Control method and device of mechanical arm and storage medium

Abstract

The application provides a control method of a mechanical arm, a control device of the mechanical arm and a computer readable storage medium. The control method of the mechanical arm comprises the following steps: obtaining hair follicle data for indicating a hair follicle pose, and a control instruction issuing period and a control delay of the mechanical arm; predicting a target motion trajectory of the mechanical arm according to the hair follicle data and the control instruction issuing period and the control delay of the mechanical arm; and applying a control quantity to the mechanical arm to control the mechanical arm to move along the target motion trajectory, wherein the control quantity at least comprises a feedforward control term determined based on the control delay and the target motion trajectory. The application can be used for reducing the influence of the indefinite delay of the obtained hair follicle data and the fixed delay of the control instruction on the control of the mechanical arm by predicting the target motion trajectory of the mechanical arm, thereby improving the control precision of the mechanical arm.

Description

Technical Field

[0001] This invention relates to the field of automatic control, and more particularly to a control method for a robotic arm, a control device for a robotic arm, and a computer-readable storage medium. Background Technology

[0002] In automated hair transplant surgeries, real-time robotic arm control primarily utilizes visual sensors to acquire pose perception data of hair follicles, thereby controlling the robotic arm to track the follicles in real time and improve the accuracy and automation of the procedure. However, this control method currently faces two major technical bottlenecks. First, the acquisition of visual perception data is subject to intermittent delays, directly causing the movement trajectory of the hair follicle to be discontinuous. Second, the control commands for the robotic arm have a fixed execution delay during actual execution, resulting in a time lag between the actual movement trajectory of the robotic arm and the target trajectory. Both of these issues severely impact the real-time tracking performance.

[0003] In order to overcome the above-mentioned defects in the existing technology, there is an urgent need in the field for an improved control method for robotic arms to reduce the impact of the intermittent delay of the acquired hair follicle data and the fixed delay of the control commands on the control of the robotic arms, thereby improving the control accuracy of the robotic arms. Summary of the Invention

[0004] The following provides a brief overview of one or more aspects to offer a basic understanding of them. This overview is not an exhaustive summary of all conceived aspects, nor is it intended to identify key or decisive elements of all aspects, nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed descriptions that follow.

[0005] To overcome the aforementioned deficiencies in the prior art, the present invention provides a control method for a robotic arm, a control device for a robotic arm, and a computer-readable storage medium. By predicting the target motion trajectory of the robotic arm, the method can mitigate the impact of the intermittent delay of the acquired hair follicle data and the fixed delay of the control commands on the control of the robotic arm, thereby improving the control accuracy of the robotic arm.

[0006] Specifically, the control method for the robotic arm provided according to the first aspect of the present invention includes the following steps: acquiring hair follicle data for indicating hair follicle pose, and the issuance period and control delay of control commands for the robotic arm; predicting a target motion trajectory of the robotic arm based on the hair follicle data and the issuance period and control delay of the control commands for the robotic arm; and applying a control quantity to the robotic arm to control the robotic arm to move along the target motion trajectory. The control quantity includes at least a feedforward control term determined based on the control delay and the target motion trajectory.

[0007] Furthermore, in some embodiments of the present invention, the step of predicting the target motion trajectory of the robotic arm based on the hair follicle data and the release cycle and control delay of the control command of the robotic arm includes: performing a prediction difference on the hair follicle data to determine a first target motion trajectory whose update duration meets the release cycle of the robotic arm; and determining a second target motion trajectory that meets the requirement of lagging the control delay based on the control delay and the first target motion trajectory.

[0008] Furthermore, in some embodiments of the present invention, the step of predicting the difference in the hair follicle data to determine a first target motion trajectory whose update duration satisfies the release control command cycle of the robotic arm includes: constructing a state-space equation for the robotic arm based on the hair follicle data. The state-space equation includes a state equation and an observation equation. The state equation is:

[0009] x k =Ax k-1 +ω k

[0010]

[0011] Among them, s k v represents the first target position at time k. k Let dt represent the target velocity at time k, ω represent the period for issuing control commands from the robotic arm, and ω represent the velocity at time k. k The noise is a process noise that follows a Gaussian distribution ω. k ~N(0,Q) k ), Q k Let be the process noise covariance matrix. The observation equation is:

[0012] z n =Hx n +γ n

[0013]

[0014] Among them, S n V represents the actual target location determined based on the hair follicle data at time n. n Let t represent the actual target velocity at time n. n γ represents the time corresponding to the nth moment. n To measure the noise, which follows a Gaussian distribution γ n ~N(0,R n ), R n To measure the noise covariance matrix.

[0015] Furthermore, in some embodiments of the present invention, the step of predicting the difference in the hair follicle data to determine the first target motion trajectory whose update duration satisfies the cycle of the robotic arm's control command further includes: based on the optimal estimate x from the previous moment in the state equation. k-1|k-1 The optimal estimate x at the current moment k|k-1 The uncertainty P in making predictions and updating the predicted state k|k-1 :

[0016] x k|k-1 =Ax k-1|k-1 +ω k

[0017] P k|k-1 =AP k-1|k-1 A T

[0018] Among them, P k|k-1 To account for the uncertainty in the predicted state, and in response to the acquisition of new hair follicle data, the Kalman gain K is calculated. n Based on this, update the optimal estimate and the reduced uncertainty of the predicted state at the current moment:

[0019] x k|k =x k|k-1 +K n (z n -Hx k|k-1 )

[0020] P k|k = (1-K) n H)P k|k-1

[0021] Among them, P k|k Let I represent the updated uncertainty, and let I be the identity matrix.

[0022] Furthermore, in some embodiments of the present invention, the step of determining a second target motion trajectory that satisfies the control delay and the first target motion trajectory includes: determining the actual target speed of the robotic arm in response to acquiring new hair follicle data.

[0023]

[0024] Among them, S n t represents the hair follicle data acquired at time n. n This represents the time corresponding to the nth moment; based on the first target's motion trajectory s k Determine that the robotic arm moves at a constant linear speed to T. delay The first target displacement length after time:

[0025] Dtarget =Vel n *T delay

[0026] Among them, D target The robotic arm moves at a constant linear speed to T. delay The target displacement length after time T; and the addition of a random term to the first target displacement length to indicate the change in its actual motion speed, to determine the robotic arm's variable-speed linear motion to T. delay The second target displacement length after time:

[0027] D random =-sgn(Vel n )*ε*T delay

[0028] Among them, D random The robotic arm undergoes variable speed linear motion to T. delay The second target displacement length, ε, after time step 1 follows a normal distribution.

[0029] Furthermore, in some embodiments of the present invention, applying a control quantity to the robotic arm to control the robotic arm to move along the target motion trajectory includes: based on a first target position s at multiple times. k The first target's trajectory s and the first target's displacement length D are formed. target and the second target displacement length D random The feedforward control term is determined, and the third target trajectory controlled by the feedforward is determined accordingly: Expected_Trajectory = s + D target +D random .

[0030] Furthermore, in some embodiments of the present invention, the control quantity further includes a feedback control term determined based on the control error between the target motion trajectory and the actual motion position of the robotic arm. Applying the control quantity to the robotic arm to control its movement along the target motion trajectory further includes: obtaining the actual motion position d of the robotic arm and, in conjunction with the first target motion trajectory s, determining the control error of the robotic arm.

[0031] e = sd

[0032] Where e is the control error between the target motion trajectory and the actual motion position of the robotic arm; and based on the control error, the feedback control term is determined, and accordingly the fourth target motion trajectory after feedforward control and feedback control is determined:

[0033] Expected_Trajectory = s + Dtarget +D random +k*e

[0034] Where k is the proportionality coefficient, which ranges from 0 to 1.

[0035] Furthermore, the control device for the robotic arm provided according to a second aspect of the present invention includes a prediction module and a control module. The prediction module is used to predict the target motion trajectory of the robotic arm based on acquired hair follicle data and the issuance cycle and control delay of the control commands of the robotic arm. The control module is used to apply a control quantity to the robotic arm to control the robotic arm to move along the target motion trajectory. The control quantity includes at least a feedforward control term determined based on the control delay and the target motion trajectory.

[0036] Furthermore, in some embodiments of the present invention, the control device further includes a visual perception module for acquiring hair follicle data indicating hair follicle pose information during the control of the robotic arm.

[0037] Furthermore, according to a third aspect of the present invention, a computer-readable storage medium is provided thereon storing computer instructions. When the computer instructions are executed by a processor, a control method for a robotic arm as provided in the first aspect of the present invention is implemented. Attached Figure Description

[0038] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0039] Figure 1 A flowchart illustrating a control method for a robotic arm according to some embodiments of the present invention is shown.

[0040] Figure 2 A flowchart illustrating a control method for a robotic arm according to some embodiments of the present invention is shown. Detailed Implementation

[0041] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Although the description of the present invention is presented in conjunction with preferred embodiments, this does not mean that the features of the invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of the present invention. To provide a thorough understanding of the invention, many specific details will be included in the following description. The invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description.

[0042] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0043] Furthermore, the terms "upper," "lower," "left," "right," "top," "bottom," "horizontal," and "vertical" used in the following description should be understood as the orientations shown in the relevant paragraphs and accompanying drawings. These relative terms are for illustrative purposes only and do not imply that the described apparatus must be manufactured or operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0044] It is understood that although terms such as "first," "second," and "third" may be used herein to describe various components, regions, layers, and / or parts, these components, regions, layers, and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers, and / or parts. Therefore, the first components, regions, layers, and / or parts discussed below may be referred to as second components, regions, layers, and / or parts without departing from some embodiments of the present invention.

[0045] As mentioned above, in the automated application of hair transplant surgery, the real-time motion target robotic arm control method mainly acquires the pose perception data of hair follicles through visual sensors, and then controls the robotic arm to track the hair follicles in real time to improve the accuracy and automation level of the surgical operation. However, the current control method faces two major technical bottlenecks. On the one hand, the acquisition of visual perception data has an intermittent delay, which directly causes the movement trajectory of the hair follicle target to be discontinuous. On the other hand, the control commands of the robotic arm have a fixed execution delay during actual execution, causing the actual movement trajectory of the robotic arm to lag behind the target trajectory. Both of these problems seriously affect the real-time tracking effect.

[0046] To overcome the aforementioned deficiencies in the prior art, the present invention provides a control method for a robotic arm, a control device for a robotic arm, and a computer-readable storage medium. By predicting the target motion trajectory of the robotic arm, the method can mitigate the impact of the intermittent delay of the acquired hair follicle data and the fixed delay of the control commands on the control of the robotic arm, thereby improving the control accuracy of the robotic arm.

[0047] In some non-limiting embodiments, the control device for the robotic arm provided in the second aspect of the present invention is implemented based on the control method for the robotic arm provided in the first aspect of the present invention.

[0048] Specifically, the control device for the robotic arm provided in the first aspect of the present invention includes a prediction module and a control module. The prediction module is used to predict the target motion trajectory of the robotic arm based on acquired hair follicle data, the issuance cycle of control commands from the robotic arm, and the control delay. The control module is used to apply a control quantity to the robotic arm to control the robotic arm to move along the target motion trajectory. Here, the control quantity includes at least a feedforward control term determined based on the control delay and the target motion trajectory.

[0049] Furthermore, in some alternative embodiments, the control device for the robotic arm provided in the first aspect of the present invention further includes a vision perception module for acquiring hair follicle data indicating hair follicle pose information during the control of the robotic arm.

[0050] Furthermore, in some non-limiting embodiments, the control device for the robotic arm provided in the first aspect of the present invention includes a memory. Here, the memory includes, but is not limited to, the computer-readable storage medium provided in the third aspect, on which computer instructions are stored. The prediction module and the control module are connected to the memory and configured to execute the computer instructions stored in the memory to implement the control method for the robotic arm as provided in the first aspect of the present invention.

[0051] The working principle of the control device system of the robotic arm described above will be described below with reference to some embodiments of the control methods of the robotic arm. Those skilled in the art will understand that these embodiments of the control methods are merely non-limiting implementations provided by the present invention, intended to clearly demonstrate the main concepts of the invention and provide some specific solutions convenient for public implementation, rather than limiting all functions or all operating modes of the control device of the robotic arm. Similarly, the control device of the robotic arm is also only a non-limiting implementation provided by the present invention, and does not constitute a limitation on the executing subject and execution order of each step in these robotic arm control methods.

[0052] Please refer to Figure 1 . Figure 1 A flowchart illustrating a control method for a robotic arm according to some embodiments of the present invention is shown.

[0053] like Figure 1 As shown, the control device for the robotic arm provided in the first aspect of the present invention can first acquire hair follicle data for indicating the pose of hair follicles via a vision perception module.

[0054] In addition, the aforementioned control device can also obtain the release cycle and control delay of the control command servoj of the robotic arm based on the model of the robotic arm.

[0055] Subsequently, the control device can predict the target motion trajectory of the robotic arm through the prediction module, based on the hair follicle data and the issuance cycle and control delay of the control commands of the robotic arm.

[0056] Please refer to further information. Figure 2 . Figure 2 A flowchart illustrating a control method for a robotic arm according to some embodiments of the present invention is shown.

[0057] like Figure 2 As shown, the prediction module can first use Kalman filtering to predict the difference in hair follicle data in order to determine the first target motion trajectory whose update duration meets the release cycle of the robotic arm.

[0058] Specifically, the prediction module can construct the state-space equations of the robotic arm based on hair follicle data. The state-space equations include state equations describing the motion state of the tracked target hair follicles and observation equations describing the relationship between the state of the target hair follicles and the observed values.

[0059] Here, the state equation is:

[0060] x k =Ax k-1 +ω k

[0061]

[0062] Among them, s k v represents the first target position at time k. k Let dt represent the target velocity at time k, ω represent the cycle period for issuing control commands to the robotic arm, and ω represent the velocity at time k. k The noise is a process noise that follows a Gaussian distribution ω. k ~N(0,Q) k ), Q k The process noise covariance matrix is...

[0063] Here, the observation equation is:

[0064] z n =Hx n +γ n

[0065]

[0066] Among them, S n V represents the actual target location determined based on hair follicle data at time n. n Let t represent the actual target velocity at time n. n γ represents the time corresponding to the nth moment. n To measure the noise, which follows a Gaussian distribution γ n ~N(0,R n ), R n To measure the noise covariance matrix.

[0067] Then, the prediction module can predict and update the aforementioned state-space equations. Specifically, the prediction module can predict based on the optimal estimate x from the previous time step in the state equations. k-1|k-1 The optimal estimate x at the current moment k|k-1 The uncertainty P in making predictions and updating the predicted state k|k-1 :

[0068] x k|k-1 =Ax k-1|k-1 +ω k

[0069] P k|k-1 =AP k-1|sk-1 A T

[0070] Among them, P k|k-1 To represent the uncertainty of the predicted state, I is the identity matrix.

[0071] Next, the prediction module can determine whether the visual perception module has acquired new hair follicle data. Specifically, the controller stores hair follicle data and its corresponding acquisition time, indexing the hair follicle data by timestamp. If hair follicle data with the corresponding timestamp is not available, it is considered unacquired; if data with the required timestamp is acquired, it is considered acquired. Because the control command issuance cycle of the robotic arm is longer than the update cycle of the visual perception module acquiring hair follicle data, new hair follicle data cannot be acquired between two updates.

[0072] Subsequently, in response to acquiring new hair follicle data, the prediction module can calculate the Kalman gain K. n Based on this, update the optimal estimate and the reduced uncertainty of the predicted state at the current moment:

[0073] x k|k =x k|k-1 +K n (z n -Hx k|k-1 )

[0074] P k|k = (1-K) n H)P k|k-1

[0075] Among them, P k|k This is due to the uncertainty following the update.

[0076] Specifically, the prediction module can first initialize the Kalman filter, that is, set... This indicates the target's position S0 at time 0, obtained from the sensing information, with an initial velocity of 0, and P. 0|0 Set it as a diagonal matrix.

[0077] The prediction module can then fine-tune key parameters of the aforementioned state equation and observation equation. The parameter part includes the process noise covariance matrix Q. k And the measurement noise covariance matrix R n , where R n It is generated from hair follicle data acquired by the visual perception module. The perception error originates from the stereo vision measurement error. The known standard deviations of the stereo vision measurement of the system are δ. x ,δ y ,δ z And each dimension is independent, then Here, we assume ω k Let the variance of the speed jitter be defined as the speed jitter that is not accurately modeled in the system. but:

[0078]

[0079] Afterwards, the prediction module can be adjusted. The optimal prediction result is obtained by adjusting the magnitude of the process noise covariance. If the process noise covariance is large, the prediction result is more reliable than the measurement result. Parameters are adjusted using the statistical properties of the measurement residuals. The measurement residuals are the error obtained by subtracting the predicted measurement value from the current measurement value. Ideally, they should approximate autocorrelation-free white noise, and their variance should be consistent with the predicted measurement covariance set in the filter. During parameter tuning, the velocity noise variance is continuously changed based on the experimental data. Observe the statistical characteristics of the measurement residuals (e.g., mean square error, distribution) to make them match the corresponding covariance prediction in Kalman filtering, thereby approximating the optimal parameter range.

[0080] Then, the prediction module can determine the first target position s at multiple time points based on the adjusted parameters. k , forming the first target's trajectory s.

[0081] Thus, the control device provided in the second aspect of the present invention can obtain a first target motion trajectory based on predictive interpolation, which makes the position update time of the target hair follicle meet the control command release cycle of the robotic arm. This reduces the impact of the intermittent delay in the hair follicle data obtained based on the visual perception module, thereby avoiding a certain vacuum period in the control of the robotic arm and improving the control accuracy of the robotic arm.

[0082] Then, the prediction module can predict based on the control delay T. delay Based on the first target trajectory s mentioned above, the second target trajectory after satisfying the hysteresis control delay is determined.

[0083] Specifically, after initially acquiring hair follicle data, the prediction module can set the actual target speed of the robotic arm, Vel, to 0.

[0084] In response to acquiring new hair follicle data, the prediction module can determine the robotic arm's actual target speed:

[0085]

[0086] Among them, S n t represents the hair follicle data acquired at time n. n This represents the time corresponding to the nth moment.

[0087] Then, the prediction module can predict the trajectory of the first target based on its movement. k Determine the robotic arm to move at a constant linear speed to T. delay The first target displacement length after time:

[0088] D target =Vel n *T delay

[0089] Among them, Dtarget The robotic arm moves at a constant linear speed to T delay The length of the target displacement after time step [time].

[0090] Then, the prediction module can add a random term to the first target displacement length to indicate the actual change in its motion speed, in order to determine the extent of the robotic arm's variable-speed linear motion to T. delay The second target displacement length after time step:

[0091] D random =-sgn(Vel n )*ε*T delay

[0092] Among them, D random For the robotic arm to change speed linear motion to T delay The second target displacement length, ε, after time step 1 follows a normal distribution.

[0093] Thus, the control device provided in the second aspect of the present invention can predict the target displacement length after the control delay and add the target displacement length to the first target motion trajectory, thereby reducing the influence of the fixed control delay of the robotic arm control command, avoiding a certain lag between the actual trajectory and the expected trajectory, and thus improving the control accuracy of the robotic arm.

[0094] After that, such as Figure 2 As shown, the control device can apply control quantities to the robotic arm via the control module to control the robotic arm to move along the target motion trajectory. The control quantities include at least a feedforward control term determined based on the control delay and the target motion trajectory.

[0095] Specifically, such as Figure 2 As shown, the control module can determine the first target position s based on multiple time points. k The first target's trajectory s and the first target's displacement length D are formed. target and the second target displacement length D random The feedforward control term is determined, and the trajectory of the third target controlled by the feedforward is determined accordingly.

[0096] Expected_Trajectory = s + D target +D random .

[0097] Thus, the control device provided in the second aspect of the present invention can control the movement of the robotic arm based on the determined third target motion trajectory controlled by feedforward.

[0098] In addition, Figure 2In the illustrated embodiment, the control quantity further includes a feedback control term determined based on the control error between the target motion trajectory and the actual motion position of the robotic arm. Specifically, the control module can obtain the actual motion position d of the robotic arm and, in conjunction with the first target motion trajectory s, determine the control error of the robotic arm:

[0099] e = sd

[0100] Where e is the control error between the target motion trajectory and the actual motion position of the robotic arm.

[0101] Then, the control module can determine the feedback control term based on the control error, and accordingly determine the motion trajectory of the fourth target after feedforward control and feedback control:

[0102] Expected_Trajectory = s + D target +D random +k*e

[0103] Where k is the proportionality coefficient, which ranges from 0 to 1.

[0104] Thus, in complex actual working conditions, the control device provided by the second aspect of the present invention can correct the target position by adding feedback control terms, thereby solving the problem that simple feedforward control cannot make the robotic arm reach the specified desired position, and thus improving the control accuracy of the robotic arm.

[0105] In summary, the robotic arm control method, robotic arm control device, and computer-readable storage medium provided by the present invention can all improve the control accuracy of the robotic arm by predicting the target motion trajectory of the robotic arm, thereby reducing the impact of the intermittent delay of the acquired hair follicle data and the fixed delay of the control command on the robotic arm control.

[0106] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0107] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0108] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0109] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a robotic arm, characterized in that, Includes the following steps: Acquire hair follicle data used to indicate hair follicle pose, as well as the issuance cycle and control delay of the control commands of the robotic arm; The hair follicle data is used to predict the difference in order to determine the first target motion trajectory whose update duration meets the release cycle of the robotic arm. ; as well as In response to acquiring new hair follicle data, the actual target speed of the robotic arm is determined based on the hair follicle data and time. Based on the actual target speed, the robotic arm is determined to move at a constant linear speed to... First target displacement length after time step Displacement length towards the first target A random term is added to indicate the actual change in the robot arm's linear motion to determine the speed change of the robotic arm. The second target displacement length after time point ; Based on the first target's motion trajectory The first target displacement length and the second target displacement length The sum of these factors determines the feedforward control term, and based on this, the trajectory of the third target under feedforward control is determined. ; as well as A control quantity is applied to the robotic arm to control the robotic arm to move along the trajectory of the third target. The motion, wherein the control quantity includes at least the feedforward control term.

2. The control method as described in claim 1, characterized in that, The hair follicle data is used to predict the difference to determine a first target motion trajectory whose update duration satisfies the robotic arm's control command cycle. The steps include: Based on the hair follicle data, the state-space equation of the robotic arm is constructed, wherein the state-space equation includes a state equation and an observation equation. The state equation is: in, express The first target position at any time express The target speed at any given moment This indicates the cycle in which control commands are issued by the robotic arm. This is process noise, which follows a Gaussian distribution. , The process noise covariance matrix is ​​the trajectory of the first target. It consists of the first target position at multiple moments. composition, The observation equation is: in, express The actual target location determined based on the hair follicle data at any given time. express The actual target speed at any given moment, Indicates the first The time corresponding to each moment To measure the noise, it follows a Gaussian distribution. , To measure the noise covariance matrix.

3. The control method as described in claim 2, characterized in that, The hair follicle data is used to predict the difference to determine a first target motion trajectory whose update duration satisfies the robotic arm's control command cycle. The steps also include: Based on the optimal estimate of the previous time step in the state equation The optimal estimate for the current moment Uncertainty in making predictions and updating the predicted state : in, To predict the uncertainty of the state; and In response to acquiring new hair follicle data, the Kalman gain is calculated. Based on this, update the optimal estimate and the reduced uncertainty of the predicted state at the current moment: in, Due to the uncertainty following the update, It is an identity matrix.

4. The control method as described in claim 1, characterized in that, In response to acquiring new hair follicle data, the actual target speed of the robotic arm is determined based on the hair follicle data and time. The steps are achieved through the following formula: in, Indicates the first Real-time acquisition of hair follicle data, Indicates the first The time corresponding to each moment Based on the actual target speed, the robotic arm is determined to move at a constant linear speed to... First target displacement length after time step : The addition of a random term to the first target displacement length to indicate changes in its actual speed is used to determine the extent to which the robotic arm's linear motion changes speed. The second target displacement length after time point The steps are achieved through the following formula: in, It follows a normal distribution. .

5. The control method as described in claim 4, characterized in that, The first target motion trajectory The first target displacement length and the second target displacement length The sum of these terms determines the feedforward control term, and based on this, the trajectory of the third target under feedforward control is determined. The steps are achieved through the following formula: 。 6. The control method as described in claim 1, characterized in that, The control quantity also includes a feedback control term determined based on the control error between the target motion trajectory and the actual motion position of the robotic arm. Applying the control quantity to the robotic arm to control the robotic arm to move along the fourth target motion trajectory includes: Obtain the actual movement position of the robotic arm The first target motion trajectory, combined with the update duration, satisfies the release cycle of the robotic arm. Determine the control error between the first target motion trajectory and the actual motion position of the robotic arm. : According to the control error The feedback control term is determined, and the fourth target motion trajectory, after feedforward and feedback control, is determined accordingly. : in, This is a proportionality coefficient, ranging from 0 to 1. The first target displacement length, The second target displacement length.

7. A control device for a robotic arm, characterized in that, include: The prediction module is configured to: acquire hair follicle data for indicating hair follicle pose, as well as the issuance cycle and control delay of the control commands of the robotic arm; The hair follicle data is used to predict the difference in order to determine the first target motion trajectory whose update duration meets the release cycle of the robotic arm. ; In response to acquiring new hair follicle data, the actual target speed of the robotic arm is determined based on the hair follicle data and time; based on the actual target speed, the robotic arm is determined to move at a constant linear speed to... First target displacement length after time step Displacement length towards the first target A random term is added to indicate the actual change in the robot arm's linear motion to determine the speed change of the robotic arm. The second target displacement length after time point ; as well as The control module is configured to: based on the motion trajectory of the first target The first target displacement length and the second target displacement length The sum of these factors determines the feedforward control term, and based on this, the trajectory of the third target under feedforward control is determined. ; and apply control signals to the robotic arm to control the robotic arm to move along the trajectory of the third target. The motion, wherein the control quantity includes at least the feedforward control term.

8. The control device as described in claim 7, characterized in that, Also includes: The visual perception module is used to acquire hair follicle data that indicates the pose information of hair follicles during the control of the robotic arm.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, the control method of the robotic arm as described in any one of claims 1 to 6 is implemented.

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