CN108107882B - Automatic calibration and detection system of service robot based on optical motion tracking - Google Patents
Automatic calibration and detection system of service robot based on optical motion tracking Download PDFInfo
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Abstract
The invention discloses an automatic calibration and detection system of a service robot based on optical motion tracking, which realizes automatic calibration and detection of the robot, can reduce manual participation in the calibration and detection process of the service robot and improve the efficiency. The system has very good universality, and for different robots, the system can be conveniently used only by inputting corresponding parameter models and set calibration actions.
Description
Technical Field
The invention relates to the field of automatic calibration and detection of service robots, in particular to an automatic calibration and detection system of a service robot based on optical motion tracking.
Background
The process of adjusting the kinematic or kinetic parameters of a robot is generally called robot calibration. Due to errors generated in the machining and assembling processes of robot parts or abrasion caused in the using process, the difference exists between the actual value and the nominal value of the robot model parameter, and the existence of the model error can cause errors of subsequent behavior decision and motion control. Therefore, before the robot leaves a factory or works, the robot is detected and calibrated to a certain degree so as to improve the control precision and ensure the normal operation of the robot.
The current calibration method mainly comprises external calibration and self-calibration. Self-calibration mainly utilizes self redundant sensor information of the robot to establish a limiting relation between model parameters, and then solves and optimizes the parameters. The method has the advantages that no external measuring equipment is required to be added, but the method is limited by the error and the locality of the information of the sensor, and the accuracy of the calibration result is limited generally. The external calibration method obtains an actual measurement value of a device to be calibrated by using external measurement equipment, calculates an expected value by using a parameter model, and solves and obtains an actual value of a model parameter by comparing errors between the minimized measurement value and the expected value. The external equipment can obtain global information with small errors, so that the result of the external calibration method is accurate. However, due to the difference of the external measuring tools, the corresponding calibration process is different. In addition, the existing external calibration method often needs manual parameter measurement or manual intervention in the calibration process, and cannot automatically complete the detection and calibration of the robot, so that the efficiency is low.
In addition, the structure of the robot system in actual operation can be changed continuously due to mechanical wear or external force action, so that the performance of the robot is reduced. Therefore, the performance detection and the parameter recalibration of the robot system are required to be carried out regularly. The performance detection can monitor the performance change of the system, and continuously recalibrate and calibrate the parameters of the robot system based on the performance change, so that the robot system can adapt to the change of a system mechanism. Meanwhile, the result of the performance detection provides a diagnosis basis for the robot system, and defects or defective parts in the system design can be found, so that the actual performance of the robot system can meet the design requirement.
Service robots are a large category of robots, and although the design purpose and application occasion of the service robots are different from those of conventional industrial robots, the service robots themselves are still an extremely complex set of electromechanical mechanisms. A typical service robot generally has modules for sensing, decision-making, control, movement, operation, etc. The proper operation and cooperation of these modules depends on numerous system parameters. And these system parameters need to be obtained through a detection and calibration process.
Meanwhile, the application and popularization of the service robot have certain characteristics: (1) the number of demands is huge: with the increase of labor cost, the social demand on the service robots is continuously improved, the number of the service robots may be increased explosively, and the efficiency of detection and calibration links in the production process of the service robots corresponding to the service robots is generally low at present, so that the huge application demand of the service robots cannot be met. (2) The price cost is low: due to the limited ability of the general public to bear the cost of the service robot, the processing and assembly of parts usually cannot adopt expensive high-precision technology, and therefore, the calibration and adjustment of parameters and performance detection in the later production stage are more depended on. The application characteristics of the service robot increase the requirements on a calibration detection system with high automation degree and good universality in the production, manufacturing and using processes.
However, the conventional calibration and detection system requires a relevant person to have rich background knowledge and experience, is cumbersome to operate, and depends heavily on manual participation, so that the mass production and manufacturing of the service robot cannot be satisfied.
Disclosure of Invention
The invention aims to provide an automatic calibration and detection system of a service robot based on optical motion tracking, which realizes the automation of calibration and detection of the service robot and improves the efficiency.
The purpose of the invention is realized by the following technical scheme:
an automatic calibration and detection system of a service robot based on optical motion tracking comprises:
the optical motion tracking system MCS is used as an external measuring tool and can track the pose of the service robot to be calibrated, and the measured data is used as external measuring data and transmitted to the calibration server;
the service robot to be calibrated is used for operating according to a preset mode, recording related data through the internal sensor, and transmitting the recorded data serving as internal measurement data to the calibration server;
the modeling unit is used for establishing a kinematic model of the service robot to be calibrated and transmitting the kinematic model to the calibration server; the variables described by the kinematic model include: the method comprises the following steps of (1) calibrating parameters to be calibrated, variables containing internal measurement data of the service robot, variables containing external measurement data of the service robot and functional relations among the variables;
the calibration server is used for collecting external measurement data and internal measurement data, calculating an expected value through the relation between the internal measurement data and the model, and determining a parameter to be calibrated in the kinematic model by minimizing the sum of squares of errors between the expected value and an actual measurement value;
the performance detection unit is used for updating the model parameters of the service robot by utilizing the motion module with the determined parameters to be calibrated; and then, comparing errors between the updated expected value of the model and external measurement data, and detecting whether the calibration result meets the detection requirement.
The calibration server also comprises: an MCS Bridge unit, an NTP time synchronization unit and an ROS Bridge unit;
the MCS Bridge unit is used for converting the format of external measurement data sent by the MCS system into the format required by the calibration server;
the ROS Bridge unit is used for converting the format of the internal measurement data sent by the service robot into the format required by the calibration server;
and the NTP time synchronization unit is used for taking charge of clock synchronization between the MCS system and the service robot.
The data recorded by the service robot to be calibrated are transmitted to the calibration server as internal measurement data, and the method comprises the following steps:
if the internal measurement data volume exceeds the threshold value, the data volume is considered to be large, and the internal measurement data is stored in an offline data unit in the service robot to be calibrated; and if the internal measurement data quantity does not exceed the threshold value, the data quantity is considered to be small, and the internal measurement data is transmitted to an ROS Bridge unit in the calibration server through the network.
The parameter calibration problem is described as:
y＝f(q,x,p)；
wherein q is internal measurement data, x is external measurement data, p is a parameter to be calibrated in the kinematic model, and f is a function relation symbol;
the above formula is a non-thread equation, and the parameter to be calibrated is obtained by using a nonlinear solving method;
alternatively, it is linearized by differentiation to yield:
Δy(q,x,p)＝Ψ(q,x,p)·Δp；
in the above formula, Δ p ═ pr-p，prIs a nominal value, Δ y is a function variation value, Ψ (q, x, y) is a Jacobian matrix for the variable p;
and then the parameters to be calibrated are obtained by solving the above formula.
According to the technical scheme provided by the invention, the automatic calibration and detection of the robot are realized, the manual participation in the calibration and detection process of the service robot can be reduced by using the system, and the efficiency is improved. The system has very good universality, and for different robots, the system can be conveniently used only by inputting corresponding parameter models and set calibration actions.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic diagram of an automatic calibration and detection system of a service robot based on optical motion tracking according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a working process of an automatic calibration and detection system of a service robot based on optical motion tracking according to an embodiment of the present invention;
fig. 3 is a schematic view of an operation model of a service robot according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic diagram of an automatic calibration and detection system of a service robot based on optical motion tracking according to an embodiment of the present invention; as shown in fig. 1, the system mainly includes: the system comprises an optical motion tracking system (MCS), a service robot to be calibrated, a modeling unit, a calibration server and a performance detection unit; wherein:
the MCS system is used as an external measuring tool and can track the pose of the service robot to be calibrated, and the measured data is used as external measuring data and transmitted to the calibration server;
the service robot to be calibrated is used for operating according to a preset mode, recording related data through the internal sensor, and transmitting the recorded data serving as internal measurement data to the calibration server;
the modeling unit is used for establishing a kinematic model of the service robot to be calibrated and transmitting the kinematic model to the calibration server; the variables described by the kinematic model include: the method comprises the following steps of (1) calibrating parameters to be calibrated, variables containing internal measurement data of the service robot, variables containing external measurement data of the service robot and functional relations among the variables;
the calibration server is used for collecting external measurement data and internal measurement data, calculating an expected value through the relation between the internal measurement data and the model, and determining a parameter to be calibrated in the kinematic model by minimizing the sum of squares of errors between the expected value and an actual measurement value;
the performance detection unit is used for updating the model parameters of the service robot by utilizing the motion module with the determined parameters to be calibrated; then, comparing the error between the expected value of the updated model (i.e. the value calculated again according to the model after updating the model) and the external measurement data, and detecting whether the calibration result meets the detection requirement.
Those skilled in the art will appreciate that the data measured by the MCS system at one time includes six dimensions at most, i.e. the position and attitude (x, y, z, roll, pitch, yaw) of the rigid body in three-dimensional space, but different applications may only use data of partial dimensions, such as odometer calibration in the following embodiments, since the robot translates on the ground, only three-dimensional data of x coordinate, y coordinate, and robot orientation yaw is used. Therefore, the external measurement data is an external measurement value in practical use.
For different robot configurations, one measurement will obtain the external measurement value and the corresponding model expected value under the configuration, so that a residual error under the current configuration can be calculated.
Further, the calibration server further comprises: an MCS Bridge unit, an NTP time synchronization unit and an ROS Bridge unit;
the MCS Bridge unit is used for converting the format of external measurement data sent by the MCS system into the format required by the calibration server;
the ROS Bridge unit is used for converting the format of the internal measurement data sent by the service robot into the format required by the calibration server;
and the NTP time synchronization unit is used for taking charge of clock synchronization between the MCS system and the service robot.
Further, the step of transmitting the data recorded by the service robot to be calibrated as internal measurement data to the calibration server includes:
if the internal measurement data volume exceeds the threshold value, the data volume is considered to be large, and the internal measurement data is stored in an offline data unit in the service robot to be calibrated; and if the internal measurement data quantity does not exceed the threshold value, the data quantity is considered to be small, and the internal measurement data is transmitted to an ROS Bridge unit in the calibration server through the network.
In addition, the parameter calibration problem is described as:
y＝f(q,x,p)；
wherein q is internal measurement data, x is external measurement data, p is a parameter to be calibrated in the kinematic model, and f is a function relation symbol;
the above formula is a non-thread equation, and the parameter to be calibrated is obtained by using a nonlinear solving method;
alternatively, it is linearized by differentiation to yield:
Δy(q,x,p)＝Ψ(q,x,p)·Δp；
in the above formula, Δ p ═ pr-p，prFor a known nominal value, Δ y is a function variation value, Ψ (q, x, y) is a Jacobian matrix for the variable p, and the parameter to be calibrated is obtained by solving the above equation.
For ease of understanding, the following description will be made in detail with respect to the operation of the above-described system.
Fig. 2 is a schematic diagram illustrating the operation of the system shown in fig. 1. From fig. 2, the main inputs of the calibration server include: (1) model: the model defines a kinematic model of the robot to be calibrated, and variables described by the model are mainly classified into three types: the parameters to be calibrated, the variables containing the internal measurement data of the robot, the variables containing the external measurement data of the robot, and the functional relationship among the variables. (2) Data: the input data is mainly divided into two categories: one is external measurement data, data measured by MCS; another type is measurement data of sensors inside the server robot. The output of the system is mainly the real value of the parameter to be solved in the kinematic model.
Those skilled in the art will appreciate that the kinematic model established by the modeling unit may be implemented in a conventional manner; in addition, when the MCS system collects external measurement data, the robot can be placed in a measurement area to complete a set of pre-designed actions, and then the MCS system tracks the pose, so as to measure corresponding data.
The calibration server is internally provided with format conversion units (MCS Bridge unit and ROS Bridge unit) which can respectively convert the formats of internal and external measurement data so as to meet the requirement of calibration calculation; in addition, the external measurement data and the internal measurement data required for calibration detection are data generated at the same time, but because the MCS system and the service robot are isolated systems, an NTP time synchronization module is also arranged in the calibration server to be responsible for keeping the clocks of the MCS system and the service robot synchronized.
In addition, the service robot has different measurement data amounts and different transmission modes to the calibration server; in short, if the data volume is small, the data volume can be directly sent to the ROS Bridge unit in the calibration server through the network in an online transmission mode; and if the data volume is large, the corresponding measurement is temporarily stored in the local service robot, and the measurement is sent to the calibration server in an off-line transmission mode after the measurement is finished.
And the calibration server is used for calculating the parameters to be calibrated in the kinematic model by combining the received data with a calibration algorithm so as to calculate the true values of the parameters.
After the calibration server calculates the true value of the parameter to be calibrated, performance detection is required to detect whether the calibration result meets the detection requirement
Based on the scheme of the embodiment of the invention, the service robots to be calibrated and detected can enter the system in sequence, and the system can automatically collect required measurement data. The robot completes a set of pre-designed actions in the measurement area, and the system obtains related data and solves parameters according to a calibration algorithm, thereby completing the calibration detection process. The system does not need manual participation in the operation process, and can automatically complete batch calibration detection tasks.
The technical scheme of the embodiment of the invention can be applied to the batch production process of the service robots, the assembly production process of a single/set of complex robots or the manufacturing process of other hardware systems with calibration requirements; the method can also be applied to the performance detection and upgrading updating process of the existing service robot system or other hardware systems; the method can also be applied to automatic calibration of related systems such as a detachable system, a self-assembly system and the like.
Illustratively, the system provided by the invention can be realized and applied to the chassis calibration of a mobile operation service robot. The MCS system adopted by the system consists of 12 intelligent infrared cameras with built-in FPGA processing chips, and can cover the system within a range (20 m)2Test area) mark points are continuously tracked, and the capture precision is up to 0.1 mm. The MCS system can provide high-frequency and accurate external measurement data for the automatic calibration system, the measurement data are transmitted to the calibration server in the local area network through the network switch, and the sensor data in the robot are also transmitted to the calibration server in real time through the network. The robot enters a test area and runs according to a preset arc track, the system records pose measurement information and encoder data corresponding to the robot, and the diameter and wheel spacing of left and right wheels of a chassis of the robot can be accurately obtained by using a model of a differential wheel.
In this example, the chassis driving mechanism model of the robot is shown in fig. 3, and the pose thereof is formed by combining the motions of the two front driving wheels.
Let the rotation speeds of the left and right driving wheels be omegaLAnd ωRThe current angular velocity of the robot is ω, and the linear velocity is v, and according to the model, there are:
the parameter matrix C is the parameter to be calibrated, and can be expressed as:
in the above formula, b is the distance between two driving wheels, rLAnd rRThe radius of the left driving wheel and the right driving wheel.
From the above two equations, one can obtain:
wherein (x)k,yk,θk),(xk+1,yk+1,θk+1) The pose (x-direction coordinate, y-direction coordinate, orientation theta) of the robot at the time k and the time k +1 respectively, T is a sampling interval, and w iskAngular velocity, v, of the robot at time kkIs the linear velocity of the robot at time k.
By using the change of the orientation of the robot in the N sampling periods, the following thread relationship can be established:
wherein theta is0And thetaNThe orientation of the robot at the start time (time 0) and the end time (time N), wL,iAnd wR,iThe time I is the rotation speed of the left and right driving wheels during (I, I +1) (. phi.)θIs a coefficient vector.
Executing P times of sampling process and recording corresponding data, thus obtaining:
wherein phiθ,pFor the coefficient vector obtained in the p-th sampling process, the linear relation established according to the formula can be solved to obtain C in the parameter matrix C2，1And C2，2。
Similarly, the relation of the position change of the robot in the N sampling periods can be utilized to solve C in the parameter matrix C1，1And C1，2C above1，1、C1，2、C2，1、C2，2The elements in four positions in the parameter matrix C (2 x 2). Wherein x0,xNIs the x coordinate of the robot at the starting time (0 time) and the ending time (N time)0,yNIs the y coordinate of the robot at the starting time (time 0) and the ending time (time N), phixyIs a coefficient vector.
After calibration is started, the mark points are fixed on the robot and enter a calibration field to operate around, data acquisition times are set, the system automatically acquires data, and a related parameter matrix C of the odometer is calculated according to a model relation.
And updating a model in the service robot according to the calculated related parameters of the odometer, and performing performance detection to detect whether the calibration result meets the requirements.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. An automatic calibration and detection system of a service robot based on optical motion tracking is characterized by comprising:
the optical motion tracking system MCS is used as an external measuring tool and can track the pose of the service robot to be calibrated, and the measured data is used as external measuring data and transmitted to the calibration server;
the service robot to be calibrated is used for operating according to a preset mode, recording related data through the internal sensor, and transmitting the recorded data serving as internal measurement data to the calibration server;
the modeling unit is used for establishing a kinematic model of the service robot to be calibrated and transmitting the kinematic model to the calibration server; the variables described by the kinematic model include: the method comprises the following steps of (1) calibrating parameters to be calibrated, variables containing internal measurement data of the service robot, variables containing external measurement data of the service robot and functional relations among the variables;
the calibration server is used for collecting external measurement data and internal measurement data, calculating an expected value through the relation between the internal measurement data and the model, and determining a parameter to be calibrated in the kinematic model by minimizing the sum of squares of errors between the expected value and an actual measurement value;
the performance detection unit is used for updating the model parameters of the service robot by utilizing the motion module with the determined parameters to be calibrated; then, comparing errors between the updated expected value of the model and external measurement data, and detecting whether the calibration result meets the detection requirement;
the calibration server also comprises: an MCS Bridge unit, an NTP time synchronization unit and an ROS Bridge unit;
the MCS Bridge unit is used for converting the format of external measurement data sent by the MCS system into the format required by the calibration server;
the ROS Bridge unit is used for converting the format of the internal measurement data sent by the service robot into the format required by the calibration server;
the NTP time synchronization unit is used for taking charge of clock synchronization between the MCS system and the service robot;
the automatic calibration of the service robot comprises chassis calibration, and the problem of parameter calibration is described as follows:
y＝f(q,x,p)；
wherein q is internal measurement data, x is external measurement data, p is a parameter to be calibrated in the kinematic model, and f is a function relation symbol;
the above formula is a non-thread equation, and the parameter to be calibrated is obtained by using a nonlinear solving method;
alternatively, it is linearized by differentiation to yield:
Δy(q,x,p)＝Ψ(q,x,p)·Δp；
in the above formula, Δ p ═ pr-p，prIs a nominal value, Δ y is a function variation value, Ψ (q, x, y) is a Jacobian matrix for the variable p;
then, the parameter to be calibrated is obtained by solving the above formula;
when the pose of the robot is formed by the combination of the motions of the two front driving wheels, the rotating speeds of the left driving wheel and the right driving wheel are respectively set to be omegaLAnd ωRIf the current angular velocity of the robot is ω and the linear velocity is v, then:
the parameter matrix C is the parameter to be calibrated, and is expressed as:
in the above formula, b is the distance between two driving wheels, rLAnd rRThe radius of the left driving wheel and the right driving wheel;
according to the above two formulas, we get:
wherein (x)k,yk,θk)、(xk+1,yk+1,θk+1) The pose of the robot at the moment k and the moment k +1 is shown, x and y show the horizontal and vertical coordinates of the corresponding moment, and theta shows the orientation of the corresponding moment; t is a sampling interval; omegakAngular velocity, v, of the robot at time kkThe linear velocity of the robot at the moment k;
the following thread relation is established by using the change of the orientation of the robot in N sampling periods:
wherein, ω isL,iAnd ωR,iI time is the rotation speed of the left and right driving wheels, phiθIs a coefficient vector of0、θNRespectively representing the directions of the robot at the time 0 and the time N;
executing P times of sampling process and recording corresponding data to obtain:
wherein phiθ,pFor the coefficient vector obtained in the p-th sampling process, the linear relation established according to the formula can be solved to obtain C in the parameter matrix C2,1And C2,2(ii) a Similarly, the relation of the position change of the robot in the N sampling periods is utilized to solve C in the parameter matrix C1,1And C1,2C above1,1、C1,2、C2,1、C2,2Are elements of four positions in the parameter matrix C.
2. The system of claim 1, wherein the data recorded by the service robot to be calibrated is transmitted to the calibration server as internal measurement data, and the system comprises:
if the internal measurement data volume exceeds the threshold value, the data volume is considered to be large, and the internal measurement data is stored in an offline data unit in the service robot to be calibrated; and if the internal measurement data quantity does not exceed the threshold value, the data quantity is considered to be small, and the internal measurement data is transmitted to an ROS Bridge unit in the calibration server through the network.
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| CN100573387C (en) * | 2007-12-10 | 2009-12-23 | 华中科技大学 | Freedom positioning system for robot |
| CN101436073A (en) * | 2008-12-03 | 2009-05-20 | 江南大学 | Wheeled mobile robot trace tracking method based on quantum behavior particle cluster algorithm |
| NZ598500A (en) * | 2009-08-31 | 2013-11-29 | Neato Robotics Inc | Method and apparatus for simultaneous localization and mapping of mobile robot environment |
| CN103279949B (en) * | 2013-05-09 | 2015-10-07 | 浙江大学 | Based on the multi-camera parameter automatic calibration system operation method of self-align robot |
| CN104281148A (en) * | 2013-07-07 | 2015-01-14 | 哈尔滨点石仿真科技有限公司 | Mobile robot autonomous navigation method based on binocular stereoscopic vision |
| JP2015103059A (en) * | 2013-11-26 | 2015-06-04 | トヨタ自動車株式会社 | Autonomous mobile body |
| CN105045263B (en) * | 2015-07-06 | 2016-05-18 | 杭州南江机器人股份有限公司 | A kind of robot method for self-locating based on Kinect depth camera |
| CN105094130B (en) * | 2015-07-29 | 2018-01-23 | 广东省自动化研究所 | The AGV transfer robots air navigation aid and device of laser guidance map structuring |
-
2016
- 2016-11-24 CN CN201611048827.5A patent/CN108107882B/en active Active
Non-Patent Citations (4)
| Title |
|---|
| 《6R型工业机器人标定算法与实验研究》;南小海;《万方学位论文》;20091231;第1-60页 * |
| 《一般7R串联机器人标定的仿真与实验》;王品等;《机器人》;20060930;第28卷(第5期);第483-487,494页 * |
| 《基于距离精度的测量机器人标定模型及算法》;任永杰等;《计量学报》;20080731;第29卷(第3期);第198-202页 * |
| 《机器人标定技术研究》;刘振宇等;《机器人》;20020930;第24卷(第5期);第447-450页 * |
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