter adaptive law based on generalized momentum, effectively estimates the size of the load, and calculates the external force estimation value using the external force observer model. By estimating the load, the influence of load changes on the external force estimation can be reduced, making the external force estimation value more accurate. At the same time, setting a smaller external force threshold range can effectively realize the detection of collision, improve the accuracy and robustness of the collision detection method, and have strong practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of the main structure of the six-axis robotic arm of Example 1 of the present invention;

[0061] Figure 2 This is a step diagram of the indirect parameter adaptive six-axis robot arm collision detection method according to Example 1 of the present invention;

[0062] Figure 3 for Figure 2 Control flow chart of the collision detection method of the six-axis robot arm with intermediate and indirect parameter adaptation;

[0063] Figure 4 This is a simulation result diagram of external force estimation of the six-axis robot arm in Example 1 of the present invention under no-load conditions;

[0064] Figure 5 This is a simulation result diagram of external force estimation of the six-axis robot arm of Example 1 of the present invention under a load of 2.75 kg;

[0065] Figure 6 This is a simulation result diagram of load estimation of the six-axis robot arm of Example 1 of the present invention when the load is 2.75 kg;

[0066] Figure 7 It is a structural schematic diagram of an indirect parameter adaptive six-axis robot arm collision detection system according to Embodiment 2 of the present invention;

[0067] Figure 8 This is a flow chart of a control method for a six-axis robotic arm with indirect parameter adaptation according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0069] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is considered to be "fixed to" another component, it may be directly fixed on the other component or there may be a central component at the same time.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0071] Example 1

[0072] See also Figure 1 , which is a schematic diagram of the main structure of the six-axis robotic arm of this embodiment. Figure 1 In the figure, the six joints of the six-axis robot arm correspond to six servo motors respectively. Figure 1 Marked 1 to 6. Each joint is driven by a servo motor to achieve corresponding rotational movement. The servo motor has a built-in 17-bit absolute encoder for joint angle feedback. The servo motor is driven by a motor driver, which is connected to the controller. The controller calculates real-time control signals and sends them to the driver to control the movement of the motor. The controller in this embodiment is a controller with a product model of cSPACE, and the driver is a driver with a product model of GTHD-003-2AEC2. Of course, in other embodiments, the controller and driver can also use other models.

[0073] In order to overcome the influence of model uncertainty caused by load changes of industrial robot arms on the accuracy of collision detection, realize online load estimation and update the dynamic model, thereby improving the accuracy of collision detection, this embodiment provides a collision detection method based on indirect parameter adaptation. Figure 2 and Figure 3 , Figure 2 This is a step diagram of the indirect parameter adaptive six-axis robot arm collision detection method of this embodiment; Figure 3 for Figure 2 Control flow chart of the collision detection method of the six-axis robot arm with intermediate and indirect parameter self-adaptation. The collision detection method includes the following steps:

[0074] S1: Construct a physical model of a six-axis robot with an unknown load. During the operation of the six-axis robot, since the weight of the object (load) grasped or driven each time is unknown, the driving signal fed back each time also changes accordingly. In this embodiment, the existing physical model of the six-axis robot is used, which can be expressed as:

[0075]

[0076] Where τ is the joint driving torque, denoted as τ = [τ 1 ,…,τ n ] T ,q, and They represent the joint angle, angular velocity and angular acceleration respectively, and are denoted as q=[q 1 ,…,q n ] T , and The six-axis robot arm of this embodiment has six joints, so n=6. m ,β L ) is the inertia matrix of the robot arm, is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, β m ∈R 22×1 is the system parameter of the robot body, β L ∈R 10×1 is a load-related parameter, β f ∈R 12×1 is the friction related parameter, τ e is the collision torque.

[0077] In this embodiment, the inertia-related parameters can be set as:

[0078] β m=[-3.8947,0.8424,-0.5184,0.3450,-0.2295,-0.4797,-0.3677,0.0774,0.8046,4.3792,

[0079] 1.5747,0.2905,3.6873,0.7633,0.2324,1.5901,-0.0643,0.0642,0.0597,0.1722,0.0698,0.0397] T Friction related parameters can be set as:

[0080] β f =[14.2017,17.1847,11.9733,6.5531,3.2934,2.6232,18.9684,38.5417,2.8630,4.7834,2.4479,2.9940] T

[0081] Of course, in other embodiments, corresponding inertia-related parameters and friction-related parameters may be set according to the characteristic parameters of the six-axis robotic arm itself.

[0082] S2: Collect motion data of each joint of the six-axis robot arm, and calculate the load estimation value of the load according to the motion data. The motion data includes the actual angle, actual angular velocity and driving torque of each joint of the six-axis robot arm.

[0083] The load estimate is calculated as follows:

[0084] S21: Build an adaptive function.

[0085] S211; define generalized momentum as: By linearizing the parameters of the generalized momentum, we obtain:

[0086]

[0087] In the formula, and They represent the system parameter set β of the manipulator obtained by generalized momentum. m and load parameter set β L The corresponding regression matrix.

[0088] S212: Derivative the generalized momentum and linearize the parameters to obtain:

[0089]

[0090] In the formula, and They represent the system parameter set β of the manipulator obtained by the derivative of generalized momentum. m , load parameter set β L and the regression matrix corresponding to the friction parameters.

[0091] S213: Order β L The estimated value of and defines the model prediction error used for estimation. The model prediction error ε can be expressed as:

[0092]

[0093] In the formula, τ f , are τ, Filter The output value after.

[0094] They are Filter The output value after.

[0095] S214: Construct an adaptive function based on generalized momentum according to the model prediction error. Then the adaptive function τ L It can be expressed as:

[0096]

[0097] S22: Calculate the adaptive law matrix Γ L . Adaptive law matrix Γ L It can be calculated using the following formula:

[0098]

[0099] In the formula, u≥0 is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L (t) is the maximum eigenvalue of .

[0100] S23: Construct an indirect parameter adaptive law based on generalized momentum, take motion data as input, and calculate the load estimation value in the indirect parameter adaptive law. The indirect parameter adaptive law based on generalized momentum can be expressed as:

[0101]

[0102] In the formula, is the upper bound of the adaptive law estimation rate, is the saturation function, is the projection mapping function, and They are closed sets interior and boundaries. for The unit normal vector pointing outward when .

[0103] In this embodiment, in order to simplify the online calculation amount of the indirect adaptive algorithm and improve the accuracy of load estimation, only the load quality, that is, β L =m L ∈R 1×1 Secondly, the joints with less load influence are ignored, and the joints with more significant influence are used to construct the regression matrix for load estimation.

[0104] In this embodiment, the parameters of the indirect parameter adaptation law can be set to: Γ(0)=3, u=0.125,t f =0.01,ρ m =10000,

[0105] S3: Obtain an external force observer model, input the load estimation value and motion data into the external force observer model, and obtain the external force estimation value. The external force observer model can be expressed as:

[0106]

[0107] In the formula, is the load estimate of the generalized momentum, C T , G, F f They are G(q,β m ,β L ), is the abbreviation of, r is the observed value of the external torque, K 1 , K 2 and K 3 are all positive definite diagonal gain matrices of the external force observer.

[0108] In this embodiment, the positive definite diagonal matrix of the external force observer is selected as:

[0109] K 1 =diag(900,900,900,900,900,900)

[0110] K 2 =diag(1 / 15,1 / 15,1 / 15,1 / 15,1 / 15,1 / 15)

[0111] K 3 =diag(1.25 / 30,1.25 / 30,1.25 / 30,1.25 / 30,1.25 / 30,1.25 / 30)

[0112] S4: Determine whether the estimated external force value of each joint is less than a preset external force range. If the estimated external force value of any joint exceeds the external force range, it is considered that a collision has occurred. Otherwise, it is considered that no collision has occurred.

[0113] Specifically, the external force threshold r of joint i (i = 1, 2, 3, 4, 5, 6) mi Can be designed as:

[0114] r mi =τ max i +τ mari

[0115] Among them, r mi is the external force threshold corresponding to joint i, τ maxi is the maximum disturbance torque when joint i moves freely without collision, τ mari For safety margin.

[0116] When the observed external force value r exceeds the threshold range, it can be considered that a collision has occurred, that is:

[0117]

[0118] Where det(·) is the collision detection function. When the observed value of any joint i exceeds the threshold, it is considered that a collision has occurred (YES), otherwise it is considered that no collision has occurred (NO).

[0119] In this embodiment, the control process of the collision detection method based on indirect parameter adaptation is as follows: Figure 3 When a collision is detected, the six-axis robot arm must stop moving immediately. In other cases, the six-axis robot arm works normally.

[0120] In this embodiment, the threshold of each joint is selected as:

[0121] [r m1 ,r m2 ,r m3 ,r m4 ,r m5 ,r m6 ] T =[1,7,5,1,1,1] T .

[0122] Simulation Verification

[0123] The six-axis robot arm is subjected to a collision test without load, and the collision torque received by the six-axis robot arm during the collision is collected. The collision detection method of this embodiment is used to estimate the collision torque to obtain an estimated torque. The final result is as follows: Figure 4 See Figure 4 , which is a simulation result diagram of the external force estimation of the six-axis robot arm in the present embodiment under no-load condition. It can be seen from the figure that when the six-axis robot arm is not hit, the estimated torque is consistent with the collision torque. When the six-axis robot arm is hit, the estimated torque is close to the collision torque and can be used to replace the real collision torque.

[0124] When the six-axis robot arm is loaded with an object of 2.72 kg, a collision test is performed, and the collision torque received by the six-axis robot arm during the collision is collected. The collision detection method of this embodiment is used to estimate the collision torque to obtain an estimated torque, and the estimated value of the load is estimated at the same time. The final result is as follows: Figure 5 and Figure 6 See Figure 5 and Figure 6 , Figure 5 This is a simulation result diagram of the external force estimation of the six-axis robot arm of this embodiment under the condition of a load of 2.75kg; Figure 6 The following is a simulation result diagram of the load estimation of the six-axis robot arm of this embodiment when the load is 2.75 kg. Figure 5 It can be seen from the figure that after a short fluctuation, the predicted estimated torque is basically consistent with the actual collision torque, indicating that the collision detection method of this embodiment can effectively estimate the collision torque of the six-axis robot. Figure 6 It can be seen that after a short fluctuation, the predicted load estimate value tends to be consistent with the actual load value, and when a collision occurs, the predicted load estimate value is also close to the actual load value, indicating that the collision detection method of this embodiment can effectively estimate the size of the load.

[0125] To sum up, the collision detection method of this embodiment can effectively estimate the size of the load, thereby reducing the impact of load changes on external force estimation, making the external force estimation value more accurate. At the same time, a smaller threshold range can effectively realize collision detection, thereby improving the accuracy and robustness of the collision detection method, and has strong practical application value.

[0126] Example 2

[0127] Based on the indirect parameter adaptive six-axis robot collision detection method of Example 1, in order to improve the accuracy of the existing indirect parameter adaptive six-axis robot collision detection, this embodiment also provides an indirect parameter adaptive six-axis robot collision detection system, which can be controlled by the collision detection method of Example 1. Figure 7 , which is a schematic diagram of the structure of the indirect parameter adaptive six-axis robot arm collision detection system of this embodiment. The collision detection system includes an acquisition module, a load estimation module, an external force observer and a collision signal generation module.

[0128] The acquisition module is used to collect the motion data of the six-axis robot. The motion data includes the actual angle, actual angular velocity and driving torque of each joint of the six-axis robot. Specifically, the acquisition module can be electrically connected to the encoder, motor driver or sensor installed on the six-axis robot, receive the corresponding signal and convert it into the actual angle, actual angular velocity and driving torque of each corresponding joint.

[0129] The load estimation module is used to: 1. Define the generalized momentum, derive the generalized momentum and linearize the parameters to obtain the regression matrix. 2. Define the model prediction error for load estimation, and then construct an adaptive function based on the model prediction error and the regression matrix. Adaptive function τ L It can be expressed as:

[0130]

[0131] In the formula, is the adaptive regression matrix, and ε is the model prediction error.

[0132] 2. Calculate the adaptive law matrix. Adaptive law matrix Γ L The calculation is done using the following formula:

[0133]

[0134] Where u is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L (t) is the maximum eigenvalue of .

[0135] 3. Calculate the load estimate. The load estimate is calculated using the following formula:

[0136]

[0137] In the formula, is the upper bound of the adaptive law estimation rate, is the saturation function, is the projection mapping function.

[0138] The external force observer is used to calculate the external force estimate based on the motion data and the load estimate. The external force observer is expressed as:

[0139]

[0140]

[0141] In the formula, is the load estimate of the generalized momentum, C T , G, F f They are G(q,β m ,β L ), Abbreviation of is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, r is the observed value of the external torque, K 1 , K 2 and K 3 are all positive definite diagonal gain matrices of the external force observer.

[0142] The collision signal generation module is used to determine whether the external force estimation value exceeds a preset external force threshold, and if so, generates a collision signal. The external force threshold r of joint i (i = 1, 2, 3, 4, 5, 6) is mi Can be set to:

[0143] r mi =τ max i +τ mari

[0144] In the formula, r mi is the external force threshold corresponding to joint i, τ maxi is the maximum disturbance torque when joint i moves freely without collision, τ mari For safety margin.

[0145] Example 3

[0146] In order to implement the indirect parameter adaptive six-axis robot arm collision detection method of Example 1 and apply it to the existing six-axis robot arm to simplify the detection process in practical applications and improve the accuracy and robustness of collision detection, this embodiment provides an indirect parameter adaptive six-axis robot arm collision detection device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the indirect parameter adaptive six-axis robot arm collision detection method of Example 1 are implemented, thereby implementing collision detection of the existing indirect parameter adaptive six-axis robot arm and improving the accuracy and robustness of collision detection.

[0147] The computer device may be a smart phone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server or cabinet server (including an independent server or a server cluster composed of multiple servers) that can execute programs. The computer device of this embodiment includes at least but is not limited to: a memory and a processor that can communicate with each other through a system bus.

[0148] In this embodiment, the memory (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the computer device. Of course, the memory may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.

[0149] The processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data, thereby implementing the steps of the indirect parameter adaptive six-axis robot arm collision detection method as in Example 1, thereby implementing the existing indirect parameter adaptive six-axis robot arm collision detection, and improving the accuracy and robustness of collision detection.

[0150] Example 4

[0151] Based on the indirect parameter adaptive six-axis robot arm collision detection method of embodiment 1, this embodiment provides an indirect parameter adaptive six-axis robot arm control method. The control method uses the indirect parameter adaptive six-axis robot arm collision detection method of embodiment 1 to detect whether the six-axis robot arm collides, and when the six-axis robot arm collides, the six-axis robot arm is controlled to stop running. Figure 8 , which is a flow chart of the control method of the indirect parameter adaptive six-axis robotic arm of this embodiment.

[0152] In this embodiment, the steps of the control method of the six-axis robot arm with indirect parameter self-adaptation are as follows:

[0153] 1. Set the sampling period to detect the motion data of the six-axis robot in real time.

[0154] 2. Initialize the encoders at each joint to provide real-time feedback of the torque signal and angle signal of each joint.

[0155] 3. The indirect parameter adaptive six-axis robot arm collision detection method of Example 1 is used to detect whether the six-axis robot arm collides, and a collision signal is generated when a collision occurs, and the six-axis robot arm is controlled to stop moving.

[0156] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An indirect parameter adaptive six-axis robot arm collision detection method, which is used to determine whether the six-axis robot arm collides with other objects, characterized in that: The collision detection method comprises the following steps: S1: Obtain the physical model of the six-axis robot with unknown load; S2: Collecting motion data of each joint of the six-axis robotic arm, and calculating the load estimation value of the six-axis robotic arm according to the motion data; the specific calculation method of the load estimation value is as follows: S21: construct an adaptive function; the adaptive function τ L Expressed as: In the formula, is the adaptive regression matrix, ε is the model prediction error; S22: Calculate the adaptive law matrix Γ L ; The adaptive law matrix Γ L The calculation is done using the following formula: Where u is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L The maximum eigenvalue of (t), Γ L (t) is the adaptive law matrix function with respect to time t; S23: construct an indirect parameter adaptive law based on generalized momentum, take the motion data as input, and calculate the load estimation value in the indirect parameter adaptive law; the indirect parameter adaptive law is expressed as: In the formula, is the upper bound of the adaptive law estimation rate, is a saturation function, is the projection mapping function, and They are closed sets the interior and boundaries of for The unit normal vector pointing outward when ; S3: obtaining an external force observer model, inputting the load estimation value and the motion data into the external force observer model, and obtaining an external force estimation value; S4: Determine whether the estimated external force value of each joint is less than a preset external force threshold. If the estimated external force value of any joint exceeds the external force threshold, it is considered that a collision has occurred; otherwise, it is considered that no collision has occurred.

2. The indirect parameter adaptive six-axis robot arm collision detection method according to claim 1, characterized in that: In step S1, the physical model of the six-axis robot is expressed as: Where τ is the joint driving torque, and Represent the joint angle, angular velocity and angular acceleration respectively, M(q,β m ,β L ) is the inertia matrix of the robot arm, is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, β m is the system parameter of the robot body, β L is a load-related parameter, β f is the friction related parameter, τ e is the collision torque.

3. The indirect parameter adaptive six-axis robot arm collision detection method according to claim 1, characterized in that: In step S21, the method for constructing the adaptive function is as follows: S211: Define generalized momentum as: By linearizing the parameters of the generalized momentum, we obtain: In the formula, and They represent the system parameter set β of the manipulator obtained by generalized momentum. m and load parameter set β L The corresponding regression matrix; S212: Deriving the generalized momentum and linearizing the parameters, we obtain: In the formula, and They represent the system parameter set β of the manipulator obtained by the derivative of generalized momentum. m , load parameter set β L and friction parameter β f The corresponding regression matrix; S213: Order β L The estimated value of and define the model prediction error used for estimation, the model prediction error is expressed as: In the formula, They are Filter The output value after They are Filter The output value after S214: Constructing an adaptive function based on the generalized momentum according to the model prediction error.

4. The indirect parameter adaptive six-axis robot arm collision detection method according to claim 1, characterized in that: In step S3, the external force observer model is expressed as: In the formula, is the load estimate of the generalized momentum, r is the observed value of the external torque, and K1, K2, and K3 are all positive definite diagonal gain matrices of the external force observer.

5. The indirect parameter adaptive six-axis robot arm collision detection method according to claim 1, characterized in that: In step S4, the external force threshold is set to: r mi =t maxi +t mari In the formula, r mi is the external force threshold corresponding to joint i, τ maxi is the maximum disturbance torque when joint i moves freely without collision, τ mari For safety margin.

6. An indirect parameter adaptive six-axis robot arm collision detection system, which adopts the indirect parameter adaptive six-axis robot arm collision detection method according to any one of claims 1 to 5, characterized in that: The detection system comprises: An acquisition module, which is used to acquire motion data of the six-axis robotic arm; The load estimation module is used to:

1. define generalized momentum, derive the generalized momentum and linearize the parameters to obtain a regression matrix; define a model prediction error for estimating load, and then construct an adaptive function based on the model prediction error and the regression matrix; the adaptive function τ L Expressed as: In the formula, is the regression matrix, ε is the model prediction error; 2. Calculate the adaptive law matrix; the adaptive law matrix Γ L The calculation is done using the following formula: Where u is the forgetting factor, ρ m The upper bound of the update rate set to prevent load estimation from diverging, λ max is Γ L The maximum eigenvalue of (t), Γ L (t) is the adaptive law matrix function with respect to time t; 3. Calculate the load estimate; the load estimate is calculated using the following formula: In the formula, is the upper bound of the adaptive law estimation rate, is a saturation function, is the projection mapping function; An external force observer, which is used to calculate an external force estimate value based on the motion data and the load estimate value; the external force observer is expressed as: In the formula, is the load estimate of the generalized momentum, C T , G, F f They are G(q,β m ,β L ), Abbreviation of is the centrifugal force and Coriolis force matrix, G(q,β m ,β L ) is the gravity matrix, is the friction force, r is the observed value of the external torque, K1, K2 and K3 are all positive definite diagonal gain matrices of the external force observer; The collision signal generating module is used to determine whether the external force estimation value exceeds a preset external force threshold value, and if so, generate a collision signal.

7. The indirect parameter adaptive six-axis robot arm collision detection system according to claim 6, characterized in that: The motion data includes the actual angle, actual angular velocity and driving torque of each joint of the six-axis robotic arm.

8. The indirect parameter adaptive six-axis robot arm collision detection system according to claim 6, characterized in that: The external force threshold is set to: r mi =t maxi +t mari In the formula, r mi is the external force threshold corresponding to joint i, τ maxi is the maximum disturbance torque when joint i moves freely without collision, τ mari For safety margin.

9. An indirect parameter adaptive six-axis robot arm collision detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The functional modules in the collision detection device are deployed in the form of an indirect parameter adaptive six-axis robotic arm collision detection system as described in any one of claims 6 to 8. When the processor executes the computer program, the steps of the indirect parameter adaptive six-axis robotic arm collision detection method as described in any one of claims 1 to 5 are implemented to realize indirect parameter adaptive six-axis robotic arm collision detection.

10. A control method for a six-axis robot arm with indirect parameter self-adaptation, characterized in that: It uses the indirect parameter adaptive six-axis robot arm collision detection method as described in any one of claims 1 to 5 to detect whether the six-axis robot arm collides, and when the six-axis robot arm collides, controls the six-axis robot arm to stop running.

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