CN105573310B - Coal mine roadway robot positioning and environment modeling method - Google Patents

Coal mine roadway robot positioning and environment modeling method Download PDF

Info

Publication number
CN105573310B
CN105573310B CN201410534858.6A CN201410534858A CN105573310B CN 105573310 B CN105573310 B CN 105573310B CN 201410534858 A CN201410534858 A CN 201410534858A CN 105573310 B CN105573310 B CN 105573310B
Authority
CN
China
Prior art keywords
robot
environment
error
angle
laser radar
Prior art date
Application number
CN201410534858.6A
Other languages
Chinese (zh)
Other versions
CN105573310A (en
Inventor
王芳
马娟荣
吕翀
吕博
Original Assignee
航天科工智能机器人有限责任公司
Filing date
Publication date
Application filed by 航天科工智能机器人有限责任公司 filed Critical 航天科工智能机器人有限责任公司
Priority to CN201410534858.6A priority Critical patent/CN105573310B/en
Publication of CN105573310A publication Critical patent/CN105573310A/en
Application granted granted Critical
Publication of CN105573310B publication Critical patent/CN105573310B/en

Links

Abstract

The invention belongs to a method suitable for autonomous positioning and environment modeling of a ground mobile robot in a coal mine underground roadway environment. The method comprises the following steps of 1) a robot combination positioning algorithm; 2) positioning correction based on the environment information; 3) robot 3D environmental modeling method. The invention has the advantages that the three-dimensional model of the roadway can be automatically generated through the coal mine roadway measuring mobile robot, the underground roadway and the spatial relationship thereof can be three-dimensionally, intuitively and accurately expressed and reflected, and the invention has positive significance for guiding the site production and training the safety production of miners.

Description

Coal mine roadway robot positioning and environment modeling method

Technical Field

The invention belongs to a navigation control method of an intelligent mobile robot, and particularly relates to a method suitable for autonomous positioning and environment modeling of a ground mobile robot in a coal mine underground roadway environment.

Background

The intelligent mobile robot is a robot system which can sense the environment and the self state through a sensor, realize autonomous navigation movement facing a target in the environment with obstacles and further complete a preset task. In order to realize autonomous navigation movement of the robot, a series of problems such as trajectory planning, movement control, environment modeling, real-time positioning and the like must be solved.

The three-dimensional model of the coal mine tunnel can reflect the underground tunnel and the spatial relationship thereof in a three-dimensional and accurate manner, has positive significance for guiding field production, training miner safety production and implementing underground rescue, is one of important contents of mine digital construction, and lays a foundation for realizing visual virtual reproduction of the underground environment of the mine.

The underground tunnel environment of the coal mine is complex, particularly when the coal mine collapses, gas explosion and other accidents happen, the underground condition is very bad, great difficulty is brought to the measurement work, and even the safety of the measuring personnel is endangered. The intelligent robot technology is applied to coal mine tunnel detection, and a safe, quick and effective solution is provided for underground coal mine surveying and mapping.

Disclosure of Invention

The invention aims to provide a coal mine tunnel robot positioning and environment modeling method which can be used for a robot positioning and environment modeling algorithm for coal mine underground tunnel detection. And the synchronous positioning and mapping algorithm module is used for establishing a 3D environment model of the underground roadway while accurately positioning by fusing information of various sensors.

The invention is realized in such a way that a coal mine tunnel robot positioning and environment modeling method comprises the following steps,

1) a robot combined positioning algorithm;

2) positioning correction based on the environment information;

3) robot 3D environmental modeling method.

The method comprises the following steps that 1) in the underground environment of the coal mine, a combined navigation positioning mode of a medium-precision inertial navigation system and a mileage instrument based on Kalman filtering is selected, wherein the medium-precision inertial navigation system has strong attitude keeping capacity, but positioning errors are accumulated continuously along with time; the measurement error of the mileage meter generally increases along with the mileage, the mileage meter and the mileage meter have strong complementarity, a high-precision autonomous navigation system can be formed by combination,

the state equation of the inertial navigation/mileage gauge combined navigation system is as follows: x is the number ofk=Akxk-1+wk-1

Wherein:

variable of stateFor accelerometer random constant zero offset error, Δ vk、ΔskSpeed error and position error, Δ K, respectively, resolved for inertial navigationkThe error of the calibration coefficient of the mileage gauge;

state transition matrixT is sampling time;

system noise WkIs zero mean white noise, noiseThe variance matrix is QkI.e. E [ w ]k]=0、

The measurement equation of the inertial navigation/mileage gauge combined navigation system is as follows: z is a radical ofk=Hkxkk

Wherein: observed quantity ZkIs from tk-1Time tkAt the moment, the difference between the increment of displacement calculated by inertial navigation and the increment of displacement measured by the odometer, i.e.After random errors such as slipping and sliding are eliminated, the measured value and the real value d of the odometerkThe relationship between is

Measuring matrix

Measurement noise ξkFor zero mean white noise, the measured noise variance matrix is Rk, i.e., E [ ξk]=0、

In the step 2), errors of the odometer mainly comprise scale coefficient errors and random errors caused by slipping or sliding, a global coordinate system of robot navigation is set as an OXYZ (oxy-Z) -northeast-day coordinate system, a coordinate origin is set as a starting point of the robot navigation, a relative coordinate system OrXrYr of the robot is defined as a robot origin, a forward direction of the robot is set as an Xr axis, a direction perpendicular to the Xr axis and anticlockwise by 90 degrees is set as an YR axis, a heading angle psi of the robot is defined as an included angle of the Xr axis of the robot relative to an X axis and is positive in the north, a pitch angle theta is defined as an included angle between a projection of the Xr axis in an OYZ plane and a Y axis and is positive in the upward direction, a target heading angle α is defined as an included angle of a connecting line of the target and the robot relative to the Xr axis and is positive in the anticlockwise direction,

selecting environmental information detected by a laser radar to correct the error of the mileage gauge, and setting the pitching angle of the laser radar to be 0 during positioning correction, wherein the algorithm comprises the following steps:

(1) according to displacement increment provided by the odometerAnd the heading angle psi provided by the inertial navigation systemkAngle of pitch thetakFrom time k-1, the position (x) of the robot in the global mapk-1,yk-1,zk-1) Estimate the predicted position of the robot at time k

(2) Location (x) in a global map according to known environmental featureso,yo,zo) And predicted position (x ') of robot k time'k,y′k,z′k) Calculating the distance l of the environmental characteristic relative to the robot at the moment ko

(3) Scanning the surrounding environment by the laser radar at the time k, extracting the environment characteristics from the scanning points, and obtaining the actually measured distance rho of the currently concerned environment characteristics in the robot coordinate systemoAnd angle αo

(4) Removing abnormal data: comparison loAnd rhooWhen l isooWhen the wheel slip is larger than the set slip threshold value M, the wheel of the robot is considered to be in a slip state; when l isooWhen the sliding threshold value N is smaller than the set sliding threshold value N, the wheels of the robot are considered to be in a sliding state; the abnormal data does not participate in navigation calculation and error correction;

(5) coordinates (rho) in the robot coordinate system from the feature points obtained by the lidaroo) And the location (x) of the environmental feature in the global mapo,yo,zo) Calculating the position (x) of the robot at time kk,yk,zk) And the actual mileage d from the time k-1 to the time k of the robotk

(6) Calculating the error delta K of the graduation coefficient of the mileage instrumentkUsing Δ K in the combined navigation algorithmkCorrecting the measured value of the mileage gauge:

in the step 3), in order to realize the 3D environment modeling of the coal mine tunnel, a two-dimensional laser radar and a high-precision electric control rotary table are selected to form an environment detection system, the two-dimensional laser radar can rotate around an YR axis under the drive of the electric control rotary table, the pitch angle β of the laser radar is defined as positive when the laser radar faces upwards and negative when the laser radar faces downwards,

the environment detected by the radar is represented by a two-dimensional array T in a two-dimensional rectangular grid and height map mannerm×nRecording the environment map:

according to the position (x) of the robot at the moment kk,yk,zk) Heading angle psikAngle of pitch thetakAnd lidar pitch angle βkAnd obstacle information (p) detected by the laser radaroo) The coordinates (x) of the obstacle in the global coordinate system can be calculatedo,yo,zo):

Assuming that the size of the grid is w × w, the two-dimensional coordinates (x) of the grid occupied by the obstacleg,o,yg,o) Comprises the following steps:

(int () represents a rounding operation)

When the two-dimensional lidar is scanned in pitch,

the invention has the advantages that the three-dimensional model of the roadway can be automatically generated through the coal mine roadway measuring mobile robot, the underground roadway and the spatial relationship thereof can be three-dimensionally, intuitively and accurately expressed and reflected, and the invention has positive significance for guiding the site production and training the safety production of miners.

Drawings

FIG. 1 is a control system schematic;

fig. 2 is a schematic diagram of an information processing algorithm.

Detailed Description

The invention is described in detail below with reference to the following figures and examples:

the control principle of the coal mine tunnel detection robot is shown in figure 1. The internal sensors include a odometer and an inertial navigation system for measuring the displacement and attitude of the robot. The external sensor comprises a laser radar, a camera, an ultrasonic ranging sensor and an infrared ranging sensor, wherein the laser radar and the camera are used for directly sensing environmental information, and the ultrasonic and infrared ranging sensors are used for emergently avoiding obstacles. The vehicle-mounted computer is used for collecting information of each sensor, processing the information, making a decision and sending a control instruction to the driving unit.

The algorithm principle of the information acquisition processing module is shown in fig. 2. The internal sensor odometer and the inertial navigation system obtain the current position and the course of the robot through a combined navigation algorithm; filtering the laser radar data, extracting environmental features from the camera information after image processing, and fusing the two types of sensor data by adopting a feature level data fusion algorithm; updating a global map by the pose and environmental characteristics of the robot through an SLAM algorithm; and the parameters of the mileage gauge model are corrected according to the global map and the current environmental characteristics, so that the positioning precision is improved.

A coal mine tunnel robot positioning and environment modeling method comprises the following steps:

1. robot combined positioning algorithm

In the coal mine underground environment, the robot cannot receive GPS information, so the robot must have an autonomous positioning function. In addition, in order to ensure the accuracy of environment mapping, a high requirement is put forward on the positioning accuracy of the robot during long-term navigation. Therefore, a combined navigation and positioning mode of a Kalman filtering-based medium-precision inertial navigation system and a mileage instrument is selected. The medium-precision inertial navigation system has strong attitude keeping capability, but the positioning error can be continuously accumulated along with time; while the measurement error of the odometer generally increases with mileage. The two have strong complementarity, and a high-precision autonomous navigation system can be formed by combination.

The state equation of the inertial navigation/mileage gauge combined navigation system is as follows: x is the number ofk=Akxk-1+wk-1

Wherein:

variable of stateFor accelerometer random constant zero offset error, Δ vk、ΔskSpeed error and position error, Δ K, respectively, resolved for inertial navigationkThe error of the calibration coefficient of the mileage gauge;

state transition matrixT is sampling time;

the system noise Tk is zero mean white noise and the noise variance matrix is Qk, i.e., E [ w ]k]=0、

The measurement equation of the inertial navigation/mileage gauge combined navigation system is as follows: z is a radical ofk=Hkxkk

Wherein:

the observed quantity zk is the displacement increment of inertial navigation solution from the time tk-1 to the time tkAnd mileage instrument

Difference between measured increments of displacement, i.e.(after eliminating random errors such as slipping and sliding, the measured value and the real value d of the odometerkThe relationship between is

Measuring matrix

Measurement noise ξkFor zero mean white noise, the measured noise variance matrix is Rk, i.e., E [ ξk]=0、

2. Location correction based on environmental information

The odometer is based on an encoder mounted on the drive wheel to convert the wheel rotation into a linear displacement relative to the ground, and has certain limitations. The odometer errors mainly include scale factor errors, and random errors due to slippage or sliding. In order to maintain the positioning accuracy of the integrated navigation system, it is important to suppress the error of the odometer.

The robot relative coordinate system OrXrYr is defined as an included angle of an Xr axis of the robot relative to an X axis and is positive in a north bias, a pitch angle theta is defined as an included angle between a projection of the Xr axis in an OYZ plane and a Y axis and is positive in an upward direction, and a target heading angle α is defined as an included angle between a connecting line of a target and the robot relative to the Xr axis and is positive in a counterclockwise direction.

Environmental information detected by the laser radar is selected to correct the error of the odometer. The pitch angle of the laser radar is set to 0 in the positioning correction. The algorithm comprises the following steps:

(7) according to displacement increment provided by the odometerAnd the heading angle psi provided by the inertial navigation systemkAngle of pitch thetakFrom time k-1, the position (x) of the robot in the global mapk-1,yk-1,zk-1) (obtained by an integrated navigation system) estimates the predicted position of the robot at time k

(8) Location (x) in a global map according to known environmental featureso,yo,zo) And predicted position of robot at time kCalculating the distance l of the environmental characteristic relative to the robot at the moment ko

(9) Scanning the surrounding environment by the laser radar at the moment k, extracting the environmental characteristics from the scanning points to obtain the current timeMeasured distance rho of environment characteristic of note in robot coordinate systemoAnd angle αo

(10) Removing abnormal data: comparison loAnd rhooWhen l isooWhen the wheel slip is larger than the set slip threshold value M, the wheel of the robot is considered to be in a slip state; when l isooAnd when the sliding speed is less than the set sliding threshold value N, the wheels of the robot are considered to be in a sliding state. The abnormal data does not participate in navigation calculation and error correction.

(11) Coordinates (rho) in the robot coordinate system from the feature points obtained by the lidaroo) And the location (x) of the environmental feature in the global mapo,yo,zo) Calculating the position (x) of the robot at time kk,yk,zk) And the actual mileage d from the time k-1 to the time k of the robotk

(12) Calculating the error delta K of the graduation coefficient of the mileage instrumentkUsing Δ K in the combined navigation algorithmkCorrecting the measured value of the mileage gauge:

3. robot 3D environment modeling method

In order to realize the 3D environment modeling of the coal mine tunnel, a two-dimensional laser radar and a high-precision electric control rotary table are selected to form an environment detection system, the two-dimensional laser radar can rotate around an YR axis under the driving of the electric control rotary table, and the pitch angle β of the laser radar is defined as positive when the laser radar faces upwards and negative when the laser radar faces downwards.

The environment detected by the radar is represented by means of a two-dimensional cartesian rectangular grid and a height map. Using a two-dimensional array Tm×nRecording the environment map:

according to the position (x) of the robot at the moment kk,yk,zk) Heading angle psikAngle of pitch thetakAnd lidar pitch angle βkAnd obstacle information (p) detected by the laser radaroo) The coordinates (x) of the obstacle in the global coordinate system can be calculatedo,yo,zo):

Assuming that the size of the grid is w × w, the two-dimensional coordinates (x) of the grid occupied by the obstacleg,o,yg,o) Comprises the following steps:

(int () represents a rounding operation)

When the two-dimensional lidar is scanned in pitch,

Claims (1)

1. a coal mine tunnel robot positioning and environment modeling method is characterized in that: which comprises the following steps of,
1) a robot combined positioning algorithm;
2) positioning correction based on the environment information;
3) a robot 3D environment modeling method;
the method comprises the following steps that 1) in the underground environment of the coal mine, a combined navigation positioning mode of a medium-precision inertial navigation system and a mileage instrument based on Kalman filtering is selected, wherein the medium-precision inertial navigation system has strong attitude keeping capacity, but positioning errors are accumulated continuously along with time; the measurement error of the mileage meter generally increases along with the mileage, and the mileage meter have strong complementarity throughThe combination can form a high-precision autonomous navigation system, and the state equation of the inertial navigation/mileage gauge combined navigation system is as follows: x is the number ofk=Akxk-1+wk-1
Wherein:
state variable xk=[▽k,Δvk,Δsk,ΔKk],▽kFor accelerometer random constant zero offset error, Δ vk、ΔskSpeed error and position error, Δ K, respectively, resolved for inertial navigationkThe error of the calibration coefficient of the mileage gauge;
state transition matrixT is sampling time;
system noise wkIs zero mean white noise, and the noise variance matrix is QkI.e. E [ w ]k]=0、
The measurement equation of the inertial navigation/mileage gauge combined navigation system is as follows: z is a radical ofk=Hkxkk
Wherein: observed quantity zkIs from tk-1Time tkMoment, inertia resolved displacement incrementIncremental displacement from a odometer measurementA difference ofAfter eliminating slip and sliding random errors, the odometer measuresWith the true value dkIn betweenThe relationship is
Measuring matrix
Measurement noise ξkIs zero mean white noise, and the measured noise variance matrix is RkI.e. E [ ξk]=0、
The error of the odometer in the step 2) comprises scale coefficient error and random error caused by slipping or sliding, a global coordinate system of robot navigation is OXYZ as a northeast-heaven coordinate system, a coordinate origin is an initial point of the robot navigation, a relative coordinate system OrXrYr of the robot is defined by taking the robot as the origin, a forward direction of the robot is taken as an Xr axis, a direction which is perpendicular to the Xr axis and is anticlockwise for 90 degrees is taken as an Yr axis, a heading angle phi of the robot in the global coordinate system is defined by an included angle of the Xr axis of the robot relative to the X axis and is positive in the north direction, a pitch angle theta is defined by an included angle between a projection of the Xr axis in an OYZ plane and the Y axis, the upward direction is positive, a target heading angle α is defined by an included angle of a connecting line of a target and the robot relative to the Xr axis and is positive in the anticlockwise direction,
selecting environmental information detected by a laser radar to correct the error of the mileage gauge, and setting the pitching angle of the laser radar to be 0 during positioning correction, wherein the algorithm comprises the following steps:
(1) according to displacement increment provided by the odometerAnd the heading angle psi provided by the inertial navigation systemkAngle of pitch thetakFrom time k-1, the position (x) of the robot in the global mapk-1,yk-1,zk-1) Estimate the predicted position of the robot at time k
(2) Location (x) in a global map according to known environmental featureso,yo,zo) And predicted position of robot at time kCalculating the distance l of the environmental characteristic relative to the robot at the moment ko
(3) Scanning the surrounding environment by the laser radar at the time k, extracting the environment characteristics from the scanning points, and obtaining the actually measured distance rho of the currently concerned environment characteristics in the robot coordinate systemoAnd angle αo
(4) Removing abnormal data: comparison loAnd rhooWhen l isooWhen the wheel slip is larger than the set slip threshold value M, the wheel of the robot is considered to be in a slip state; when l isooWhen the sliding threshold value N is smaller than the set sliding threshold value N, the wheels of the robot are considered to be in a sliding state; the abnormal data does not participate in navigation calculation and error correction;
(5) coordinates (rho) in the robot coordinate system from the feature points obtained by the lidaroo) And the location (x) of the environmental feature in the global mapo,yo,zo) Calculating the position (x) of the robot at time kk,yk,zk) And the actual mileage d from the time k-1 to the time k of the robotk
(6) Calculating the error delta K of the graduation coefficient of the mileage instrumentkUsing Δ K in the combined navigation algorithmkCorrecting the measured value of the mileage gauge:
in the step 3), in order to realize the 3D environment modeling of the coal mine tunnel, a two-dimensional laser radar and a high-precision electric control rotary table are selected to form an environment detection system, the two-dimensional laser radar can rotate around an YR axis under the drive of the electric control rotary table, the pitch angle β of the laser radar is defined as positive when the laser radar faces upwards and negative when the laser radar faces downwards,
the environment detected by the radar is represented by a two-dimensional array T in a two-dimensional rectangular grid and height map mannerm×nRecording the environment map:
according to the position (x) of the robot at the moment kk,yk,zk) Heading angle psikAngle of pitch thetakAnd lidar pitch angle βkAnd obstacle information (p) detected by the laser radaroo) The coordinates (x) of the obstacle in the global coordinate system can be calculatedo,yo,zo):
Assuming that the size of the grid is w × w, the two-dimensional coordinates (x) of the grid occupied by the obstacleg,o,yg,o) Comprises the following steps:
int () represents a rounding operation
When the two-dimensional lidar is scanned in pitch,
CN201410534858.6A 2014-10-11 Coal mine roadway robot positioning and environment modeling method CN105573310B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410534858.6A CN105573310B (en) 2014-10-11 Coal mine roadway robot positioning and environment modeling method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410534858.6A CN105573310B (en) 2014-10-11 Coal mine roadway robot positioning and environment modeling method

Publications (2)

Publication Number Publication Date
CN105573310A CN105573310A (en) 2016-05-11
CN105573310B true CN105573310B (en) 2020-06-26

Family

ID=

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1731091A (en) * 2005-07-13 2006-02-08 李俊峰 vehicle-carrying quick positioning and orienting method
CN101201626A (en) * 2007-12-10 2008-06-18 华中科技大学 Freedom positioning system for robot
CN102288176A (en) * 2011-07-07 2011-12-21 中国矿业大学(北京) Coal mine disaster relief robot navigation system based on information integration and method
JP5170687B2 (en) * 2006-10-18 2013-03-27 国立大学法人 奈良先端科学技術大学院大学 Remote control system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1731091A (en) * 2005-07-13 2006-02-08 李俊峰 vehicle-carrying quick positioning and orienting method
JP5170687B2 (en) * 2006-10-18 2013-03-27 国立大学法人 奈良先端科学技術大学院大学 Remote control system
CN101201626A (en) * 2007-12-10 2008-06-18 华中科技大学 Freedom positioning system for robot
CN102288176A (en) * 2011-07-07 2011-12-21 中国矿业大学(北京) Coal mine disaster relief robot navigation system based on information integration and method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于位置修正的井下车辆INS/Odometer组合导航系统;李增科等;《煤炭学报》;20131130;第38卷(第11期);第2077-2083页 *
惯性与传感器技术在智能机器人导航控制中的应用;王芳等;《惯性技术发展动态发展方向研讨会文集》;20121231;第14-19页 *

Similar Documents

Publication Publication Date Title
US8260483B2 (en) Guidance, navigation, and control system for a vehicle
EP3336489A1 (en) Method and system for automatically establishing map indoors by mobile robot
Cho et al. A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding
CN103472823B (en) A kind of grating map creating method of intelligent robot
US8838273B2 (en) System for autonomously dispensing media on large scale surfaces
US8775063B2 (en) System and method of lane path estimation using sensor fusion
CN105300375B (en) A kind of robot indoor positioning and air navigation aid based on single vision
DE69915156T2 (en) Automatic guiding and measuring device
Li et al. LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments
CN103424114B (en) A kind of full combined method of vision guided navigation/inertial navigation
KR20130128024A (en) Measuring system for determining 3d coordinates of an object surface
Gruyer et al. Map-aided localization with lateral perception
Cheng et al. Visual odometry on the Mars exploration rovers-a tool to ensure accurate driving and science imaging
CN106840179B (en) Intelligent vehicle positioning method based on multi-sensor information fusion
CN102538781B (en) Machine vision and inertial navigation fusion-based mobile robot motion attitude estimation method
US7693654B1 (en) Method for mapping spaces with respect to a universal uniform spatial reference
EP2165155B1 (en) Device for assisting in the navigation of a person
CN105066917B (en) A kind of small pipeline GIS-Geographic Information System measuring device and its measurement method
CN104236548A (en) Indoor autonomous navigation method for micro unmanned aerial vehicle
US8903576B2 (en) Device, program product and computer implemented method for touchless metrology using an inertial navigation system and laser
Montemerlo et al. Large-scale robotic 3-d mapping of urban structures
CN103337066B (en) 3D obtains the calibration steps of system
Rose et al. An integrated vehicle navigation system utilizing lane-detection and lateral position estimation systems in difficult environments for GPS
CN103175524B (en) A kind of position of aircraft without view-based access control model under marking environment and attitude determination method
US20140309841A1 (en) Autonomous Mobile System

Legal Events

Date Code Title Description
PB01 Publication
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20160719

Address after: 100074 Beijing, Fengtai District, Yungang District, South West Lane, building 2, floor 20

Applicant after: Aerospace Science and technology intelligent robot Co., Ltd.

Address before: 100074, No. 1, Yungang West Road, Beijing, Fengtai District

Applicant before: Beijing Automation Control Equipment Research Institute

Effective date of registration: 20160719

Address after: 100074 Beijing, Fengtai District, Yungang District, South West Lane, building 2, floor 20

Applicant after: Aerospace Science and technology intelligent robot Co., Ltd.

Address before: 100074, No. 1, Yungang West Road, Beijing, Fengtai District

Applicant before: Beijing Automation Control Equipment Research Institute

GR01 Patent grant