CN110262508B - Automatic guiding system and method applied to unmanned freight vehicle in closed field - Google Patents
Automatic guiding system and method applied to unmanned freight vehicle in closed field Download PDFInfo
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Abstract
An automatic guiding system and method applied to unmanned freight vehicles in a closed field belong to the field of unmanned. The existing technology for reforming closed fields such as wharfs and the like to carry out unmanned driving has the problems of high reforming cost and low automatic driving navigation accuracy. The invention uses the positioning system to obtain the pose information of the vehicle center point in real time by the main positioning system and the auxiliary positioning system; monitoring the lane through a sensing system, detecting, identifying and classifying the obstacle and judging the running speed; the decision system receives the data information uploaded by the positioning system and the sensing system to decide the vehicle and perform local path planning; and calculating a control command according to the local path reference point information planned by the decision system through the control system, and transmitting the control command to the executing mechanism, wherein the control command comprises a target angle and a target speed of the wheel. The invention is suitable for the reconstruction of unmanned automatic guidance of the closed field, and has high navigation precision.
Description
Technical Field
The invention relates to an automatic guiding system and method applied to an unmanned freight vehicle in a closed field.
Background
The automatic guiding system of the unmanned vehicle is mainly realized through a sensing system, a positioning system, a decision making system and a control system. At present, an automatic guiding system of an unmanned vehicle is realized by various vehicle-mounted sensors, such as GPS positioning, laser radar, millimeter wave radar and cameras. Because each sensor has respective use scene and function, GPS location is more accurate under the open and non-shielding condition, laser radar can realize the range finding to the barrier, also can realize auxiliary positioning, millimeter wave radar can realize range finding and speed measurement, and the vision camera can realize functions such as lane line discernment, lane keep, barrier discernment and classification. In order to improve the reliability of an automatic guiding system of an unmanned vehicle, the prior art mostly adopts a scheme of multi-sensor fusion, namely, the functions of the sensors are fused together.
The automatic guiding system of the closed scene of the wharf is different from the system composition in principle according to the selected sensor, taking the current automatic wharf as an example, mainly adopts a magnetic nail positioning mode and a navigation system composed of an inertial gyroscope, and the method has the remarkable defects that: (1) The paved magnetic nails and the induction antennas on the vehicle are expensive, and the number and the cost are high; (2) The requirements on the field are strict, the pavement needs to be remodeled, and interference substances such as metal cannot exist in the pavement and a certain depth range, otherwise, the accuracy of detecting the magnetic nails by the induction antenna can be influenced; (3) For the traditional old wharf, the method is not suitable for the positioning method, and the reconstruction or reconstruction cost is high; (4) Although the magnetic nails are positioned accurately, the navigation path is limited, the flexibility is limited, and vehicles cannot pass through the area without the magnetic nails. In order to effectively solve the defects, the invention provides an automatic guiding system applied to a closed scene unmanned vehicle, which adopts a GPS as a main positioning method and adopts multi-sensor fusion as an auxiliary positioning automatic guiding system, thereby avoiding the high-cost and high-consumption investment of site reformation design, magnetic nail laying and the like.
Disclosure of Invention
The invention aims to solve the problems of high reconstruction cost and low automatic driving navigation accuracy existing in the existing unmanned technology for reconstructing closed places such as wharfs and the like, and provides an automatic guiding system and method applied to unmanned freight vehicles in the closed places.
An automatic guiding system applied to a closed-field unmanned freight vehicle comprises a positioning system, a sensing system, a decision making system and a control system,
The positioning system adopts GPS and inertial navigation as a main positioning system, and adopts a combination of a vehicle-mounted sensor group and a road side sensor group as an auxiliary positioning system, and is used for uploading the pose information of an accurate and stable vehicle center point obtained by the main positioning system and the auxiliary positioning system in real time to the decision system;
The sensing system adopts a laser radar, a millimeter wave radar and a visual camera multi-sensor combination, is used for monitoring a lane, detecting, identifying and classifying obstacles and judging the running speed, and uploading the judged information to the decision system;
And the decision system is used for deciding the vehicle by combining the data information uploaded by the positioning system and the sensing system and carrying out local path planning, wherein the algorithm of the local path planning is a method for generating a track by adopting a spiral curve.
An automatic guidance method for use on a closed-field unmanned freight vehicle, said method comprising the steps of:
The method comprises the steps that firstly, accurate and stable pose information of a vehicle center point, which is obtained in real time by a main positioning system and an auxiliary positioning system, is uploaded to a decision system through the positioning system; the positioning system comprises a main positioning system and auxiliary positioning systems, wherein GPS and inertial navigation are adopted as the main positioning systems of the positioning system, and a combination of a vehicle-mounted sensor group and a road side sensor group is adopted as the auxiliary positioning systems;
monitoring the lane through the sensing system, detecting, identifying and classifying the obstacle, judging the running speed, and uploading the judged information to the decision making system; the sensing system adopts a multi-sensor mode of a laser radar, a millimeter wave radar and a visual camera;
Step three, the decision system receives the data information uploaded by the positioning system and the sensing system to make a decision on the vehicle, and local path planning is performed;
And step four, calculating a control command through the control system according to the local path reference point information planned by the decision system, and transmitting the control command to the executing mechanism, wherein the control command comprises a target angle and a target speed of the wheels.
The beneficial effects of the invention are as follows:
the invention provides an automatic guiding system and a method which are applied to a closed-field unmanned freight vehicle. Has the following advantages:
(1) The positioning mode has more advantages, compared with the positioning of the magnetic nails, the GPS positioning range is wider, and the magnetic nails are not spaced from each other;
(2) The path planning is a track generated on line in real time, the driving route can be changed at any time, and the control is more flexible;
(3) Not only can the anti-collision protection be realized, but also the obstacle detouring function of the obstacle can be realized, and the obstacle detouring strategy is complete;
(4) For the port environment, the automatic guiding system can be suitable for the environment of a new wharf and can also be suitable for the environment of a traditional old wharf;
(5) Compared with a combined navigation system of a magnetic nail positioning and inertial measurement unit, the automatic guiding system has advantages in the overall cost and the complexity of wharf reconstruction or reconstruction, and the reconstruction time period is shorter;
(6) The magnetic nails are time-consuming and labor-consuming to maintain, the maintenance period is relatively long, the process is relatively complicated, and the scheme is convenient in later maintenance, and is time-saving and labor-saving.
Drawings
FIG. 1 is a block diagram of the overall system according to the present invention;
FIG. 2 is an unmanned freight vehicle incorporating an automatic guidance system in accordance with the present invention;
FIG. 3 is a schematic view of a specific installation configuration of an automatic guidance system on an unmanned freight vehicle in accordance with the present invention;
FIG. 4 is a flow chart of an automatic boot method according to the present invention;
FIG. 5 is a schematic illustration of an unmanned vehicle of the present invention operating at a dock site;
FIG. 6 is an algorithm flow chart of a decision making system according to the present invention;
Fig. 7 is a schematic diagram of the coordinate system relationship between the XY ground coordinate system (cartesian coordinate system) and the road coordinate system (SL coordinate system) according to the present invention;
Detailed Description
The first embodiment is as follows:
The automatic guiding system applied to the unmanned freight vehicle in the closed field of the embodiment is shown in fig. 1,2 and 3, wherein the automatic guiding system 2 comprises a positioning system 4, a sensing system 5, a decision system 6 and a control system 7,
The positioning system 4 adopts GPS (reference numeral 2d in the figure) and inertial navigation as a main positioning system 4, and adopts a combination of a vehicle-mounted sensor group and a road side sensor group as an auxiliary positioning system, so as to upload pose information of an accurate and stable vehicle center point obtained by the main positioning system 4 and the auxiliary positioning system in real time to the decision system 6;
The sensing system 5 is used for monitoring the lane by adopting a multi-sensor combination of the laser radar 2b, the millimeter wave radar 2a and the visual camera 2c, detecting, identifying and classifying the obstacle and judging the running speed, and uploading the judged information to the decision system 6;
the decision system 6 is used for making a decision on the vehicle by combining the data information uploaded by the positioning system 4 and the sensing system 5 and carrying out local path planning, wherein the algorithm of the local path planning is a method for generating a track by adopting a spiral curve.
The second embodiment is as follows:
as shown in fig. 1, 2 and 3, in the positioning system 4, the main positioning system 4 includes a GPS (reference numeral 2d in the figure) respectively disposed at the top of a box 2e at the head and tail of the freight vehicle, and an antenna of the GPS (reference numeral 2d in the figure) extends out of the surface of the box 2e, and a gyroscope inertial navigation is selectively installed in the middle of the box 1 as the main positioning system 4; the vehicle body 1 carries a load 3;
And the vehicle-mounted sensor group of the auxiliary positioning system comprises 6 laser radars 2b, 8 cameras 2c and 6 millimeter wave radars 2a, and the specific setting mode is as follows: the method comprises the steps that a combination of 3 laser radars 2b, 4 cameras 2c and 3 millimeter wave radars 2a which are respectively arranged on a vehicle head and a vehicle tail is used as an auxiliary positioning system, a box body 2e is respectively arranged on the vehicle head or the vehicle tail, 4 cameras 2c are arranged on the top surface of the box body 2e, the 4 cameras 2c are uniformly arranged into a straight line, 3 laser radars 2b are arranged in the middle of the outer side surface of the box body 2e, the 3 laser radars 2b are uniformly arranged into a horizontal straight line, 3 millimeter wave radars 2a are arranged on the lower portion of the outer side surface of the box body 2e, and the 3 millimeter wave radars 2a are uniformly arranged into a horizontal straight line; the drive test sensor group of the auxiliary positioning system is a laser radar installed in the field;
Providing stable and accurate pose information and speed information through an active positioning system and an auxiliary positioning system; a schematic diagram of an actual sensor mounting arrangement is shown in fig. 1,2 and 3 below. The positioning result of GPS (the reference numeral is 2d in the figure) is utilized and combined with the combined positioning result of the sensor group to output a comparatively ideal positioning result under the condition of open wharf environment; when the GPS (reference numeral 2d in the figure) signal has a shielding position, such as a special area below a shore bridge, below a track crane, near a storage yard and the like, the auxiliary positioning of the vehicle is carried out through a road side sensor group, so that the unmanned vehicle is ensured to have stable and accurate pose information in the whole wharf scene, and the determined positioning information is uploaded to the decision system 6.
And a third specific embodiment:
The sensing system 5 measures the distance of the obstacle by using the laser radar 2b groups at the head and the tail of the vehicle and estimates the running speed of the obstacle; detecting, identifying and classifying the obstacle by using the vehicle-mounted camera 2c group and detecting a lane, and accurately measuring the running speed information of the obstacle by using the millimeter wave radar 2a group; the detection and classification of the obstacle are completed through a multi-sensor fusion technology, then the detected obstacle distance, the speed information and the size information of the obstacle are output, and the obtained obstacle information is uploaded to the decision system 6.
The specific embodiment IV is as follows:
The automatic guiding system applied to the unmanned freight vehicle in the closed field in the embodiment adopts the industrial controller with the GPU to make a decision, and the decision system 6 combines the current pose information of the vehicle obtained by the positioning system 4, the obstacle information obtained by the sensing system 5 and the globally planned path information to make a decision so as to complete the local path planning.
Fifth embodiment:
The automatic guiding system applied to the unmanned freight vehicle in the closed field in the embodiment is controlled by the control system 7 by adopting an industrial controller with a GPU,
Firstly, receiving the path points issued by the decision system 6 as reference points, and converting the information of the reference points into actual control commands, namely the rotation angle and the speed of the wheels;
Then, the control command is issued to the controller of the actual executing mechanism 8, and in the process, the control system 7 continuously corrects the path deviation, the speed deviation and the rotation angle of the vehicle, so that the control of the vehicle is completed, the track is tracked, and the vehicle can accurately run according to the target track given by the decision system 6 and normally reach the target point.
In summary, the automatic guiding system 2 of the unmanned vehicle provided by the invention can be fully applied to a closed dock environment, and a schematic diagram of the unmanned vehicle running on the dock is given as shown in fig. 5 below.
Specific embodiment six:
an automatic guiding method applied to an unmanned freight vehicle in a closed field according to the present embodiment, as shown in fig. 4, includes the steps of:
Firstly, the positioning system 4 is used for uploading the pose information of the accurate and stable vehicle center point obtained in real time by the main positioning system 4 and the auxiliary positioning system to the decision system 6; the positioning system 4 comprises a main positioning system 4 and auxiliary positioning systems, wherein a GPS (reference numeral 2d in the figure) and inertial navigation are adopted as the main positioning system 4 of the positioning system 4, and a combination of a vehicle-mounted sensor group and a road side sensor group is adopted as the auxiliary positioning systems;
Step two, monitoring the lane through the perception system 5, detecting, identifying and classifying the obstacle, judging the running speed, and uploading the judged information to the decision system 6; wherein, the perception system 5 adopts a multi-sensor combination form of a laser radar 2b, a millimeter wave radar 2a and a visual camera 2 c;
Step three, the decision system 6 receives the data information uploaded by the positioning system 4 and the sensing system 5 to make a decision on the vehicle, and local path planning is performed;
And step four, calculating a control command through the control system 7 according to the local path reference point information planned by the decision system 6, and transmitting the control command to the executing mechanism, wherein the control command comprises a target angle and a target speed of the wheels.
Seventh embodiment:
In the third step, the decision system 6 receives the data information uploaded by the positioning system 4 and the sensing system 5 to make a decision on the vehicle, and the process of making a local path planning is that the decision system 6 makes a decision by adopting an industrial controller with a Graphic Processing Unit (GPU), and the decision system 6 makes a decision by combining the current pose information of the vehicle obtained by the positioning system 4, the obstacle information obtained by the sensing system 5 and the path information of global planning, so as to complete the local path planning.
Eighth embodiment:
The automatic guiding method applied to the unmanned freight vehicle in the closed field in the embodiment is characterized in that the algorithm of the local path planning adopts a method for generating a track by a spiral curve, namely:
Step 1, defining an XY ground coordinate system (Cartesian coordinate system) and a road coordinate system (SL coordinate system), determining an initial state value (current XY coordinate, course angle and curvature of a vehicle) by converting an initial value obtained by a positioning system 4 into the road coordinate system through the XY ground coordinate system, and determining a termination state value (XY coordinate of a target point, course angle and curvature) by converting a termination value obtained by a dispatching system into the road coordinate system through the XY ground coordinate system; the dispatching system is a remote fleet management system and is arranged at the upstream of automatic driving;
Step 2, solving coefficients of the spiral curve according to the initial state value and the termination state value, and determining unknown parameters in the coefficients of the spiral curve; estimating and calculating unknown parameters in coefficients of the spiral curve by using a gradient descent method to obtain a plurality of groups of estimated values;
step 3, obtaining an expression of the curve according to the estimated value of the unknown parameter, and generating all tracks;
Step 4, evaluating each track by using an evaluation function so as to meet the physical performance of the vehicle as an evaluation requirement and obtain an optimal track; transforming the road coordinate system into a coordinate system of an XY ground coordinate system to obtain a series of track point information under the XY coordinate system, and transmitting the points to a control system 7;
wherein,
The single track point information comprises coordinate positions, course angles and speed information of track points;
The evaluation requirement of the physical performance of the vehicle refers to speed information and position information of the obstacle, so that the selection of the track is completed, and the function of bypassing the obstacle is realized;
Detailed description nine:
an automatic guiding method applied to a closed-field unmanned freight vehicle in the embodiment, the algorithm of the local path planning adopts the algorithm flow of a decision system 6 from step 1 to step 4 of the method of generating a track by a spiral curve, the algorithm flow is as follows, the algorithm flow is as shown in figure 6,
First, according to a kinematic model of a vehicle center point: the position is denoted (x, y), the angular velocity head is denoted θ, the longitudinal velocity of the vehicle is denoted v, the curvature is denoted k, and the kinematic equation of the velocity, angular velocity, and curvature change rate with respect to time is expressed as:
Starting from the planned path, the motion of the vehicle is expressed as a function of curvature with respect to the travel distance S, so the kinematic equation described above is transformed into the following form:
dx/ds＝cos[θ(s)]
dy/ds＝sin[θ(s)]
dθ/ds＝k(s)
Then, a specific algorithm flow is designed:
Acquiring information uploaded by a positioning system 4 and a sensing system 5, namely the current pose of a vehicle and surrounding obstacle information; then describing a lane coordinate system by S and L, wherein the coordinates of a definition point under the SL coordinate system are denoted as p (S, L), wherein S represents arc length, and L represents lateral deviation from a lane center line, which is essentially the relation between the two coordinate systems, and a schematic diagram of the two coordinate systems is shown in FIG. 7;
Then searching a path which enables the vehicle to stably run and meets constraint between a starting point and a terminal point so as to realize path planning;
Then, a path is represented by a polynomial spiral curve, and a track is generated by the spiral curve; the polynomial spiral is a plane curve, the curvature of the polynomial spiral is a polynomial function k(s) related to the arc length, and a third-order polynomial is used at low speed, and a fifth-order polynomial is used at high speed to ensure the continuity of curvature change rate and derivative thereof;
the third order polynomial spiral is expressed as:
k(s)＝k0+k1s+k2s2+k3s3
From the vehicle start configuration: q init＝[xIyIθIkI to the end configuration q goal＝[xGyGθGkG), at the start position s=0, k 0＝kI is also four unknowns k 2,k3,k4,sG, at high speed, the higher derivative of k can be guaranteed to be continuous, the initial state of the vehicle is expanded, and at s=0, the first and second derivatives of k to s, namely (Simplified to/>)Can be obtained/>The initial value is defined as: /(I)The method can obtain the following steps:
k0＝kI
Calculating to the moment, wherein only two parameters of k 3,sG are unknown; by adding two inputs, the cubic polynomial can be expanded into a penta-order polynomial;
To improve the numerical accuracy, the new parameter definition p= [ p 0...3,sG ] is used, and the following polynomials are directly used for the coefficient solving: k(s) =a (p) +b (p) s+c (p) s 2+d(p)s3, which are constrained to be equal to the path curvature at equidistant points on the path:
k(0)＝p0
k(sG)＝p3
An expression for the polynomial coefficients can be found:
a(p)＝p0
P 0＝kI,p3＝kG is known, so that only three unknowns remain, i.e Three unknowns:
State given: start position q init＝[xI,yI,θI,kI ], desired end point q goal＝[xG,yG,θG,kG ]. With the start point position as the origin, a new start point q init＝[0,0,0,kI and end point q goal can be obtained. Giving a candidate point P, calculating the end position state according to the point P, and calculating by using a gradient descent method:
θp(s)＝a(p)s+b(p)s2/2+c(p)s3/3+d(p)s4/4
kp(s)＝a(p)+b(p)s+c(p)s2+d(p)s3
Gradient descent method, using x p(sG) as a reference path end point, implying s G＝sG (p), the state of the path end point is configured to:
taking the vector as a function of the parameter P, calculating a jacobian matrix of the endpoint state vector about unknown parameters, wherein the unknown parameters are
Since k p(sG)＝p3 is not a function of an unknown variable, no derivative on k p is required; the goal is to find P so that
First, toAssuming a reasonable initial value, the jacobian matrix is used to pass/>Iterative optimization/>, of gradient descent methodObtain a better estimated value/>
Then, the iterative process is repeated to letReplace/>Until deltaq is small enough to meet the expected value; wherein, ζ represents the substitution of the preceding term for the following term by the symbol;
There is a very critical problem in that Based on the above calculation, a set of initial guesses corresponds to a set of target point coordinates. This in effect converts the problem of generating multiple paths into an estimation of the initial values of the parameters.
In the initial value estimation process, the problem of solving an equation set is solved, which is equivalent to the state values of the known initial point and the target point, and the unknown polynomial coefficientsPerforming inverse solution; in the actual process, the current point and the target point are known, the information of the current point is obtained by the positioning system 4, and the information of the target point is from a command issued by the scheduling system; therefore, the left side in the following equation set is actually a known value, and the right side is an expression containing an unknown parameter, or the unknown parameter/>, is calculated by these equationsFor the value of s G, in the case of determining the target point, the curve length is calculated through a certain conversion relation, that is, the projection point coordinate of the center line of the lane line of the target point under the SL coordinate system is represented, so s G is equivalent to a known value; now leave/>Is unknown;
θp(s)＝a(p)s+b(p)s2/2+c(p)s3/3+d(p)s4/4
kp(s)＝a(p)+b(p)s+c(p)s2+d(p)s3
then, under the condition that the information of the target point is known, the unknown parameters can be calculated, namely An initial estimate; then a track can be obtained according to each group of estimated values, and finally generation of all tracks is completed;
Then, all the tracks are evaluated through an evaluation function, the evaluation function can be combined with obstacle information, lane limit information, mechanical performance of the vehicle body, comfort level and the like to evaluate, and the track with the highest evaluation is selected to be the most optimal track;
and then, the optimal track point information is issued to the control system 7.
Detailed description ten:
The control system 7 of the present embodiment is used for receiving the route points issued by the decision system 6 as reference points, and converting the information of these reference points into actual control commands, namely the rotation angle and the speed of the wheels. And the control command is issued to a controller of the actual executing mechanism, and in the process, the control system 7 can continuously correct the path deviation, the speed deviation and the rotation angle of the vehicle, so that the track tracking of the control implementation track of the vehicle is finally completed, and the vehicle can correctly run according to the target track given by the decision system 6 and normally reach the target point.
In summary, the automatic guiding system 2 of the unmanned vehicle provided by the invention can be fully applied to a closed dock environment, and a schematic diagram of the unmanned vehicle running on the dock is given as shown in fig. 5 below.
Examples:
the automatic guiding system 2 of the unmanned vehicle provided by the invention can be applied to unmanned vehicles in closed scenes of wharfs. The structural block diagram of the whole system is shown in the following figure 1, and the specific composition structure comprises a positioning system 4, a sensing system 5, a decision system 6 and a control system 7.
Each subsystem is an indispensable part, the positioning system 4 provides the current stable and accurate pose of the vehicle in real time, the perception system 5 provides obstacle information in real time, and the decision system 6 decides the next action of the vehicle in real time according to the current pose of the vehicle and the obstacle information, and plans the local path in real time. The decision system 6 delivers the planned optimal path to the control system 7 in the form of a series of points, which serve as reference points for the control system 7. After the control system 7 receives the command issued by the decision, a control command, namely the target angle and the target speed of the front and rear wheels, is calculated according to the coordinate positions and the speeds of the current point and the reference point, and then issued to an executing mechanism to complete the tracking of the track, and finally reaches the target point normally.
Claims (2)
1. An automatic guiding method applied to an unmanned freight vehicle in a closed field comprises an automatic guiding system on the unmanned freight vehicle, wherein the automatic guiding system comprises a positioning system, a sensing system, a decision making system and a control system,
The positioning system adopts GPS and inertial navigation as a main positioning system, and adopts a combination of a vehicle-mounted sensor group and a road side sensor group as an auxiliary positioning system, and is used for uploading pose information of a vehicle center point obtained by the main positioning system and the auxiliary positioning system in real time to the decision system;
the sensing system adopts a combination of a laser radar, a millimeter wave radar and a visual camera, is used for monitoring a lane, detecting, identifying and classifying obstacles and judging the running speed, and uploading the judged information to the decision system;
The decision system is used for deciding the vehicle by combining the data information uploaded by the positioning system and the sensing system and carrying out local path planning, wherein the local path planning is a method for generating a track by adopting a spiral curve;
In the positioning system, the main positioning system comprises GPS antennas respectively arranged at the top of a box body of the head and the tail of a freight vehicle, and a gyroscope inertial navigation system is arranged in the middle of the vehicle body to serve as the main positioning system; the vehicle-mounted sensor group of the auxiliary positioning system comprises a combination of 3 laser radars, 4 cameras and 3 millimeter wave radars which are respectively arranged at the head and the tail of the vehicle as the auxiliary positioning system, wherein the head or the tail of the vehicle is respectively provided with a box body, the top surface of the box body is provided with 4 cameras, the 4 cameras are uniformly arranged into a straight line, the middle part of the outer side surface of the box body is provided with 3 laser radars, the 3 laser radars are uniformly arranged into a horizontal straight line, the lower part of the outer side surface of the box body is provided with 3 millimeter wave radars, and the 3 millimeter wave radars are uniformly arranged into a horizontal straight line; the drive test sensor group of the auxiliary positioning system is a laser radar installed in the field;
Providing stable and accurate pose information and speed information through an active positioning system and an auxiliary positioning system; under the condition of open wharf environment, the positioning result of the GPS is utilized and combined with the combined positioning result of the sensor group to output the positioning result; when the GPS signal has a shielding position, the auxiliary positioning of the vehicle is carried out through the road side sensor group, so that the unmanned vehicle can have stable and accurate pose information in the whole wharf scene, and the determined positioning information is uploaded to the decision system;
the sensing system measures the distance of the obstacle by utilizing a laser radar group at the head and the tail of the vehicle, and estimates the running speed of the obstacle; detecting, identifying and classifying the obstacle and detecting the lane by using the vehicle-mounted camera group,
Accurately measuring the running speed information of the obstacle by utilizing the millimeter wave radar group; detecting and classifying the obstacle through the fusion of multiple sensors, then outputting the distance of the detected obstacle, the speed information and the size information of the obstacle,
Uploading the obtained obstacle information to a decision system;
The decision system adopts an industrial controller with a GPU to make decisions;
The control system adopts an industrial controller with a GPU to make decisions,
Firstly, receiving path points issued by a decision system as reference points, and converting information of the reference points into actual control commands, namely the rotation angle and the speed of wheels;
Then, a control command is issued to a controller of an actual executing mechanism, the control system continuously corrects the path deviation, the speed deviation and the rotation angle of the vehicle, the control of the vehicle is completed, the track is tracked, and the vehicle can accurately run according to a target track given by a decision system and normally reach the target point;
the method is characterized in that: the method comprises the following steps:
Step one, the pose information of a vehicle center point obtained in real time by a main positioning system and an auxiliary positioning system is uploaded to a decision system through the positioning system; the positioning system comprises a main positioning system and auxiliary positioning systems, wherein GPS and inertial navigation are adopted as the main positioning systems of the positioning system, and a combination of a vehicle-mounted sensor group and a road side sensor group is adopted as the auxiliary positioning systems;
Monitoring the lane through the sensing system, detecting, identifying and classifying the obstacle, judging the running speed, and uploading the judged information to the decision making system; the sensing system adopts a laser radar, millimeter wave radar and visual camera combination mode;
Step three, the decision system receives the data information uploaded by the positioning system and the sensing system to make a decision on the vehicle, and local path planning is performed;
step four, calculating a control command through a control system according to the local path reference point information planned by the decision system, and transmitting the control command to an executing mechanism, wherein the control command comprises a target angle and a target speed of wheels;
In the third step, the decision system receives the data information uploaded by the positioning system and the sensing system to make a decision on the vehicle, and the process of carrying out local path planning is that the decision system adopts an industrial controller with a GPU to make a decision, and the decision system combines the current pose information of the vehicle, the obstacle information obtained by the sensing system and the path information of global planning, which are obtained by the positioning system, to make a decision, so as to complete local path planning;
The algorithm of the local path planning adopts a method for generating a track by a spiral curve, namely:
Step 1, defining an XY ground coordinate system and a road coordinate system, determining an initial state value by converting an initial value obtained by a positioning system into the road coordinate system through the XY ground coordinate system, and determining a termination state value comprising an XY coordinate of a target point, a course angle and curvature by converting a termination value obtained by a dispatching system into the road coordinate system through the XY ground coordinate system;
Step 2, solving coefficients of the spiral curve according to the initial state value and the termination state value, and determining unknown parameters in the coefficients of the spiral curve; estimating and calculating unknown parameters in coefficients of the spiral curve by using a gradient descent method to obtain a plurality of groups of estimated values;
step 3, obtaining an expression of the curve according to the estimated value of the unknown parameter, and generating all tracks;
Step 4, evaluating each track by using an evaluation function so as to meet the physical performance of the vehicle as an evaluation requirement and obtain an optimal track; transforming the road coordinate system into a coordinate system of an XY ground coordinate system to obtain a series of track point information under the XY coordinate system, and transmitting the points to a control system;
wherein,
The single track point information comprises coordinate positions, course angles and speed information of track points;
The evaluation requirement of the physical performance of the vehicle refers to speed information and position information of the obstacle, so that the selection of the track is completed, and the function of bypassing the obstacle is realized;
The algorithm of the local path planning adopts the algorithm flow of a decision system from the 1 st step to the 4 th step of the method for generating the track by the spiral curve as follows,
First, according to a kinematic model of a vehicle center point: the position is denoted (x, y), the angular velocity head is denoted θ, the longitudinal velocity of the vehicle is denoted v, the curvature is denoted k, and the kinematic equation of the velocity, angular velocity, and curvature change rate with respect to time is expressed as:
from planning the path, the motion of the vehicle is expressed as a function of curvature with respect to the travel distance S, transforming the kinematic equation described above into the following form:
dx/ds＝cos[θ(s)]
dy/ds＝sin[θ(s)]
dθ/ds＝k(s)
Then, a specific algorithm flow is designed:
acquiring information uploaded by a positioning system and a sensing system, namely the current pose of a vehicle and surrounding obstacle information; then, describing a lane coordinate system by S and L, and defining coordinates of points under the SL coordinate system as p (S, L), wherein S represents arc length and L represents lateral deviation from a lane center line;
Then searching a path which enables the vehicle to stably run and meets constraint between a starting point and a terminal point so as to realize path planning;
Then, a path is represented by a polynomial spiral curve, and a track is generated by the spiral curve; the polynomial spiral is a plane curve, the curvature of the polynomial spiral is a polynomial function k(s) related to the arc length, and a third-order polynomial is used at low speed, and a fifth-order polynomial is used at high speed to ensure the continuity of curvature change rate and derivative thereof;
the third order polynomial spiral is expressed as:
k(s)＝k0+k1s+k2s2+k3s3
From the vehicle start configuration: q init＝[xIyIθIkI to the end configuration q goal＝[xGyGθGkG), at the start position s=0, k 0＝kI is also four unknowns k 2,k3,k4,sG, at high speed, the higher derivative of k is guaranteed to be continuous, the initial state of the vehicle is extended, and at s=0, the first and second derivatives of k to s, namely Simplified to/>ObtainingDefining initial values as: /(I)The method comprises the following steps:
k0＝kI
Calculating to the moment, wherein two parameters of k 3,sG are unknown; expanding the third order polynomial into a fifth order polynomial by adding two inputs;
The new parameter definition p= [ p 0...3,sG ] is used and the following polynomials are used for the solution of the coefficients: k(s) =a (p) +b (p) s+c (p) s 2+d(p)s3, which are constrained to be equal to the path curvature at equidistant points on the path:
k(0)＝p0
k(sG)＝p3
Solving an expression of polynomial coefficients:
a(p)＝p0
P 0＝kI,p3＝kG is known, so that only three unknowns remain, i.e
Three unknowns:
State given: start position q init＝[xI,yI,θI,kI, desired end point q goal＝[xG,yG,θG,kG; taking the starting point position as an origin, a new starting point q init＝[0,0,0,kI and an end point q goal can be obtained; giving a candidate point P, calculating the end position state according to the point P, and calculating by using a gradient descent method:
θp(s)＝a(p)s+b(p)s2/2+c(p)s3/3+d(p)s4/4
kp(s)＝a(p)+b(p)s+c(p)s2+d(p)s3
Gradient descent method, using x p(sG) as a reference path end point, implying s G＝sG (p), the state of the path end point is configured to:
Taking the vector as a function of a parameter P, calculating a jacobian matrix of the endpoint state vector about unknown parameters, wherein the unknown parameters are
Since k p(sG)＝p3 is not a function of an unknown variable, no derivative on k p is required; the goal is to find P so that
First, toAssuming a reasonable initial value, the jacobian matrix is used to pass/>Iterative optimization of gradient descent methodObtain the estimated value/>
Then, the iterative process is repeated to letReplace/>Until deltaq meets the expected value; wherein, ζ represents the substitution of the preceding term for the following term by the symbol;
in the initial value estimation process, the initial point and target point state values are known, and the unknown polynomial coefficients are calculated Performing inverse solution; the current point and the target point are known, the information of the current point is obtained by a positioning system, and the information of the target point is from a command issued by a scheduling system; therefore, the left side of the equation set is a known value, the right side is an expression containing an unknown parameter, and the unknown parameter/>, is calculated from these equationsS G denotes the curve length, i.e. the projected point coordinates of the target point at the lane line center line, so s G is known,/>Is unknown;
θp(s)＝a(p)s+b(p)s2/2+c(p)s3/3+d(p)s4/4
kp(s)＝a(p)+b(p)s+c(p)s2+d(p)s3
then, under the condition that the target point information is known, the unknown parameters are calculated, namely An initial estimate; then a track can be obtained according to each group of estimated values, and finally generation of all tracks is completed;
Then, all the tracks are evaluated through an evaluation function, the evaluation function can be combined with obstacle information, lane limit information, mechanical performance of the vehicle body, comfort level and the like to evaluate, and the track with the highest evaluation is selected to be the most optimal track;
And then, sending the optimal track point information to a control system.
2. The automated guided method for use on a closed-field unmanned freight vehicle of claim 1, wherein: the control system receives the path points issued by the decision system as reference points and converts the information of the reference points into actual control commands, namely the rotation angle and the speed of the wheels; and issuing a control command to a controller of an actual executing mechanism, wherein the control system can continuously correct the path deviation, the speed deviation and the rotation angle of the vehicle in the process, and finally, the control of the vehicle is completed to track the track, so that the vehicle can correctly run according to the target track given by the decision system and normally reach the target point.
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