CN114952939A - Collaborative robot collision detection method and system based on dynamic threshold - Google Patents
Collaborative robot collision detection method and system based on dynamic threshold Download PDFInfo
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
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- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1682—Dual arm manipulator; Coordination of several manipulators
Abstract
The invention relates to a method and a system for detecting collision of a cooperative robot based on a dynamic threshold, wherein the method comprises the following steps: establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer; determining a time-varying threshold boundary of the generalized momentum observer residual error according to the time-varying equation and the friction commutation error; identifying dynamic threshold parameters of the generalized momentum observer according to the time-varying threshold boundary; and acquiring residual error output of the generalized momentum observer in real time, calculating a dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value. The dynamic threshold method provided by the invention can change along with the error of the observer, improves the reliability and sensitivity of collision detection, particularly can avoid the missing report of collision detection, and further improves the safety performance in man-machine cooperation.
Description
Technical Field
The invention belongs to the technical field of robot automation control, and particularly relates to a collaborative robot collision detection method and system based on a dynamic threshold.
Background
Collision detection is used as a core content in man-machine cooperation, and domestic and foreign scholars propose various detection methods, and can be divided into two types according to whether external sensors are included or not. KWan et al utilizes a laser radar and a 3D scanner to model a working environment in real time, and compared with a depth camera image modeling mode, the method avoids the problem of image projection but is time-consuming in a dynamic modeling task. The Bosch robot covers a capacitance type electronic skin on the surface of a connecting rod, and can feed back human-computer contact to a control system in a tactile manner. However, it is considered that external sensors are generally expensive and complicated wiring methods affect the robot configuration. Therefore, people prefer a technical route without an external sensor in practical application, and a collision observer is established depending on model identification information, so that economic and efficient collision detection is realized.
The early collision observer takes the joint motion information of the robot as input, obtains the joint theoretical driving moment, compares the joint theoretical driving moment with the sampling driving moment, and judges whether collision occurs. However, this method requires joint acceleration information and therefore has a certain amount of noise and delay. And the land economy people provide a collision detection algorithm based on an energy deviation observer from the perspective of an energy method. The robot system is regarded as a whole, and when external collision occurs, the work of the motor is increased, so that energy is changed. The output torque and the corresponding displacement during collision are monitored, so that the calculation of the acceleration is avoided, and a control block diagram is shown in figure 1. Because the detection process needs to be conducted on energy so as to generate time delay, PD control is added in a forward channel, and oscillation after collision is inhibited through a second-order system, so that the response speed is improved, and the overshoot is reduced.
In addition to the energy analysis, Deluca et al propose a collision detection based on a generalized momentum observer from the viewpoint of momentum detection. The method relies on a dynamic model to realize the establishment of a first-order low-pass filter, and the magnitude of the external impact force can be estimated only by inputting information such as joint speed, displacement, current and the like. The method is proved to be a mature and complete framework at present and is applied to online detection of collision of the cooperative robot. The method is based on the self-interference cancellation technology, and further provides an extended state observer based on the self-interference cancellation technology, aiming at the fact that external environment disturbance exists in the robot during operation and is inspired by the radio frequency interference cancellation technology in the communication field. By utilizing the observation difference of the front-end observer and the rear-end extended state observer in the process of detecting the external disturbance signal, the modeling error and the external collision are respectively regarded as the internal disturbance and the external disturbance, and then the external disturbance separation is completed to realize the collision detection, and the working principle is shown in fig. 2. However, the observer cannot estimate the actual moment when detecting disturbance, so that the observer is suitable for application occasions only needing to judge collision and not needing to accurately estimate the moment.
Due to the successful application of the generalized momentum observer in collision detection, subsequent scholars have made many derived research discussions based on this. Alexandros utilizes a Hann window to carry out fast Fourier transform on a current signal, notices that different contact with a robot can generate different frequency signals, and provides a method for distinguishing human-computer contact types based on frequency domain analysis by combining the research of active dragging and accidental collision of a predecessor in human-computer cooperation. Based on a frequency domain analysis method, the Lixijing is refined and perfected, and a virtual sensor is provided for detecting and classifying human-computer collision. The virtual sensor comprises two filters of low-pass and band-pass, and the principle flow is shown in fig. 3. And (3) completing collision detection by using a low-pass filter, then designing a band-pass filter based on current frequency domain distribution characteristics, filtering low-frequency contact signals and high-frequency noise in the band-pass filter, and observing only frequency signals of accidental collision. The collision torque is accurately estimated by comparing the output signals of the two observers, so that the type distinction of human-computer collision is completed, and the method has stronger practical application significance.
In summary, the common collision detection methods are summarized in the following table (table 1):
compared with a joint moment observation method, the momentum observer can observe external collision moment without an external sensor, but cannot effectively identify the collision type. The virtual sensor can effectively distinguish the collision type by outputting the virtual torque amplitude to observe based on the current frequency information, but has the defect that the size of the collision torque cannot be detected. Therefore, in the actual use process, the observation method needs to be improved, so as to complete the human-computer collision detection and the type recognition.
Disclosure of Invention
In order to avoid collision under-reporting of fixed threshold detection and improve the detection sensitivity of a disturbance observer, the invention provides a cooperative robot collision detection method based on a dynamic threshold in a first aspect, which comprises the following steps: establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer; determining a time-varying threshold boundary of the generalized momentum observer residual error according to the time-varying equation and the friction commutation error; identifying dynamic threshold parameters of the generalized momentum observer according to the time-varying threshold boundary; and acquiring residual error output of the generalized momentum observer in real time, calculating a dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value.
In a second aspect of the present invention, a cooperative robot collision detection system based on dynamic threshold is provided, including: the first determining module is used for establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model and determining a time-varying equation of a residual error of the generalized momentum observer; the second determination module is used for determining a time-varying threshold boundary of the generalized momentum observer residual error according to the time-varying equation and the friction commutation error; the identification module is used for identifying the dynamic threshold parameter of the generalized momentum observer according to the time-varying threshold boundary; and the judging module is used for acquiring the residual error output of the generalized momentum observer in real time, calculating the dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value.
In a third aspect of the present invention, there is provided an electronic device comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for cooperative robot collision detection based on dynamic threshold provided by the present invention in the first aspect.
In a fourth aspect of the present invention, a computer readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for cooperative robot collision detection based on dynamic threshold values provided in the first aspect of the present invention.
The invention has the beneficial effects that:
1. considering the friction commutation error when the speed crosses zero, and taking the factor as a compensation term to participate in the boundary determination of the dynamic threshold; the dynamic threshold is output by the real-time follow observer, so that the phenomenon that the overall threshold is excessively amplified due to local peaks such as reversing friction and the like is avoided, and the reliability and the sensitivity of collision detection are improved;
2. compared with the existing collision detection technology based on a fixed threshold, the output threshold of the dynamic threshold method can change along with the error of the observer, especially the missing report of collision detection can be avoided, and the safety guarantee performance in man-machine cooperation is improved.
Drawings
FIG. 1 is a block diagram of a collision detection control based on an energy method in the prior art;
FIG. 2 is a block diagram illustrating an extended state observation based on self-interference cancellation in the prior art;
FIG. 3 is a block diagram of a prior art virtual sensor based on frequency domain analysis;
FIG. 4 is a basic flow diagram of a method of cooperative robot collision detection based on dynamic thresholds in some embodiments of the present invention;
FIG. 5 is a schematic illustration of fixed threshold and dynamic threshold detection in some embodiments of the invention;
FIG. 6 is a graph of a comparison of the response of different gain factors Kr in some embodiments of the invention;
FIG. 7 is a graphical representation of observer output versus dynamic threshold change in the absence of a collision in some embodiments of the invention;
FIG. 8 is a waveform diagram of a disturbance observer fixed threshold detection in the prior art;
FIG. 9 is a waveform diagram of disturbance observer dynamic threshold detection in some embodiments of the invention;
FIG. 10 is a schematic diagram of a cooperative robot collision detection system based on dynamic thresholds in some embodiments of the present invention;
fig. 11 is a schematic structural diagram of an electronic device in some embodiments of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 4 and 5, in a first aspect of the present invention, there is provided a cooperative robot collision detection method based on dynamic threshold, including: s100, establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer; s200, determining a time-varying threshold boundary of the residual error of the generalized momentum observer according to the time-varying equation and the friction commutation error; s300, identifying dynamic threshold parameters of the generalized momentum observer according to the time-varying threshold boundary; s400, acquiring residual error output of the comparative generalized momentum observer in real time, calculating a dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value.
In step S100 in some embodiments of the present invention, the step of establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer comprises the following steps:
due to errors in modeling, identification, and sampling data, the actual generalized momentum observer residual can be expressed as:
from the laplace transform one can derive:
wherein r, τ and Z, P, f respectively represent the output, torque, position coordinates, generalized momentum and friction of the generalized momentum observer;
representing the actual values obtained by calculation or sampling corresponding to r, tau and Z, P, wherein r is convolution operation; q represents a joint position parameter, and k represents a gain coefficient; ε represents the filter coefficient, t represents time; tau is m Representing the drive torque of the joint.
Further, the theoretical output and actual output error of the observer can be defined as follows:
using a robot dynamics model, formula (2) is substituted into equation (3) to obtain:
in step S200 in some embodiments of the present invention, said determining a time-varying threshold boundary of a generalized momentum observer residual from said time-varying equation and a friction commutation error comprises: s201, separating the time-varying equation into a first error term and a second error term based on parameter sets extracted by a robot dynamic model and a nonlinear friction model; wherein: the first error term is a dynamic model error term, and the second error term is a nonlinear friction model term;
specifically, a dynamic modeling error, a friction error are respectively defined according to equation (4):
s202, correcting the second error term according to the friction reversing error; specifically, when no external force is input, the modeling error in equation (5) is eliminated, and the available observer residual output is:
since the parameter set extracted by the robot dynamics model and the nonlinear friction model can visually represent the error during modeling, the parameter set can be approximately represented by formula (5):
wherein λ is H ,λ K As a diagonal matrix of scale coefficients, δ H (t),δ f (t) is a compensation term, keeping both sides of the equation true. The error in the friction portion is largely due to model fitting, especially the commutation error at the zero crossing of the speed is the most. Therefore, the friction modeling error can be adjusted to the following form, with emphasis on compensating the friction commutation error near the zero point of speed:
in the formula (I), the compound is shown in the specification,
for friction commutation error compensation, f c I.e. friction model parameters, beta is the exponential order, and the constant value is taken to be 3 herein. At this time, the friction modeling error may be expressed as:
and S203, determining a time-varying threshold boundary of the residual error of the generalized momentum observer by taking the compensation amount in the first error term and the second error term as a constraint. Specifically, with respect to the output error of the observer model, the time-varying threshold boundary Lim of the output is set by equation (6):
the left and right shifting scaling of the formula can obtain the upper and lower threshold limits output by the observer as follows:
in the formula, L r Is a lower threshold, U r Is the upper threshold bound, δ HK Is delta H (t),δ K (t) maximum constant after merging of threshold boundaries.
In step S300 in some embodiments of the present invention, generalized momentum observer dynamic threshold parameters are identified according to the time-varying threshold boundaries. Specifically, the coefficients of the dynamic threshold can be identified from the fitted residuals output by the model. Due to the fact that in the model
Are known, and therefore the coefficient (parameter) lambda can be obtained by least square method identification H ,λ K And constructing a solving formula as follows:
as to δ HK The boundary of the constant term can be determined by the maximum error in least square identification, so as to complete the setting of the dynamic threshold.
In step S400 in some embodiments of the present invention, the residual output of the generalized momentum observer is obtained in real time, and the dynamic threshold parameter value is calculated according to the residual output, and whether the cooperative robot collides is determined according to the dynamic threshold parameter value.
The invention calculates and verifies the error inclusion of the dynamic threshold obtained by the method when the joint speed crosses zero. Specifically, based on the transfer function of the robot dynamics model, the momentum-based disturbance observer model is constructed, and the setting of the gain coefficient Kr affects the output precision and the response speed of the disturbance observer, so that the influence of different Kr values on the model is tested. Firstly, a disturbance observer is built based on Simklin, then, kinematic data are imported by taking a static model of the robot when the robot has zero gravity as a reference, square wave external torque with the amplitude of 10N · m and the period of 0.17s is imported, three groups of gain coefficients Kr are respectively set, namely K1-100, K2-500 and K3-1000, and the results are compared with original signals, and are shown in FIG. 6.
From the analysis of fig. 6, it can be seen that different gain coefficients of the disturbance observer eventually respond to the external torque disturbance near the actual amplitude, but the signal response speeds of K2 and K3 are much faster than the coefficient K1, which indicates that for the first-order transfer function model, a higher cut-off frequency can achieve a faster response speed. However, considering the low-pass filtering performance of the observer, an excessively high cut-off frequency may cause unnecessary noise, thereby deteriorating the observation result. Considering that K2 and K3 have relatively close response speeds, the gain coefficient Kr is generally set to 500 in order to balance the fast response and the noise reduction capability of the observer.
After the basic parameters of the disturbance observer are determined, the disturbance observer can be deployed to the actual motion process of the cooperative robot. Method for checking model output error of observer when no collision occurs, comparing with proposed dynamic threshold value and verifying dynamic threshold valueThe ability to track output errors. Taking the J2 joint of the Takou robot as an example, the output error of the observer is recorded, and the threshold coefficient is identified by using the least square method to obtain lambda H ＝0.1523，λ K 1.5021, the constant δ is adjusted HK ＝10.0760N·m，
The index coefficient in (1) is the same as the friction identification parameter.
Substituting the threshold coefficient into the dynamic threshold upper and lower bounds L r ,U r Fig. 7 shows the relationship between the observer output and the dynamic threshold change in the case of no collision. It can be seen that the observer does fluctuate its output value in the absence of collision, and that significant output spikes occur when the joint velocity commutes due to differences in the friction model fit. Passing through dynamic threshold
The exponential form of the term tracks errors, so that the upper limit of the threshold near the zero point of the speed can be effectively enlarged, the dynamic time variation of the threshold is realized, and the detection performance of the observer is improved.
Furthermore, compared with the output of the observer when the observer has collision or not, the fact that the experimental group has two times of obvious output torque changes near 5s and 11s can be found, which means that the external collision torque is detected at the moment, and the observation capability of the disturbance observer on the external torque is shown. Meanwhile, the observer can detect a reversing error no matter whether collision occurs or not, and the reversing error can be effectively avoided by setting a threshold value.
The generalized momentum observer is used as an external moment perception tool in collision detection, is influenced by links such as identification and data sampling in the modeling process, and the output of the generalized momentum observer fluctuates near a true value. By setting a reasonable threshold value to filter residual disturbance, collision detection can be realized. The fixed threshold value is obtained by enabling each joint of the robot to run for a long enough time without collision and increasing the maximum value of each axis disturbance by beta fix Multiplying to obtain a fixed threshold value, taking beta fix 20%, the thresholds are shown in the table below:
TABLE 2 Collision detection fixed threshold parameter
The robot is made to run a preset working track, and collision detection conditions of the cooperative robots 5s and 11s are checked by a fixed threshold method of the disturbance observer, as shown in fig. 8. When the observer output is greater than a fixed threshold, a collision is deemed to be detected. When two times of collisions act on the robot body, the collision torque is small in the J6 joint due to the fact that the moment arm of the J6 is small, and therefore the collision torque is mainly expressed in the J1-J5 joints.
As can be seen from fig. 8, when the fixed threshold algorithm is used, the joints such as J2-J4 detect two collisions, while the joints such as J1 and J5 detect only one collision, and the collision at the time of T1 is missed. The reason is analyzed, and the collision torque is found when the reversing error of the J1 and J5 joints is larger than T1, so that the fixed threshold is larger than T1 collision torque, and the report is missed. If the fixed threshold is reduced in order to detect the collision, the commutation error is larger than the fixed threshold, and a 'false alarm' condition occurs. In order to improve the situations of missing report and false report during collision detection and improve the effectiveness and sensitivity of collision detection, a dynamic threshold method is necessary to be introduced.
Similar to the acquisition of the fixed threshold value, the robot is also enabled to operate in the state without external collision, the residual error output of the observer is recorded, and the residual error output is substituted into the formula (12) to carry out least square solution to obtain a dynamic threshold value parameter lambda H ,λ K . Determining a boundary parameter delta according to the maximum error value of the residual error fitting HK The threshold parameters for each axis are obtained as shown in table 3:
TABLE 3 Collision detection dynamic threshold parameter
Deploying the dynamic threshold into the same collision output, looking at the dynamic threshold detection of the disturbance observer is shown in fig. 9. It can be seen that when the dynamic threshold algorithm is used, the two collisions of J1-J5 can be accurately detected, and no report missing occurs. The fixed threshold and dynamic threshold detection times and results were compared and are detailed in table 4.
TABLE 4 fixed threshold and dynamic threshold detection time comparison
As can be seen from Table 4, the dynamic threshold is advanced by 2-3 detection cycles compared with the fixed threshold detection method, and the false alarm condition of the fixed threshold does not occur in J1 and J5 at the time T1. Comparing table 2 and table 3, it can be seen that the minimum collision torques that can be detected by the dynamic threshold are all lower than the fixed threshold method, and the detection sensitivity of the minimum torques of each axis can be improved by about 61% on average. In conclusion, the output threshold of the dynamic threshold method can be changed along with the error of the observer, so that the method has advantages in the reliability and sensitivity of collision detection, particularly can avoid the missing report of collision detection, and improves the safety guarantee performance in human-computer cooperation.
Example 2
Referring to fig. 10, in a second aspect of the present invention, there is provided a cooperative robot collision detection system 1 based on dynamic threshold, comprising: the first determining module 11 is used for establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer; the second determining module 12 is configured to determine a time-varying threshold boundary of the generalized momentum observer residual according to the time-varying equation and the friction commutation error; the identification module 13 is used for identifying the dynamic threshold parameter of the generalized momentum observer according to the time-varying threshold boundary; and the judging module 14 is used for acquiring the residual error output of the generalized momentum observer in real time, calculating the dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides according to the dynamic threshold parameter value.
Further, the second determining module comprises: the separation unit is used for separating the time-varying equation into a first error term and a second error term based on parameter sets extracted by a robot dynamic model and a nonlinear friction model; wherein: the first error term is a dynamic model error term, and the second error term is a nonlinear friction model term; a correction unit for correcting the second error term according to a friction commutation error; and the determining unit is used for determining the time-varying threshold boundary of the generalized momentum observer residual by taking the compensation amount in the first error term and the second error term as constraint.
Example 3
Referring to fig. 11, in a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of the invention in the first aspect.
The electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 11 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 11 may represent one device or may represent a plurality of devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A collaborative robot collision detection method based on dynamic threshold is characterized by comprising the following steps:
establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model, and determining a time-varying equation of a residual error of the generalized momentum observer;
determining a time-varying threshold boundary of the generalized momentum observer residual error according to the time-varying equation and the friction commutation error;
identifying dynamic threshold parameters of the generalized momentum observer according to the time-varying threshold boundary;
and acquiring residual error output of the generalized momentum observer in real time, calculating a dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value.
2. The method of claim 1, wherein determining the time-varying threshold boundary of the generalized momentum observer residual based on the time-varying equation and the friction commutation error comprises:
separating the time-varying equation into a first error term and a second error term based on parameter sets extracted by a robot dynamics model and a nonlinear friction model; wherein: the first error term is a dynamic model error term, and the second error term is a nonlinear friction model term;
correcting the second error term according to a friction commutation error;
and determining the time-varying threshold boundary of the generalized momentum observer residual by taking the compensation amount in the first error term and the second error term as a constraint.
3. The dynamic threshold based cooperative robot collision detection method of claim 2, wherein the friction commutation error is expressed as:
wherein the content of the first and second substances,
for friction commutation error compensation, f c I.e. friction model parameters, beta is the exponential order; the second error term is expressed as:
wherein, K e (t) represents a second error term, λ K As a diagonal matrix of scale coefficients, δ k And (t) is a time-varying compensation term of the nonlinear friction model.
4. The method of claim 2, wherein determining the time-varying threshold boundary of the generalized momentum observer residual using the compensation amount in the first error term and the second error term as a constraint comprises:
taking the maximum value of the compensation quantity in the first error term and the second error term as an inequality constraint term of a time-varying threshold value of the generalized momentum observer residual error;
and determining the upper bound and the lower bound of the time-varying threshold of the generalized momentum observer residual error according to the inequality constraint term and the time-varying equation.
5. A method as claimed in claim 1, wherein the identifying generalized momentum observer dynamic threshold parameters from the time-varying threshold boundary comprises: and identifying dynamic threshold parameters of the generalized momentum observer by a least square method and a time-varying threshold boundary.
6. A method for collaborative robot collision detection based on dynamic thresholds according to any of the claims 1 to 5, characterized in that the time-varying equation is expressed as:
wherein r, τ and Z, P, f respectively represent the output, torque, position coordinates, generalized momentum and friction of the generalized momentum observer;
representing the actual values obtained by calculation or sampling corresponding to r, tau and Z, P, wherein the actual values are convolution operation; q represents a joint position parameter, and k represents a gain coefficient; ε represents the filter coefficient and t represents time.
7. A collaborative robotic collision detection system based on dynamic thresholds, comprising:
the first determination module is used for establishing a generalized momentum observer according to a robot dynamic model and a nonlinear friction model and determining a time-varying equation of a residual error of the generalized momentum observer;
the second determination module is used for determining a time-varying threshold boundary of the generalized momentum observer residual error according to the time-varying equation and the friction reversing error;
the identification module is used for identifying the dynamic threshold parameter of the generalized momentum observer according to the time-varying threshold boundary;
and the judging module is used for acquiring the residual error output of the generalized momentum observer in real time, calculating the dynamic threshold parameter value according to the residual error output, and judging whether the cooperative robot collides or not according to the dynamic threshold parameter value.
8. The collaborative robotic collision detection system in accordance with dynamic thresholds according to claim 7, characterized in that the second determination module comprises:
the separation unit is used for separating the time-varying equation into a first error term and a second error term based on parameter sets extracted by a robot dynamic model and a nonlinear friction model; wherein: the first error term is a dynamic model error term, and the second error term is a nonlinear friction model term;
a correction unit for correcting the second error term according to a friction commutation error;
and the determining unit is used for determining the time-varying threshold boundary of the generalized momentum observer residual by taking the compensation amount in the first error term and the second error term as constraint.
9. An electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method of cooperative robot collision detection based on dynamic thresholds as claimed in any one of claims 1 to 6.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, is adapted to carry out a method for collaborative robotic collision detection based on dynamic thresholds according to any of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115889079A (en) * | 2022-10-31 | 2023-04-04 | 中国电器科学研究院股份有限公司 | Double-arm gluing robot with humanoid working mode |
| CN116330280A (en) * | 2023-01-16 | 2023-06-27 | 苏州艾利特机器人有限公司 | Robot collision detection method, device, equipment and medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115889079A (en) * | 2022-10-31 | 2023-04-04 | 中国电器科学研究院股份有限公司 | Double-arm gluing robot with humanoid working mode |
| CN116330280A (en) * | 2023-01-16 | 2023-06-27 | 苏州艾利特机器人有限公司 | Robot collision detection method, device, equipment and medium |
| CN116330280B (en) * | 2023-01-16 | 2024-03-12 | 苏州艾利特机器人有限公司 | Robot collision detection method, device, equipment and medium |
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