CN109143255B - Attitude prediction method for articulated wheel loader - Google Patents

Attitude prediction method for articulated wheel loader Download PDF

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CN109143255B
CN109143255B CN201810724077.1A CN201810724077A CN109143255B CN 109143255 B CN109143255 B CN 109143255B CN 201810724077 A CN201810724077 A CN 201810724077A CN 109143255 B CN109143255 B CN 109143255B
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CN109143255A (en
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祝青园
陈炜
侯亮
卜祥建
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Xiamen University
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    • G01—MEASURING; TESTING
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Abstract

An articulated wheel loader attitude prediction method comprising the steps of: 1) acquiring unstructured pavement information and the current driving condition in front of a loader, and constructing a pavement three-dimensional point cloud topographic map; 2) predicting a future time position of the loader; 3) and predicting the tire grounding point information at the future time by combining the road surface three-dimensional point cloud topographic map and the future time position, and predicting the attitude information of the loader by the three-point model. According to the method, the safety and stability of the operation and the driving of the loader are researched by extracting the front terrain information when the loader runs, the posture of the loader at the future time is predicted in advance, and sufficient time is ensured to make a proper control scheme.

Description

Attitude prediction method for articulated wheel loader
Technical Field
The invention relates to the field of articulated wheel loaders, in particular to a method for predicting the attitude of an articulated wheel loader.
Background
The articulated wheel loader works in the field unstructured terrain environment and has the characteristics of variable structure, variable mass center, variable load and the like, so that the articulated wheel loader is poor in lateral stability and prone to rollover accidents. Because complex terrains are of great importance to the stability of the vehicle body in running, the posture change of the vehicle body caused by excitation of uneven road surfaces is one of the most direct external induction factors of the side tipping accident of the loader, for example, the running stability of the loader is greatly influenced by the bulge or the recess on the slope, and the side tipping accident is easily caused if the control is improper.
The current loader stability research is based on the current structure or stress of a vehicle, the influence of an external topographic environment which is an important factor is not considered, the safety stability of operation and driving of the loader is not researched by extracting front topographic information when the loader runs, the posture of the loader at the future time cannot be predicted in advance, and a proper control scheme is not made in enough time.
Disclosure of Invention
The invention mainly aims to solve the problems that the operation and the safety and the stability of the running of a loader are researched by extracting front terrain information when the loader runs, so that the attitude of the loader at the future time cannot be predicted in advance, and a proper control scheme is not made within enough time, and provides a method for predicting the attitude of an articulated wheel loader.
The invention adopts the following technical scheme:
the method for predicting the attitude of the articulated wheel loader is characterized by comprising the following steps
1) Acquiring unstructured pavement information and the current driving condition in front of a loader, and constructing a pavement three-dimensional point cloud topographic map;
2) predicting a future time position of the loader;
3) and predicting the tire grounding point information at the future time by combining the road surface three-dimensional point cloud topographic map and the future time position, and predicting the attitude information of the loader by the three-point model.
Preferably, the unstructured road information is terrain point cloud information which is acquired through a laser radar; the running condition is that the vehicle body posture information and the vehicle running speed information are respectively acquired by adopting an IMU and an encoder.
Preferably, in step 1), the constructing a three-dimensional point cloud topographic map of the road surface specifically comprises: and a laser radar is arranged on the model prototype and moves along with the model prototype, and the original point cloud coordinates of the terrain acquired by the laser radar are converted into a global coordinate system to perform point cloud optimization.
Preferably, the converting of the original point cloud coordinate of the terrain to the global coordinate system comprises converting a two-dimensional coordinate on a laser radar coordinate system to a vehicle body coordinate system, and converting a coordinate on the vehicle body coordinate system to the global coordinate system, wherein a specific formula is as follows:
Figure BDA0001719183190000021
wherein (x)0,y0,z0) Is a coordinate point on the global coordinate system, alpha is the downward included angle between the scanning plane of the laser radar and the vertical direction, (x)l,yl0) is the coordinate of the point scanned by the laser radar on the laser radar coordinate, and the position of the laser radar relative to the vehicle body coordinate system is (d)lx,0,dlz) V is the speed of the model prototype, Δ t is the time interval, RSFor rotating the transformed rotation matrix, the rotation matrix RSCan be calculated from the current attitude information of the vehicle as follows:
Figure BDA0001719183190000022
wherein (A), (B), (C), (D), (C), (
Figure BDA0001719183190000023
θ, ψ) are attitude information, respectively roll angle, pitch angle, and yaw angle.
Preferably, the point cloud is optimized to reduce the data volume by using volume filtering.
Preferably, in step 2), the future time position of the loader is predicted by using a differential speed traveling model.
Preferably, the predicting the future position of the loader by using the differential speed traveling model includes:
the initial position and the rotating speed information of the two differential wheels are known, and the acceleration of the wheels is not changed in a short time interval, so that the position tracks of the two differential wheels are estimated,
speed vector v of two wheels of loader on xy plane at delta t momentL，vRIs represented as follows:
vL＝((vL0+aL·Δt)·cosψ，(vL0+aL·Δt)·sinψ)
vR＝((vR0+aR·Δt)·Cosψ，(vR0+aR·Δt)·sinψ)
wherein a isL，aRAcceleration of two wheels at that moment, vL0，vR0The velocity at the initial position of the left and right wheels is psi the yaw angle, and the position locus s within a short time interval in the future can be obtained by integrating the velocity vectors of the two wheels at the future timeLAnd sRExpressed as:
sL＝∫vLdt
sR＝∫vRdt。
preferably, in step 3), the attitude information includes a pitch angle and a roll angle; three points in the three-point model are respectively tire grounding points T1 and T2 of two front wheels and a midpoint T3 of two tire grounding points of a rear wheel, the position coordinates of the tire grounding points of the two front wheels and the two rear wheels are respectively solved by adopting a differential speed driving model, then three-dimensional coordinates of the four tire grounding points are obtained by searching constructed front road point clouds, a virtual plane where a vehicle body is located is determined by the three points, and the roll angle and the pitch angle of the vehicle body can be determined by the plane.
Preferably, the virtual plane where one vehicle body is located is determined by three points, which are as follows: the relationship between the virtual plane and the three-dimensional coordinate is as follows:
Figure BDA0001719183190000031
wherein (x)Lf,yLf,zLf)，(xLf,yLr,zLf) And (x)Rr,yRr,zRr) Is a three-dimensional coordinate of the three points,
Figure BDA0001719183190000032
(nx,ny,nz) And (3) as the normal vector of the virtual plane, three unknowns of the formula are totally three, three equations are formed by three known three-point three-dimensional coordinates, and the normal vector is solved.
Preferably, three equations are formed by the known three-point three-dimensional coordinates, and the normal vector is solved by:
the normal vector
Figure BDA0001719183190000033
(nx,ny,nz) Perpendicular to the virtual plane, the vector may be represented as X around the vehicle body coordinate systemcAxis of rotation thetarY of the vehicle body coordinate systemcAxis of rotation thetapCan be represented by the formula θr，θpFor pitch and yaw of vehicles over three-dimensional terrainInclination angle:
Figure BDA0001719183190000034
wherein h is the modulus R of the normal vector of the plane where the three points are locatedX，RYRespectively X around the coordinate system of the vehicle bodyc，YcRotation matrix of shaft to obtain pitch angle thetarAnd a roll angle thetapIs calculated as follows:
Figure BDA0001719183190000035
Figure BDA0001719183190000036
as can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
according to the method, the safety and stability of the operation and the driving of the loader are researched by extracting the front terrain information when the loader runs, the posture of the loader at the future time is predicted in advance, and sufficient time is ensured to make a proper control scheme.
Drawings
FIG. 1 is a schematic diagram of attitude prediction.
FIG. 2 is a schematic view of a vehicle configuration;
FIG. 3 is a schematic illustration of vehicle position prediction;
fig. 4 is a flow chart of articulated wheel loader attitude prediction.
Detailed Description
The invention is further described below by means of specific embodiments.
The invention provides a method for predicting the attitude of an articulated wheel loader, which comprises the following steps as shown in figure 1:
1) acquiring unstructured road surface information in front of a loader and the current driving condition, and constructing a road surface three-dimensional point cloud topographic map, wherein the unstructured road surface information is topographic point cloud information, and the driving condition comprises vehicle body posture information and vehicle driving speed information.
2) A future time position of the loader is predicted.
3) And predicting the tire grounding point information at the future moment by combining the road surface three-dimensional point cloud topographic map and the future moment position, and predicting the attitude information of the loader by using the three-point model, wherein the attitude information comprises a pitch angle and a roll angle.
The method is implemented according to the following steps:
and constructing a multi-sensor information real-time acquisition system, wherein the sensor comprises a laser radar, an IMU and an encoder. Respectively collecting topographic point cloud information, vehicle body posture information and vehicle running speed information in front of the loader. The laser radar has the advantages that the laser radar has a plurality of advantages compared with other sensors (such as a vision camera), and particularly has the advantages of high precision, high acquisition speed, no influence of illumination and the like.
In the step 1), the construction of the road surface three-dimensional point cloud topographic map specifically comprises the following steps: and a laser radar is arranged on the model prototype and moves along with the model prototype, and the original point cloud coordinates of the terrain acquired by the laser radar are converted into a global coordinate system to perform point cloud optimization so as to acquire accurate terrain information.
1.1 coordinate transformation
The coordinates are transformed in two steps: (1) and converting the two-dimensional coordinates on the laser radar coordinate system to a vehicle body coordinate system. (2) And the coordinates on the vehicle body coordinate system are converted to the global coordinate system. The specific formula is as follows:
Figure BDA0001719183190000041
wherein (x)0,y0,z0) Is a coordinate point on the global coordinate system, alpha is the downward included angle between the scanning plane of the laser radar and the vertical direction, (x)l,yl0) is the coordinate of the point scanned by the laser radar on the laser radar coordinate, and the position of the laser radar relative to the vehicle body coordinate system is (d)lx,0,dlz) V isThe running speed of the model prototype, Δ t, is the time interval, RSFor rotating the transformed rotation matrix, the rotation matrix RsThe euler angle can be calculated from the current 3 rotational euler angles of the vehicle: the roll angle, pitch angle and yaw angle are calculated as follows:
Figure BDA0001719183190000042
wherein (A), (B), (C), (D), (C), (
Figure BDA0001719183190000043
θ, ψ) are 3 rotated euler angles: roll, pitch and yaw angles, obtained by the IMU.
1.2 Point cloud optimization
The point cloud geometry includes macroscopic geometric shapes and microscopic arrangements thereof, such as similar dimensions in the transverse direction and the same distance in the longitudinal direction. The voxel grid filter can achieve the function of down-sampling without destroying the geometrical structure of the point cloud. And therefore volume filtering is used to reduce the amount of data.
2) Predicting future time locations of a loader
Fig. 2 and 3 show the differential travel model for predicting the position of the future time when the vehicle travels. Specifically, the initial positions and the rotational speeds of the two differential wheels are known, and the position tracks on which the two differential wheels travel are estimated on the assumption that the acceleration of the wheels is not changed in a short time interval.
Speed vector v of two wheels of loader on xy plane at delta t momentL，vRExpressed as (3), (4):
vL＝((vL0+aL·Δt)·cosψ，(vL0+aL·Δt)·sinψ) (3)
vR＝((vR0+aR·Δt)·cosψ，(vR0+aR·Δt)·sinψ) (4)
wherein a isL，aRAcceleration of two wheels at that moment, vL0，vR0Velocity at the initial position of the left and right wheels, vL0，vR0The initial position velocity of two wheels. Psi is the yaw angle.
The position locus s within the short time interval of two rounds in the future can be obtained by integrating the speed vectors (xy plane) of two rounds at the future timeLAnd sR(in the xy plane), expressed as:
sL＝∫vLdt (5)
sR＝∫vRdt (6)
4) attitude prediction for articulated wheel loader at future time
Due to the rear axle swing structure of an articulated wheel loader, the invention proposes to calculate the attitude of the loader body by determining the body attitude based on a three-point model, as shown in fig. 3. The three points are respectively the tire grounding points T1 and T2 of the two front wheels and the middle point T3 of the two tire grounding points of the rear wheels. The position coordinates (on an XY plane) of the tire grounding points of the two front wheels and the two rear wheels are respectively solved by adopting a differential speed driving model, then three-dimensional coordinates of the four tire grounding points are obtained by searching the constructed front road point cloud, and a virtual plane where a vehicle body is located is determined by three points. From this plane the roll and pitch angles can be determined. The relationship between the virtual plane and the three-point three-dimensional coordinate of the loader body at the future time can be expressed as follows:
Figure BDA0001719183190000051
wherein (x)Lf,yLf,zLf)，(xLf,yLr,zLf) And (x)Rr,yRr,zRr) Is the three-dimensional coordinate of three points in the three-point method,
Figure BDA0001719183190000052
(nx,ny,nz) Is the normal vector of the plane.
Since the unknowns of equation (7) are three in number, three equations consisting of three-dimensional coordinates of known three points can be solved for the three unknowns. Because of the vector
Figure BDA0001719183190000053
(nx,ny,nz) Perpendicular to the virtual contact plane, so the vector can be expressed as X around the vehicle body coordinate systemcAxis of rotation thetarIn Y around the vehicle body coordinate systemcAxis of rotation thetapAnd may be represented by formula (8). Thetar，θpThe pitch and roll angles of the vehicle over three-dimensional terrain.
Figure BDA0001719183190000061
Wherein h is the modulus of the normal vector of the plane where the three points are located:
RX，RYrespectively X around the coordinate system of the vehicle bodyc，YcRotation matrix of the shaft:
the pitch angle theta can be obtained from the formulas (7) to (8)rAnd a roll angle thetapIs calculated as follows:
Figure BDA0001719183190000062
Figure BDA0001719183190000063
the above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (5)

1. The method for predicting the attitude of the articulated wheel loader is characterized by comprising the following steps
1) Acquiring unstructured pavement information and the current driving condition in front of a loader, and constructing a pavement three-dimensional point cloud topographic map;
2) predicting a future time position of the loader; predicting the future time position of the loader by adopting a differential speed driving model, which comprises the following specific steps:
the initial position and the rotating speed information of the two differential wheels are known, and the acceleration of the wheels is not changed in a short time interval, so that the position tracks of the two differential wheels are estimated,
speed vector v of two wheels of loader on xy plane at delta t momentL，vRIs represented as follows:
vL＝((vL0+aL·Δt)·cosψ，(vL0+aL·Δt)·sinψ)
vR＝((vR0+aR·Δt)·cosψ，(vR0+aR·Δt)·sinψ)
wherein a isL，aRAcceleration of two wheels at that moment, vL0，vR0The velocity at the initial position of the left and right wheels is psi the yaw angle, and the position locus s within a short time interval in the future can be obtained by integrating the velocity vectors of the two wheels at the future timeLAnd sRExpressed as:
sL＝∫vLdt
sR＝∫vRdt；
3) predicting tire grounding point information at a future moment by combining a road surface three-dimensional point cloud topographic map and the future moment position, and predicting attitude information of the loader by a three-point model; the attitude information comprises a pitch angle and a roll angle; three points in the three-point model are respectively tire grounding points T1 and T2 of two front wheels and a midpoint T3 of two tire grounding points of a rear wheel, the position coordinates of the tire grounding points of the two front wheels and the two rear wheels are respectively solved by adopting a differential speed driving model, then three-dimensional coordinates of the four tire grounding points are obtained by searching constructed front road point clouds, a virtual plane where a vehicle body is located is determined by the three points, and the roll angle and the pitch angle of the vehicle body can be determined by the plane; the method comprises the following specific steps: the relationship between the virtual plane and the three-dimensional coordinate is as follows:
Figure FDA0002583741260000011
wherein (x)Lf，yLf，zLf)，(xLf，yLr，zLf) And (x)Rr，yRr，zRr) Is a three-dimensional coordinate of the three points,
Figure FDA0002583741260000013
Figure FDA0002583741260000014
for the normal vector of the virtual plane, because the unknown numbers of the formula are three, three equations are formed by three known three-point three-dimensional coordinates, and the solving of the normal vector specifically comprises the following steps:
the normal vector
Figure FDA0002583741260000012
Perpendicular to the virtual plane, the vector may be represented as X around the vehicle body coordinate systemcAxis of rotation thetarY of the vehicle body coordinate systemcAxis of rotation thetapCan be represented by the formula θr，θpPitch and roll angles of the vehicle over three-dimensional terrain:
Figure FDA0002583741260000021
wherein h is the modulus of the normal vector of the plane where the three points are located: rX，RYRespectively X around the coordinate system of the vehicle bodyc，YcRotation matrix of shaft to obtain pitch angle thetarAnd a roll angle thetapIs calculated as follows:
Figure FDA0002583741260000022
Figure FDA0002583741260000023
2. the articulated wheel loader attitude prediction method of claim 1, wherein the unstructured road information is terrain point cloud information, collected by a lidar; the running condition is that the vehicle body posture information and the vehicle running speed information are respectively acquired by adopting an IMU and an encoder.
3. The method for predicting the attitude of an articulated wheel loader according to claim 1, wherein in step 1), the constructing a three-dimensional point cloud topographic map of the road surface is specifically: and a laser radar is arranged on the model prototype and moves along with the model prototype, and the original point cloud coordinates of the terrain acquired by the laser radar are converted into a global coordinate system to perform point cloud optimization.
4. The method of claim 3, wherein the transformation of the cloud coordinates of the terrain raw point to the global coordinate system comprises transforming the two-dimensional coordinates of the lidar coordinate system to the body coordinate system and transforming the coordinates of the body coordinate system to the global coordinate system, wherein the following formula is provided:
Figure FDA0002583741260000024
wherein (x)0，y0，z0) Is a coordinate point on the global coordinate system, alpha is the downward included angle between the scanning plane of the laser radar and the vertical direction, (x)1，y10) is the coordinate of the point scanned by the laser radar on the laser radar coordinate, and the position of the laser radar relative to the vehicle body coordinate system is (d)lx，0，dlz) V is the speed of the model prototype, Δ t is the time interval, RSFor rotating the transformed rotation matrix, the rotation matrix RSCan be calculated from the current attitude information of the vehicle as follows:
Figure FDA0002583741260000025
wherein the content of the first and second substances,
Figure FDA0002583741260000026
the attitude information includes roll angle, pitch angle and yaw angle.
5. The method of claim 3, wherein the point cloud is optimized to reduce data volume using voxel filtering.
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