CN105573310B - Coal mine roadway robot positioning and environment modeling method - Google Patents
Coal mine roadway robot positioning and environment modeling method Download PDFInfo
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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
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 state
For 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 matrix
T 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＝Hkxk+ξk，
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 odometer
And 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 iso-ρoWhen 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 iso-ρoWhen 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 lidaro,αo) 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 radaro,αo) 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:
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 state
For 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 matrix
T 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＝Hkxk+ξk。
Wherein:
the observed quantity zk is the displacement increment of inertial navigation solution from the time tk-1 to the time tk
And 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 odometer
And 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 k
Calculating 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 iso-ρoWhen 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 iso-ρoAnd 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 lidaro,αo) 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 radaro,αo) 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:
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 matrix
T 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＝Hkxk+ξk，
Wherein: observed quantity zkIs from tk-1Time tkMoment, inertia resolved displacement increment
Incremental displacement from a odometer measurement
A difference of
After eliminating slip and sliding random errors, the odometer measures
With 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 odometer
And 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 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 iso-ρoWhen 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 iso-ρoWhen 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 lidaro,αo) 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 radaro,αo) 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:
When the two-dimensional lidar is scanned in pitch,
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| CN108051837A (en) * | 2017-11-30 | 2018-05-18 | 武汉大学 | Multiple-sensor integration indoor and outdoor mobile mapping device and automatic three-dimensional modeling method |
| CN108801240A (en) * | 2018-03-30 | 2018-11-13 | 浙江大华技术股份有限公司 | A kind of air navigation aid, apparatus and system |
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| CN110058587B (en) * | 2019-03-18 | 2022-09-13 | 西安科技大学 | Coal mine fully-mechanized mining face inspection unmanned vehicle based on SLAM technology and autonomous inspection method |
| CN110174136B (en) * | 2019-05-07 | 2022-03-15 | 武汉大学 | Intelligent detection robot and intelligent detection method for underground pipeline |
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