CN113137967A - Robot positioning method and device, robot and readable storage medium - Google Patents
Robot positioning method and device, robot and readable storage medium Download PDFInfo
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- CN113137967A CN113137967A CN202110545895.7A CN202110545895A CN113137967A CN 113137967 A CN113137967 A CN 113137967A CN 202110545895 A CN202110545895 A CN 202110545895A CN 113137967 A CN113137967 A CN 113137967A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/18—Stabilised platforms, e.g. by gyroscope
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
Abstract
The embodiment of the invention discloses a robot positioning method, a device, a robot and a readable storage medium, wherein the robot comprises a communication module, the communication module is used for receiving pulse signals sent by each positioning label, and the method comprises the following steps: estimating the estimated pose of the robot at the current moment based on the inertial data of the robot, determining angle-reliable tags in each positioning tag according to the estimated angle corresponding to the estimated pose of the robot at the current moment, and determining the actual pose of the robot at the current moment based on the relative distance and the relative angle corresponding to the angle-reliable tags. According to the technical scheme, the high-frequency inertial measurement unit is used for collecting inertial data at a high frequency, the inertial measurement unit is used for collecting the inertial data when the robot moves, and positioning based on the high-frequency inertial measurement unit and positioning based on the communication module-label are integrated, so that the robot which moves quickly is accurately positioned.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a robot positioning method, a robot positioning device, a robot and a readable storage medium.
Background
Positioning technology is a key technology for mobile robot navigation. The robot is accurately positioned in real time through the positioning device, so that the navigation system can plan a path according to the accurate positioning of the robot, and then the robot is controlled to complete the work.
The current common robot positioning modes comprise fixed track positioning, visual positioning based on a virtual track, mobile robot visual positioning based on deep learning and the like, and all the positioning modes have defects. For example, for fixed track positioning, a guide rail of a metal wire or a magnetic nail needs to be laid on the ground, and a robot walks on the guide rail, but the guide rail is not only inconvenient to install, but also high in maintenance cost; based on the visual positioning of the virtual track, a guide line needs to be drawn on the ground, or a color tape needs to be laid, or a two-dimensional code navigation tape needs to be laid, but the virtual track is easily covered by dust or movable shelters, so that the robot cannot be accurately positioned; the mobile robot vision positioning method based on deep learning has the advantages of complex algorithm, high calculation difficulty, over-ideal method and lower practicability.
Disclosure of Invention
In view of the above problems, the present application provides a robot positioning method, apparatus, robot and readable storage medium.
The application provides a robot positioning method, which comprises the following steps:
acquiring relative distance and relative angle between the robot and each positioning label;
acquiring inertial data of the robot;
estimating the estimated pose of the robot at the current moment according to the inertial data;
determining a predetermined first number of angle-reliable tags from the respective positioning tags according to the comparison of the estimated angles in the estimated pose and the respective relative angles;
and determining the actual pose of the robot at the current moment according to the relative distance and the relative angle corresponding to the predetermined first number of angle reliable labels.
The application discloses robot positioning method, install first antenna and second antenna on the predetermined position of robot, first antenna with the second antenna is used for receiving the pulse signal that each location label sent, obtain relative distance and relative angle between robot and each location label, include:
acquiring a first distance between an ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags;
acquiring a second distance between the ith positioning tag and the second antenna;
determining the arrival phase difference of the pulse signals sent by the ith positioning tag at the first antenna and the second antenna;
and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
The robot positioning method of the present application, wherein the inertial data includes a linear acceleration and an angular velocity of the robot, and estimating an estimated pose of the robot at a current time according to the inertial data, includes:
estimating the displacement variation of the robot from the previous moment to the current moment according to the linear acceleration of the previous moment in the inertial data;
estimating the angle variation of the robot from the previous moment to the current moment according to the angular velocity at the previous moment in the inertial data;
and estimating the estimated pose of the robot at the current moment according to the displacement variation and the angle variation.
The robot positioning method of the present application, wherein determining a predetermined first number of angle-reliable tags from the respective positioning tags according to a comparison result between the estimated angle in the estimated pose and the respective relative angles, includes:
calculating the absolute value of the difference between the relative angle corresponding to the ith positioning label and the estimated angle, wherein I is not more than I, and I is the total number of the positioning labels;
judging whether the absolute value is larger than a preset angle threshold value or not;
if the angle is smaller than or equal to the angle threshold, the ith positioning label is reliable;
if the angle is larger than the angle threshold value, the ith positioning label is unreliable.
The method for positioning the robot according to the application, wherein the step of determining the actual pose of the robot at the current moment according to the relative distances and the relative angles corresponding to the predetermined first number of angle-reliable tags includes:
calculating a mean value corresponding to the relative angles and a variance corresponding to the relative angles according to the relative angles corresponding to the predetermined first number of angle reliability labels;
determining a predetermined second number of distance reliable labels according to the relative distances corresponding to the predetermined first number of angle reliable labels;
calculating a mean value corresponding to the relative distance and a variance corresponding to the relative distance according to the relative distance corresponding to the predetermined second number of distance reliable labels;
and inputting the mean value corresponding to the relative angle, the mean value corresponding to the relative distance, the variance corresponding to the relative angle and the variance corresponding to the relative distance into a preset unscented Kalman filter so as to obtain the actual pose of the robot at the current moment.
The method for positioning a robot according to the present application, wherein determining a predetermined second number of distance-reliable labels according to relative distances corresponding to the predetermined first number of angle-reliable labels, includes:
dividing the predetermined first number of angle-reliable labels into K groups, each group including at least 3 angle-reliable labels;
determining the position of the robot according to the angle reliable labels of each group;
calculating the mean value corresponding to the K positions and the variance corresponding to the K positions;
if the variance corresponding to the K positions is larger than a preset variance threshold value, deleting the position, which is farthest from the mean value corresponding to the K positions, in the K positions, and recalculating the mean value corresponding to the K-1 positions and the variance corresponding to the K-1 positions until the variance corresponding to the remaining positions is smaller than or equal to the preset variance threshold value, wherein the positioning label corresponding to the remaining positions is a distance reliable label;
and if the variance corresponding to the K positions is smaller than or equal to a preset variance threshold value, the positioning labels corresponding to the K positions are distance reliable labels.
The application provides a robot positioning device, the robot includes communication module, communication module is used for receiving the pulse signal that each location label sent, the device includes:
the acquisition unit is used for acquiring the relative distance and the relative angle between the robot and each positioning label; the robot is also used for acquiring inertia data of the robot;
the estimation unit is used for estimating the estimation pose of the robot at the current moment according to the inertial data;
a screening unit configured to determine a predetermined first number of angle-reliable tags from the respective positioning tags according to a comparison result of the estimated angles in the estimated pose and the respective relative angles;
and the positioning unit is used for determining the actual pose of the robot at the current moment according to the relative distance and the relative angle corresponding to the predetermined first number of angle reliable labels.
The utility model provides a robot positioning device, communication module includes first antenna and second antenna, first antenna with the second antenna is used for receiving the pulse signal that each location label sent, obtain relative distance and relative angle between robot and each location label, include:
acquiring a first distance between an ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags;
acquiring a second distance between the ith positioning tag and the second antenna;
determining the arrival phase difference of the pulse signals sent by the ith positioning tag at the first antenna and the second antenna;
and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
The application provides a robot, which comprises a memory, a processor and a communication module, wherein the memory stores a computer program, the computer program executes the robot positioning method when running on the processor, and the communication module is used for receiving pulse signals sent by various positioning tags.
The application proposes a readable storage medium storing a computer program which, when run on a processor, performs the robot positioning method.
The robot positioning method determines the position relation between a robot and each positioning tag by utilizing carrier communication between a communication module of the robot and each positioning tag, estimates the estimated pose of the robot at the current moment based on inertial data of the robot, determines angle-reliable tags in each positioning tag according to the estimated angle of the estimated pose of the robot at the current moment, and determines the actual pose of the robot at the current moment based on the relative distance and the relative angle corresponding to the angle-reliable tags. The method utilizes the characteristic that a high-frequency Inertial Measurement Unit (IMU) has high-frequency Inertial data acquisition, acquires the Inertial data of the robot during movement by using the IMU, and integrates IMU positioning and communication module-label positioning so as to realize accurate positioning of the robot which moves quickly. Compared with the existing fixed track positioning and visual positioning based on the virtual track, the method has stronger practicability, does not need to install a guide rail and does not need special personnel to maintain the guide rail and a guide line; compared with the existing mobile robot visual positioning method based on deep learning, the method has the advantages of smaller calculated amount, simple algorithm and convenience in calculation.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention. Like components are numbered similarly in the various figures.
Fig. 1 shows a schematic flow chart of a robot positioning method proposed in the present application;
fig. 2 shows a schematic diagram of a method for determining the relative distance and relative angle between a robot and a positioning tag according to the present application;
fig. 3 shows a schematic diagram of a principle proposed in the present application for determining the relative distance and relative angle between a robot and a positioning tag;
fig. 4 shows a schematic diagram of a method for determining an estimated pose of a robot according to the present application;
FIG. 5 is a schematic diagram illustrating a method for determining an angularly reliable tag according to the present application;
FIG. 6 is a schematic diagram illustrating a determination process for determining an angle-reliable label according to the present application;
fig. 7 shows a schematic diagram of a method for determining an actual pose of a robot according to the present application;
FIG. 8 is a schematic diagram illustrating a method for determining a distance reliable tag according to the present application;
fig. 9 shows a schematic structural diagram of a robot positioning device proposed in the present application;
fig. 10 shows a schematic structural diagram of a robot proposed in the present application.
Description of the main element symbols:
10-a robotic positioning device; 11-an acquisition unit; 12-an estimation unit; 13-a screening unit; 14-a positioning unit; 100-a robot; 110-a memory; 120-a processor; 130-a communication module; 131-a first antenna; 132-second antenna.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
The robot positioning method disclosed by the application is based on an Ultra Wide Band (UWB) wireless carrier communication technology, a phase difference of arrival (PDOA) principle and a high-frequency Inertial Measurement Unit (IMU), and accurately positions a robot which moves rapidly indoors with a relatively low calculation amount.
An Ultra Wide Band (UWB) technology is a wireless carrier communication technology, which does not use a sinusoidal carrier but uses nanosecond-level non-sinusoidal narrow pulses to transmit data, and thus, the occupied frequency spectrum range is Wide. The UWB technology has the advantages of low system complexity, low power spectral density of transmitted signals, insensitivity to channel fading, low interception capability, high positioning accuracy and the like, and is particularly suitable for indoor positioning of robots. In indoor positioning, UWB enables higher ranging accuracy.
The PDOA-based ranging UWB device can output distance and angle values, and the PDOA-UWB-based positioning system can accomplish estimation of the robot position and orientation. However, because of the limited time bandwidth, PDOA-UWB can only measure distances at frequencies around 10Hz, and when the indoor robot moves rapidly, there is a large error in the position and orientation estimated from the time-of-flight measurement and the phase measurement. The PDOA-UWB and IMU are used for data fusion positioning, high-frequency IMU data and low-frequency UWB pose measurement are combined, more accurate fusion positioning data with high frequency can be obtained, and accurate positioning of the robot under rapid movement is facilitated.
Example 1
One embodiment of the present application, as shown in fig. 1, proposes a robot positioning method comprising the following steps:
s100: the relative distance and relative angle between the robot and each positioning tag are obtained.
A plurality of positioning tags are arranged in an active area of the robot in advance, a communication module is installed at a specific position of the robot, and each positioning tag transmits pulse information (wireless carrier) to the communication module of the robot in real time. The robot can determine the relative distance and the relative angle between the robot and each positioning label according to the pulse information received by the communication module.
Further, considering that UWB has the advantages of high penetrating power, low power consumption, good anti-multipath effect, high security, low system complexity, and capability of providing accurate positioning accuracy, etc., the PDOA-UWB positioning technology can be used to determine the relative distance and relative angle between the robot and each positioning tag. The PDOA-UWB communication module is placed on the robot, a plurality of PDOA-UWB tags are placed in a scene needing positioning, and the robot can acquire distance and angle data of each pair of tags and communication modules through a serial port.
It will be appreciated that the communication module may be mounted to a central location of the robot, and that the location of the communication module may represent the location of the robot when the robot is located using the communication module and the respective tags. For example, a central point of one communication antenna of the communication module may be used as a coordinate origin, and when the robot is stationary, a coordinate system (Y axis is perpendicular to the X axis, and the orientation of the Y axis may not be limited) is established in a positive direction with the robot orientation as the X axis, and the relative distance and the relative angle between the robot and each positioning tag are determined under the coordinate system. The relative distance is the distance between the robot (namely the origin of coordinates) and the coordinates of the positioning label, and can be determined according to the propagation speed and the propagation time of the pulse, and the relative angle is the included angle between the connecting line between the robot (namely the origin of coordinates) and the coordinates of the positioning label and the connecting line of the positive direction coordinate axis of the X axis.
S200: and acquiring inertial data of the robot.
The inertial data of the robot includes linear acceleration and angular velocity of the robot at a certain time. The linear acceleration and the angular velocity of the robot at a certain moment can be obtained by utilizing the high-frequency inertial measurement unit. It is understood that the high frequency inertial measurement unit is a device that measures the three-axis attitude angle or angular rate and acceleration of the object. Generally, a three-axis gyroscope and three-direction accelerometers are installed in the high-frequency inertial measurement unit IMU, the gyroscope can acquire angular velocity of the robot at a certain moment, and the accelerometers can acquire linear acceleration of the robot at a certain moment. The robot can receive inertial data acquired by the IMU through the serial port.
S300: and estimating the estimated pose of the robot at the current moment according to the inertial data.
Linear accelerations in the inertial data acquired at adjacent times may be integrated to estimate the velocity and displacement variation of the robot at the adjacent times, and angular velocities in the inertial data acquired at the adjacent times may be integrated to estimate the rotation variation of the robot at the adjacent times. And then the variation and the rotation variation are added to the estimated pose displacement of the robot at the previous moment so as to estimate the estimated pose of the robot at the current moment.
S400: determining a predetermined first number of angle-reliable tags from the respective positioning tags according to the comparison of the estimated angles in the estimated pose and the respective relative angles.
Selecting angle reliable labels from the positioning labels, sequentially traversing the relative angles corresponding to the positioning labels, comparing the relative angles with the estimated angles of the estimated pose, calculating the difference between the relative angles and the estimated angles of the estimated pose, sorting the absolute values of the difference according to a sequence from small to large, selecting the relative angles corresponding to N absolute values in the front of the sorting, wherein the N absolute values in the front of the sorting are smaller than a preset angle threshold, and N is a preset first number. It can be understood that the relative angle corresponding to the top N absolute values is closest to the orientation of the robot, and therefore, the positioning tags corresponding to the N relative angles can be regarded as angle-reliable tags.
S500: and determining the actual pose of the robot at the current moment according to the relative distance and the relative angle corresponding to the predetermined first number of angle reliable labels.
Further, N angle reliable labels are screened, M distance reliable labels are selected from the N angle reliable labels according to the relative distance and the relative angle corresponding to the angle reliable labels, N is larger than or equal to M, and then the actual pose of the robot at the current moment is determined according to the N angle reliable labels and the M distance reliable labels.
It is to be understood that the above step S100 may be performed after the step S300, and the present application does not limit the order between determining the estimated pose and acquiring the relative distance and relative angle between the robot and each positioning tag.
The robot positioning method disclosed in this embodiment determines a position relationship between the robot and each positioning tag by using carrier communication between a communication module of the robot and each positioning tag, estimates an estimated pose of the robot at the current time based on inertial data of the robot, determines an angle-reliable tag in each positioning tag by using an estimated angle of the estimated pose of the robot at the current time, and determines an actual pose of the robot at the current time based on a relative distance and a relative angle corresponding to the angle-reliable tag. According to the technical scheme, the inertial measurement unit IMU with high frequency is utilized to acquire the inertial data with high frequency, the inertial measurement unit IMU is used to acquire the inertial data when the robot moves, and IMU positioning and communication module-label positioning are fused to realize accurate positioning of the robot which moves rapidly. Compared with fixed track positioning and visual positioning based on a virtual track, the technical scheme has stronger practicability, does not need to install a guide rail and does not need special personnel to maintain the guide rail and a guide line; compared with the mobile robot visual positioning method based on deep learning, the technical scheme has the advantages of smaller calculated amount, simple algorithm and convenience in calculation.
Example 2
Further, a first antenna and a second antenna may be installed at predetermined positions of the robot, the first antenna and the second antenna may receive the pulse signals transmitted by the respective positioning tags, and the relative distance and the relative angle between the robot and the respective positioning tags may be determined through the steps shown in fig. 2:
s110: and acquiring a first distance between the ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags.
S120: and acquiring a second distance between the ith positioning tag and the second antenna.
S130: and determining the arrival phase difference of the pulse signals transmitted by the ith positioning tag at the first antenna and the second antenna.
S140: and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
Exemplarily, as shown in fig. 3, O1 represents a first antenna of the robot, O2 represents a second antenna of the robot, and when the first antenna and the second antenna are installed on the robot, it is required to ensure that a ray connecting the first antenna and the second antenna can be directed to the robotThat is, when the robot is stationary, the X-axis direction in fig. 3 represents the robot direction (it is understood that the positive direction of the X-axis changes when the robot direction changes). Further, D represents the distance between O1 and O2, D is known, a represents a positioning tag, the distance from O1 to a is R, the distance from O2 to a is R — P, and P can be calculated by the arrival phase difference of the pulse signal sent by a to reach O1 and O2. As can be appreciated, the first and second,
the arrival phase difference of the pulse signals emitted by A and arriving at O1 and O2 is shown, and lambda represents the wavelength of the pulse signals.
Further, θ 1 is calculated, and as can be seen from fig. 3: the vertical segments of O2 to O1A correspond to two right triangles, which can be derived from Pythagorean theorem:
(R-P)2-(R-(P+a))2＝D2-(P+a)2then P + a ═ D2-P2+2RP)/2R, further derived from the cosine law,
because the first antenna and the second antenna are arranged at specific positions of the robot, the first antenna can be arranged at the central position of the robot when the first antenna is arranged, and the direction of the ray connecting the first antenna and the second antenna can be ensured to be directed to the direction of the robot, the direction of the robot is taken as the positive direction of the X axis, so that when the robot is at rest, theta is measured1May be the relative angle between the robot and the positioning tag a, and R may be the relative distance between the robot and the positioning tag a.
Further, according to the principle shown in fig. 3, the relative distance and the relative angle between the robot and each positioning tag are respectively determined, and each determined relative distance and each determined relative angle are fed back to the robot, so that the robot can determine the actual pose of the robot according to each relative distance and each relative angle.
Example 3
In an embodiment of the present application, as shown in fig. 4, estimating the estimated pose of the robot at the current time according to the inertial data includes the following steps:
s310: and estimating the displacement variation of the robot from the previous moment to the current moment according to the linear acceleration of the previous moment in the inertia data.
Exemplarily, the last moment can be represented as a moment t-1, and the current moment can be represented as a moment t, because the IMU for collecting the inertial data of the robot has high-frequency collection capability, and the interval between the moment t-1 and the moment t is short, when the estimated pose at the moment t is estimated by using the inertial data at the moment t-1, the estimation error can be effectively reduced, and the positioning accuracy of the robot can be effectively improved.
Linear acceleration a in inertial data acquired at time t-1t-1The speed and displacement variation of the robot at time t can be determined by integration. Exemplarily, the speed of the robot at time t
Displacement variation of robot at time t
S320: and estimating the angle variation of the robot from the previous moment to the current moment according to the angular velocity at the previous moment in the inertial data.
Linear acceleration w in inertial data acquired at time t-1t-1The integration may determine the amount of angular change of the robot at time t. Exemplarily, the angle change of the robot at the time t
S330: and estimating the estimated pose of the robot at the current moment according to the displacement variation and the angle variation.
Accumulating the displacement variation quantity to an actual position corresponding to the actual pose of the robot at the previous moment so as to estimate an estimated position corresponding to the actual pose of the robot at the current moment; and accumulating the angle variation to the actual angle corresponding to the actual pose of the robot at the previous moment so as to estimate the estimated angle corresponding to the actual pose of the robot at the current moment, and further estimating the estimated pose of the robot at the current moment according to the estimated position and the estimated angle. So that the robot can position the actual pose of the robot at the current moment according to the estimated pose of the robot at the current moment determined by the IMU. The high-frequency acquisition function of the IMU can estimate the estimation pose of the robot at the current moment in advance for the fast moving robot, and ensures that the deviation between the estimation pose and the actual pose is small, thereby ensuring the accurate positioning of the fast moving robot.
Example 4
In one embodiment of the present application, as shown in fig. 5, determining an angle-reliable tag includes the following steps:
s410: and calculating the absolute value of the difference between the relative angle corresponding to the ith positioning label and the estimated angle, wherein I is less than or equal to I, and I is the total number of the positioning labels.
S420: and judging whether the absolute value is larger than a preset angle threshold value or not.
If the angle is less than or equal to the angle threshold, executing step S430; if the angle is greater than the angle threshold, step S440 is executed. And further determining an angle-reliable label from the I positioning labels so as to position the robot by using the angle-reliable label.
S430: and determining that the ith positioning label is reliable.
S440: the ith positioning tag is unreliable.
Exemplarily, as shown in fig. 6, O1 represents a first antenna of the robot, O2 represents a second antenna of the robot, and the positive direction of the X-axis represents the corresponding real-time estimated orientation of the robot when positioning is performed based on the communication module-tag, but since the time bandwidth of positioning is limited based on the communication module-tag, for example, PDOA-UWB can only perform ranging at a frequency of about 10Hz, and when the indoor robot moves rapidly, the position and orientation estimated by the time-of-flight measurement and the phase measurement may both existAt a large error, therefore, the present embodiment utilizes the estimated angle θ in the estimated pose at the present time determined by the inertial measurement unit at a high frequency0By mixing of theta0Relative angle theta with positioning tag A1The absolute value of the difference is compared to a preset angle threshold to determine whether the location tag a is an angle-reliable tag. The distinguishing method is utilized to respectively judge each positioning label, and the angle reliable label is determined from each positioning label. Each positioning label is screened through an angle threshold value, the positioning labels with large angle errors are filtered and removed, the robot is positioned based on the remaining angle reliable labels, the calculated amount of the robot in the positioning process is effectively reduced, and the robot moving quickly can be accurately positioned.
Example 5
In an embodiment of the present application, as shown in fig. 7, determining the actual pose of the robot at the current time includes the following steps:
s510: and calculating a mean value corresponding to the relative angle and a variance corresponding to the relative angle according to the relative angle corresponding to the predetermined first number of angle reliability labels.
And calculating a mean value corresponding to the relative angle and a variance corresponding to the relative angle according to the relative angle corresponding to the N angle reliable labels.
S520: and determining a predetermined second number of distance reliable labels according to the relative distance corresponding to the predetermined first number of angle reliable labels.
S530: and calculating a mean value corresponding to the relative distance and a variance corresponding to the relative distance according to the relative distance corresponding to the predetermined second number of distance reliable labels.
S540: and inputting the mean value corresponding to the relative angle, the mean value corresponding to the relative distance, the variance corresponding to the relative angle and the variance corresponding to the relative distance into a preset unscented Kalman filter so as to obtain the actual pose of the robot at the current moment.
And taking the input mean value corresponding to the relative angle and the input mean value corresponding to the relative distance as the measured value of the actual pose of the robot, wherein the variance corresponding to the relative angle is the uncertainty of the angle in the measured value corresponding to the actual pose of the robot, and the variance corresponding to the relative distance is the uncertainty of the position in the measured value corresponding to the actual pose of the robot. The unscented kalman filter may determine whether the measured value corresponding to the actual pose of the robot is reliable according to the input mean value corresponding to the relative angle, the input mean value corresponding to the relative distance, the input variance corresponding to the relative angle, and the input variance corresponding to the relative distance, for example, if the variance corresponding to the relative distance is small, it is determined that the mean value corresponding to the relative distance is reliable, and if the variance corresponding to the relative angle is small, the mean value corresponding to the relative angle is reliable, that is, the measured value of the actual pose of the robot determined by the mean value corresponding to the relative distance and the input mean value corresponding to the relative angle is reliable, and when the unscented kalman filter performs positioning, the actual pose of the robot at the current time is biased to the measured value of the actual pose of the robot; on the contrary, if the variance corresponding to the relative distance is large, it is indicated that the mean value corresponding to the relative distance is not credible, and the variance corresponding to the relative angle is large, the mean value corresponding to the relative angle is not credible, that is, the measured value of the actual pose of the robot determined by the mean value corresponding to the relative distance and the mean value corresponding to the relative angle is not credible, and when the unscented kalman filter is used for positioning, the actual pose of the robot at the current moment is far away from the measured value of the actual pose of the robot.
The unscented Kalman filter is used for determining the actual pose of the robot at the current moment, the Jacobian matrix does not need to be analyzed and calculated by a system equation, and the unscented Kalman filter has higher precision and is more convenient to calculate in nonlinear optimization, so that the robot is more accurately positioned, and the positioning process is more convenient and faster.
Further, as shown in fig. 8, the step of determining the distance reliable tag in step S520 includes the following steps:
s521: dividing the predetermined first number of angle-reliable labels into K groups, each group comprising at least 3 angle-reliable labels.
It can be understood that if the total number of the angle-reliable tags is N, each group includes p, and p is greater than or equal to 3, then using the permutation and combination formula, K groups can be determined,
s522: and determining the position of the robot according to the angle reliable labels of each group.
The position of the robot can be determined based on any 3-degree reliable labels of each group by utilizing a triangular positioning method, and the positions corresponding to each group are respectively calculated, so that K positions can be determined. The triangle positioning method is a commonly used technical means in the positioning field, and is not further explained here.
S523: and calculating the mean value corresponding to the K positions and the variance corresponding to the K positions.
S524: if the variance corresponding to the K positions is larger than a preset variance threshold value, deleting the position, which is farthest from the mean value corresponding to the K positions, in the K positions, and recalculating the mean value corresponding to the K-1 positions and the variance corresponding to the K-1 positions until the variance corresponding to the remaining positions is smaller than or equal to the preset variance threshold value, wherein the positioning label corresponding to the remaining positions is a distance reliable label.
S525: and if the variance corresponding to the K positions is smaller than or equal to a preset variance threshold value, the positioning labels corresponding to the K positions are distance reliable labels.
The embodiment discloses a method for further screening angle-reliable tags, which is used for selecting the distance-reliable tags from the angle-reliable tags, and then inputting the mean value corresponding to the relative angle, the mean value corresponding to the relative distance, the variance corresponding to the relative angle and the variance corresponding to the relative distance of each distance-reliable tag into a preset unscented kalman filter to obtain the actual pose of the robot at the current moment. The unscented kalman filter has the advantages that the Jacobian matrix does not need to be calculated by analyzing the system equation, the accuracy is higher in nonlinear optimization, the calculation is more convenient and simpler, the complexity of robot positioning is effectively reduced, and the positioning process is more efficient and faster.
Example 6
In one embodiment of the present application, as shown in fig. 9, a robot positioning device 10 includes: an acquisition unit 11, an estimation unit 12, a screening unit 13 and a positioning unit 14.
The acquiring unit 11 is configured to acquire a relative distance and a relative angle between the robot and each positioning tag, where the robot includes a communication module, and the communication module is configured to receive a pulse signal sent by each positioning tag; the acquisition unit 11 is further configured to acquire inertial data of the robot; an estimation unit 12, configured to estimate an estimated pose of the robot at a current time according to inertial data; a screening unit 13, configured to determine a predetermined first number of angle-reliable tags from the respective positioning tags according to a comparison result of the estimated angles in the estimated pose and the respective relative angles; and the positioning unit 14 is used for determining the actual pose of the robot at the current moment according to the relative distances and the relative angles corresponding to the predetermined first number of angle-reliable labels.
Further, a first antenna and a second antenna may be installed at a predetermined position of the robot, and the first antenna and the second antenna are configured to receive the pulse signals sent by the respective positioning tags, and the acquiring the relative distance and the relative angle between the robot and the respective positioning tags includes: acquiring a first distance between an ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags; acquiring a second distance between the ith positioning tag and the second antenna; determining the arrival phase difference of the pulse signals sent by the ith positioning tag at the first antenna and the second antenna; and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
Further, the inertial data includes a linear acceleration and an angular velocity of the robot, and the estimating the estimated pose of the robot at the current time according to the inertial data includes: estimating the displacement variation of the robot from the previous moment to the current moment according to the linear acceleration of the previous moment in the inertial data; estimating the angle variation of the robot from the previous moment to the current moment according to the angular velocity at the previous moment in the inertial data; and estimating the estimated pose of the robot at the current moment according to the displacement variation and the angle variation.
Further, the determining a predetermined first number of angle-reliable tags from the respective positioning tags according to the comparison result of the estimated angle in the estimated pose and the respective relative angle includes: calculating the absolute value of the difference between the relative angle corresponding to the ith positioning label and the estimated angle, wherein I is not more than I, and I is the total number of the positioning labels; judging whether the absolute value is larger than a preset angle threshold value or not; if the angle is smaller than or equal to the angle threshold, the ith positioning label is reliable; if the angle is larger than the angle threshold value, the ith positioning label is unreliable.
Further, the determining the actual pose of the robot at the current moment according to the relative distances and the relative angles corresponding to the predetermined first number of angle-reliable tags includes: calculating a mean value corresponding to the relative angles and a variance corresponding to the relative angles according to the relative angles corresponding to the predetermined first number of angle reliability labels; determining a predetermined second number of distance reliable labels according to the relative distances corresponding to the predetermined first number of angle reliable labels; calculating a mean value corresponding to the relative distance and a variance corresponding to the relative distance according to the relative distance corresponding to the predetermined second number of distance reliable labels; and inputting the mean value corresponding to the relative angle, the mean value corresponding to the relative distance, the variance corresponding to the relative angle and the variance corresponding to the relative distance into a preset unscented Kalman filter so as to obtain the actual pose of the robot at the current moment.
Further, the determining a predetermined second number of distance-reliable tags according to the relative distances corresponding to the predetermined first number of angle-reliable tags includes: dividing the predetermined first number of angle-reliable labels into K groups, each group including at least 3 angle-reliable labels; determining the position of the robot according to the angle reliable labels of each group; calculating the mean value corresponding to the K positions and the variance corresponding to the K positions; if the variance corresponding to the K positions is larger than a preset variance threshold value, deleting the position, which is farthest from the mean value corresponding to the K positions, in the K positions, and recalculating the mean value corresponding to the K-1 positions and the variance corresponding to the K-1 positions until the variance corresponding to the remaining positions is smaller than or equal to the preset variance threshold value, wherein the positioning label corresponding to the remaining positions is a distance reliable label; and if the variance corresponding to the K positions is smaller than or equal to a preset variance threshold value, the positioning labels corresponding to the K positions are distance reliable labels.
The robot positioning apparatus 10 disclosed in the present application is used to execute the robot positioning method according to the foregoing embodiment by matching the obtaining unit 11, the estimating unit 12, the screening unit 13, and the positioning unit 14, and the implementation and the beneficial effects related to the foregoing embodiment are also applicable in this embodiment, and are not described again here.
One embodiment of the present application, as shown in fig. 10, provides a robot 100, which includes a memory 110, a processor 120, and a communication module 130, where the memory 110 stores a computer program, the computer program executes the robot positioning method described in the present application when running on the processor 120, and the communication module 130 is configured to receive pulse signals sent by each positioning tag.
Further, the communication module 130 includes a first antenna 131 and a second antenna 132, and the communication module 130 receives the pulse signal transmitted by each positioning tag by using the first antenna 131 and the second antenna 132.
In an embodiment of the present application, a readable storage medium is proposed, which stores a computer program that, when run on a processor, performs the robot localization method described herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. 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 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.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A robot positioning method, characterized in that the robot comprises a communication module for receiving pulse signals sent by each positioning tag, the method comprises:
acquiring relative distance and relative angle between the robot and each positioning label;
acquiring inertial data of the robot;
estimating the estimated pose of the robot at the current moment according to the inertial data;
determining a predetermined first number of angle-reliable tags from the respective positioning tags according to the comparison of the estimated angles in the estimated pose and the respective relative angles;
and determining the actual pose of the robot at the current moment according to the relative distance and the relative angle corresponding to the predetermined first number of angle reliable labels.
2. The robot positioning method according to claim 1, wherein a first antenna and a second antenna are installed at predetermined positions of the robot, the first antenna and the second antenna are used for receiving pulse signals transmitted by the respective positioning tags, and the acquiring of the relative distance and the relative angle between the robot and the respective positioning tags comprises:
acquiring a first distance between an ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags;
acquiring a second distance between the ith positioning tag and the second antenna;
determining the arrival phase difference of the pulse signals sent by the ith positioning tag at the first antenna and the second antenna;
and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
3. The robot positioning method according to claim 1, wherein the inertial data includes a linear acceleration and an angular velocity of the robot, and the estimating the estimated pose of the robot at the current time from the inertial data includes:
estimating the displacement variation of the robot from the previous moment to the current moment according to the linear acceleration of the previous moment in the inertial data;
estimating the angle variation of the robot from the previous moment to the current moment according to the angular velocity at the previous moment in the inertial data;
and estimating the estimated pose of the robot at the current moment according to the displacement variation and the angle variation.
4. The robot positioning method according to claim 1, wherein the determining a predetermined first number of angle-reliable tags from the respective positioning tags according to the comparison result of the estimated angle in the estimated pose and the respective relative angles comprises:
calculating the absolute value of the difference between the relative angle corresponding to the ith positioning label and the estimated angle, wherein I is not more than I, and I is the total number of the positioning labels;
judging whether the absolute value is larger than a preset angle threshold value or not;
if the angle is smaller than or equal to the angle threshold, the ith positioning label is reliable;
if the angle is larger than the angle threshold value, the ith positioning label is unreliable.
5. The robot positioning method according to any one of claims 1 to 4, wherein the determining the actual pose of the robot at the current moment according to the relative distances and the relative angles corresponding to the predetermined first number of angle-reliable tags comprises:
calculating a mean value corresponding to the relative angles and a variance corresponding to the relative angles according to the relative angles corresponding to the predetermined first number of angle reliability labels;
determining a predetermined second number of distance reliable labels according to the relative distances corresponding to the predetermined first number of angle reliable labels;
calculating a mean value corresponding to the relative distance and a variance corresponding to the relative distance according to the relative distance corresponding to the predetermined second number of distance reliable labels;
and inputting the mean value corresponding to the relative angle, the mean value corresponding to the relative distance, the variance corresponding to the relative angle and the variance corresponding to the relative distance into a preset unscented Kalman filter so as to obtain the actual pose of the robot at the current moment.
6. The robot positioning method of claim 5, wherein determining a predetermined second number of distance-reliable tags from the relative distances to which the predetermined first number of angle-reliable tags correspond comprises:
dividing the predetermined first number of angle-reliable labels into K groups, each group including at least 3 angle-reliable labels;
determining the position of the robot according to the angle reliable labels of each group;
calculating the mean value corresponding to the K positions and the variance corresponding to the K positions;
if the variance corresponding to the K positions is larger than a preset variance threshold value, deleting the position, which is farthest from the mean value corresponding to the K positions, in the K positions, and recalculating the mean value corresponding to the K-1 positions and the variance corresponding to the K-1 positions until the variance corresponding to the remaining positions is smaller than or equal to the preset variance threshold value, wherein the positioning label corresponding to the remaining positions is a distance reliable label;
and if the variance corresponding to the K positions is smaller than or equal to a preset variance threshold value, the positioning labels corresponding to the K positions are distance reliable labels.
7. A robot positioning device, characterized in that the robot comprises a communication module for receiving pulse signals sent by each positioning tag, the device comprises:
the acquisition unit is used for acquiring the relative distance and the relative angle between the robot and each positioning label; the robot is also used for acquiring inertia data of the robot;
the estimation unit is used for estimating the estimation pose of the robot at the current moment according to the inertial data;
a screening unit configured to determine a predetermined first number of angle-reliable tags from the respective positioning tags according to a comparison result of the estimated angles in the estimated pose and the respective relative angles;
and the positioning unit is used for determining the actual pose of the robot at the current moment according to the relative distance and the relative angle corresponding to the predetermined first number of angle reliable labels.
8. The robot positioning device of claim 7, wherein the communication module comprises a first antenna and a second antenna, the first antenna and the second antenna are used for receiving the pulse signals sent by the respective positioning tags, and the acquiring the relative distance and the relative angle between the robot and the respective positioning tags comprises:
acquiring a first distance between an ith positioning tag and the first antenna, wherein I is less than or equal to I, and I is the total number of the positioning tags;
acquiring a second distance between the ith positioning tag and the second antenna;
determining the arrival phase difference of the pulse signals sent by the ith positioning tag at the first antenna and the second antenna;
and determining the relative distance and the relative angle between the robot and the ith positioning label according to the first distance, the arrival phase difference and the second distance.
9. A robot, characterized by comprising a memory, a processor and a communication module, the memory storing a computer program for performing the robot positioning method of any of claims 1 to 6 when the computer program runs on the processor, the communication module being configured to receive pulsed signals transmitted by respective positioning tags.
10. A readable storage medium, characterized in that it stores a computer program which, when run on a processor, performs the robot positioning method of any of claims 1 to 6.
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| CN202110545895.7A CN113137967B (en) | 2021-05-19 | 2021-05-19 | Robot positioning method, device, robot and readable storage medium |
| PCT/CN2021/131678 WO2022242075A1 (en) | 2021-05-19 | 2021-11-19 | Robot positioning method and apparatus, robot and readable storage medium |
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| CN113137967B (en) | 2023-05-26 |
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