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CN114077255B - A Pathfinding Method for Intelligent Vehicles Based on Ellipse Model Artificial Potential Field Method - Google Patents

A Pathfinding Method for Intelligent Vehicles Based on Ellipse Model Artificial Potential Field Method Download PDF

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CN114077255B
CN114077255B CN202111388075.8A CN202111388075A CN114077255B CN 114077255 B CN114077255 B CN 114077255B CN 202111388075 A CN202111388075 A CN 202111388075A CN 114077255 B CN114077255 B CN 114077255B
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intelligent vehicle
target point
potential field
obstacle
artificial potential
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CN114077255A (en
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刘冉冉
颜海彬
藏传涛
郑恩兴
郭威
蒋益锋
李丽
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Jiangsu University of Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

本发明公开了一种基于椭圆模型人工势场法的智能车寻路方法，包括构建相应地图、建立坐标系、确定障碍物位置坐标和目标点位置，然后判断智能车当前是否到达终点位置，若到达终点位置，则完成路径规划，生成最终路径；若未到达终点位置，则用人工势场法进行路径规划，计算智能车当前位置所受到的引力、斥力及合力大小及方向，判断是否陷入局部极小值问题。优点在于：在庞杂环境中，采用椭圆模型增设虚拟目标点，虚构目标点产生的引力将改变机器人在局部最小值的状态，通过在椭圆模型上不断修正虚构目标点的位置，从而使智能车跳出局部平衡区域，同时大大降低了算法的时间成本。

The invention discloses a method of finding a way for a smart car based on an ellipse model artificial potential field method, which includes constructing a corresponding map, establishing a coordinate system, determining the position coordinates of obstacles and the position of a target point, and then judging whether the smart car has reached the end point. When the end point is reached, the path planning is completed and the final path is generated; if the end point is not reached, the artificial potential field method is used for path planning, and the gravity, repulsion, and resultant force and the direction of the current position of the smart car are calculated to determine whether it is trapped in a local area. minima problem. The advantage is that in a complex environment, the ellipse model is used to add a virtual target point. The gravitational force generated by the virtual target point will change the state of the robot at the local minimum. By continuously correcting the position of the virtual target point on the ellipse model, the smart car will jump out Locally balanced regions, while greatly reducing the time cost of the algorithm.

Description

Intelligent vehicle road-finding method based on elliptical model artificial potential field method
Technical Field
The invention relates to the technical field of automatic road searching, in particular to an intelligent vehicle road searching method based on an elliptic model artificial potential field method.
Background
The intelligent vehicle senses the road environment through a vehicle-mounted sensor, automatically plans a driving route and controls the vehicle to reach a preset target. Integrates a plurality of technologies such as automatic control, architecture, artificial intelligence, visual computing and the like, is a highly developed product of computer science, pattern recognition and intelligent control technology, is an important mark for measuring national scientific research strength and industrial level, and has wide application prospect in the fields of national defense and national economy. In recent years, mobile robots and various unmanned aerial vehicles are widely developed and applied in various industries, and become another research hotspot, and a path planning technology is one of the most basic and important technologies in robot related research. Path planning refers to that a robot searches a working space for an obstacle avoidance path from a starting point to an end point according to specific indexes (such as least time, shortest walking path and the like). Path planning research begins in the 70 s of the 20 th century, after which the research on the path planning research by the robot research and development major nations is not interrupted, and the path planning research is achieved as soon as possible. At present, the commonly used path planning method is divided into a traditional algorithm and an intelligent bionic algorithm. Among them, the conventional artificial potential field method is widely used because of its simple and convenient calculation.
The traditional artificial potential field method assumes that the target point generates attractive force to the intelligent vehicle, and the obstacle generates repulsive force to the intelligent vehicle, so that the intelligent vehicle walks along the direction of potential decline, and the intelligent vehicle just like water flows to the lower part. The algorithm has the advantages of simple structure, contribution to the real-time performance of bottom control, capability of greatly reducing the calculated amount and the calculated time, generation of a relatively smooth path and contribution to maintaining the stability of an automatic driving vehicle. The problems of the algorithm are obvious, firstly, the problem that the target is unreachable, if the distance between the intelligent vehicle and the target point is far, the attractive force becomes extremely large, the repulsive force is smaller than the attractive force and even can be ignored, at the moment, the intelligent vehicle is likely to collide with the obstacle on the advancing path to cause the failure of the intelligent vehicle to seek paths, and when a large obstacle exists near the target point, the intelligent vehicle is subjected to the action of the extremely large repulsive force of the obstacle when approaching the target point, so that the end point cannot be reached. The problem of local minimum value and the problem of zero potential energy field are the bottleneck problems of an artificial potential field method, the reason for generating zero potential energy points is that the intelligent vehicle has a plurality of obstacle points in a working area, the position distribution is special, the attraction and the repulsion can be just equal in size at a certain point of the working area, the directions are opposite, the stress of the intelligent vehicle is zero, and the intelligent vehicle can swing back and forth near the point and can not reach a destination, so that the operation efficiency of the intelligent vehicle is seriously affected. Lee, J et al control obstacle avoidance planning of a mobile robot by using an arbitrary force algorithm to establish a new artificial potential field method function, and meanwhile, the problem of symmetrical ordering of the robot, obstacles and target points is solved. The li.chunshu et al proposes a "extended walk" method with memory function that determines whether the target point is surrounded by an obstacle by walking along the edge of the obstacle to jump out of the local minimum point of the artificial potential field method and recording and analyzing the local minimum point that the robot walks through, so as to avoid the robot from oscillating back and forth all the time or the position target point from turning around. Although the method solves the defects of the artificial potential field method to a certain extent, the intelligent vehicle cannot obtain a good obstacle avoidance effect under the condition that the environment of the intelligent vehicle is complex, and an optimal path cannot be obtained or a path curve is tortuous.
The invention solves the problem that the potential field method can not reach the target point and sink into local minimum value in the process of planning the path by improving the repulsive force function and establishing an elliptic model to set the virtual target point on the basis of the traditional artificial potential field.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an intelligent vehicle road-finding method based on an elliptic model artificial potential field method.
In order to achieve the above purpose, the present invention adopts the following technical scheme: an intelligent vehicle road-finding method based on an ellipse model artificial potential field method comprises the following steps:
step 1, constructing a corresponding map according to an area where an intelligent vehicle is located, gridding the map, establishing a coordinate system, and determining coordinates of the position of an obstacle and the position of a target point in the map;
step 2, judging whether the intelligent vehicle reaches the end position currently;
step 3, if the final position is reached, path planning is completed, and a final path is generated; if the terminal position is not reached, path planning is carried out by using an artificial potential field method;
step 4, calculating the magnitude and direction of attractive force, repulsive force and resultant force of the current position of the intelligent vehicle;
step 5, judging whether the problem of local minimum value is solved;
step 6, if the local minimum is not trapped, returning to the step 2 to continue judging and path planning, if the local minimum is trapped, constructing an elliptical model according to the problem point of the local minimum, adding a virtual target point, and increasing the attractive force of the end position direction to the intelligent vehicle so as to jump out the problem of the local minimum;
step 7, calculating again by using an elliptic model artificial potential field method until the vehicle runs to the end position;
step 8, carrying out secondary optimization on the planned path, sequentially detecting path nodes backwards from the starting point, judging whether the path nodes can reach straight lines, and deleting the path from the starting point to the nodes if the path nodes can reach straight lines; if the straight line can not be reached, the node position is used as the starting point position to continuously judge whether the straight line can be reached, so that the path is simplified.
In the above-mentioned intelligent vehicle road-finding method based on elliptic model artificial potential field method, adopting artificial potential field method to avoid the phenomenon that the intelligent vehicle is unreachable to the target in the course of movement, planning a safe path from the starting point to the target point for the intelligent vehicle in the working environment containing the obstacle, and judging whether the intelligent vehicle falls into the local minimum problem, comprising:
an X-O-Y coordinate system is established by a map, and the coordinate of the robot sinking into local balance is assumed to be P min (x min ,y min ) The end position coordinate is P goal (x goal ,y goal ) The midpoint coordinate position P of the two points b The method comprises the following steps:
with P min And P goal The connection line of the two points is taken as a new axis X', and P b As an origin, an axis Y 'which passes through the origin and is perpendicular to X' is established, and a new coordinate system is obtained by rotating and translating an original coordinate system, wherein the formula is as follows:
wherein the method comprises the steps ofIs a rotation matrix +.>Is a translation matrix;
the ellipse equation is established under the new coordinate system as follows:
focal length 2c= |p min -P goal I, a=ψc, ψ is the long axis factor, a 2 ＝b 2 +c 2 。
And taking the orthogonal point of the ellipse and the X' axis as an initial virtual target point, increasing the attraction of the end point position to the intelligent vehicle, breaking the local minimum state, enabling the intelligent vehicle to walk according to the virtual target point, and if the intelligent vehicle is detected to sink into local balance again, constructing a new ellipse model, and searching for a new virtual target point.
In the intelligent vehicle road-finding method based on the elliptic model artificial potential field method, the gravitation function of the artificial potential field method is as follows:
wherein X is g Representing the position (x g ,y g )；λ 1 Representing the gravitational constant; ρ (X, X) g ) The geometric distance between the current position and the final position of the intelligent vehicle;
the first order derivative of the distance in the gravitational potential function is the gravitational force:
similarly, the repulsive potential function is as follows:
wherein X is 0 ＝(x 0 ,y 0 ) Representing the obstacle position; lambda (lambda) 2 Representing a repulsive force field constant; ρ 0 Representing the obstacle influencing radius; ρ (X, X) 0 ) Is an intelligent vehicle and an obstacleThe spacing between them;
the repulsive force function is used for solving the first derivative of the distance, namely repulsive force:
so the resultant force F applied by the intelligent vehicle in space total (X) is:
F total (X)＝F att (X)+F rep (X)
the problem that the position of the target point cannot be reached is that the repulsive force generated by the obstacle around the target point of the intelligent vehicle is larger than the attractive force of the target point;
therefore, based on the traditional repulsive force function, a distance factor rho (X, X g ) The repulsive force value is reduced according to different situations; the improved repulsive force function is shown below:
when the distance ρ (X, X) between the intelligent vehicle and the end position g ) When the obstacle is less than 1 and the obstacle is close to the robot,and ρ (X, X) g ) 2 The values are all smaller than 1, so that the repulsive force value can be reduced, and the phenomenon that the target point cannot be reached due to overlarge repulsive force around the target point is prevented.
In the above-mentioned intelligent vehicle road-finding method based on elliptic model artificial potential field method, in step 3, the artificial potential field algorithm improves its repulsive force function, and according to the distance factor ρ (X, X) between the current position of the intelligent vehicle and the target point g ) Setting three different conditions, when the position of the intelligent vehicle appears within half of the radius value affected by the obstacle, the intelligent vehicle is very close to the obstacle, and considering the factors of safety obstacle avoidance, the repulsive force cannot be reduced too much, the square coefficient of the distance factor is taken here, and the other is takenIn this respect, when the robot is located between the half position of the obstacle influence radius value and the maximum value, the intelligent vehicle is just started to approach the obstacle, and in order to prevent the attraction force from being smaller than the repulsive force and thus the target point from being inaccessible, when the distance factor is larger than the obstacle influence radius, the intelligent vehicle is not affected.
In the above-mentioned intelligent vehicle road-finding method based on the artificial potential field method of the elliptic model, in step 6, an elliptic model is constructed by using the position of the local minimum point and the position of the target point, an X 'axis is established by using the direction pointing to the target point as the positive direction, a Y' axis perpendicular to the X 'axis is made at the midpoint position of the two points, thereby establishing a new coordinate system X' -O-Y ', setting the virtual target point as the intersection point of the positive direction of the X' axis and the elliptic model, thereby increasing the force of the target point on the intelligent vehicle in the gravitational direction, thereby breaking the local minimum state, and jumping out the local balance.
Compared with the prior art, the invention has the advantages that:
1. in a complex environment, an elliptical model is adopted to add a virtual target point, the attractive force generated by the virtual target point changes the state of the robot at a local minimum value, and the position of the virtual target point is continuously corrected on the elliptical model, so that the intelligent vehicle jumps out of a local balance area, and meanwhile, the time cost of an algorithm is greatly reduced;
2. according to the distance between the current position and the end position of the intelligent vehicle and according to three different conditions of setting distance influence factors, the safety of the intelligent vehicle planning path is better ensured.
Drawings
FIG. 1 is a basic schematic diagram of an artificial potential field method in an intelligent vehicle road-finding method based on an elliptical model artificial potential field method;
FIG. 2 is a diagram of a method for establishing virtual target points by an ellipse model in an intelligent vehicle road-finding method based on an ellipse model artificial potential field method;
FIG. 3 is a specific flowchart of an elliptical model artificial potential field method in an intelligent vehicle road-finding method based on the elliptical model artificial potential field method;
fig. 4 is an effect diagram of an ellipse model artificial potential field method planning path in an intelligent vehicle road-finding method based on an ellipse model artificial potential field method.
Detailed Description
The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
Examples
Referring to fig. 1-4, an intelligent vehicle road-finding method based on an elliptical model artificial potential field method comprises the following steps:
step 1, constructing a corresponding map according to an area where an intelligent vehicle is located, gridding the map, establishing a coordinate system, and determining coordinates of the position of an obstacle and the position of a target point in the map;
step 2, judging whether the intelligent vehicle reaches the end position currently;
step 3, if the final position is reached, path planning is completed, and a final path is generated; if the terminal position is not reached, path planning is carried out by using an artificial potential field method;
step 4, calculating the magnitude and direction of attractive force, repulsive force and resultant force of the current position of the intelligent vehicle;
step 5, judging whether the problem of local minimum value is solved;
step 6, if the local minimum is not trapped, returning to the step 2 to continue judging and path planning, if the local minimum is trapped, constructing an elliptical model according to the problem point of the local minimum, adding a virtual target point, and increasing the attractive force of the end position direction to the intelligent vehicle so as to jump out the problem of the local minimum;
step 7, calculating again by using an elliptic model artificial potential field method until the vehicle runs to the end position;
step 8, carrying out secondary optimization on the planned path, sequentially detecting path nodes backwards from the starting point, judging whether the path nodes can reach straight lines, and deleting the path from the starting point to the nodes if the path nodes can reach straight lines; if the straight line can not be reached, the node position is used as the starting point position to continuously judge whether the straight line can be reached, so that the path is simplified.
The artificial potential field method is adopted to avoid the phenomenon that an intelligent vehicle cannot reach a target in the moving process, a safe path from a starting point to a target point is planned for the intelligent vehicle in a working environment containing barriers, and whether the intelligent vehicle falls into a local minimum point is judged, and the method comprises the following steps:
an X-O-Y coordinate system is established by a map, and the coordinate of the robot sinking into local balance is assumed to be P min (x min ,y min ) The end position coordinate is P goal (x goal ,y goal ) The midpoint coordinate position P of the two points b The method comprises the following steps:
with P min And P goal The connection line of the two points is taken as a new axis X', and P b As an origin, an axis Y 'which passes through the origin and is perpendicular to X' is established, and a new coordinate system is obtained by rotating and translating an original coordinate system, wherein the formula is as follows:
wherein the method comprises the steps ofIs a rotation matrix +.>Is a translation matrix;
the ellipse equation is established under the new coordinate system as follows:
focal length 2c= |p min -P goal I, a=ψc, ψ is the long axis factor, a 2 ＝b 2 +c 2 ；
And taking the orthogonal point of the ellipse and the X' axis as an initial virtual target point, increasing the attraction of the end point position to the intelligent vehicle, breaking the local minimum state, enabling the intelligent vehicle to walk according to the virtual target point, and if the intelligent vehicle is detected to sink into local balance again, constructing a new ellipse model, and searching for a new virtual target point.
The gravitation function of the artificial potential field method is as follows:
wherein X is g Representing the position (x g ,y g )；λ 1 Representing the gravitational constant; ρ (X, X) g ) The geometric distance between the current position and the final position of the intelligent vehicle;
the first order derivative of the distance in the gravitational potential function is the gravitational force:
similarly, the repulsive potential function is as follows:
wherein X is 0 ＝(x 0 ,y 0 ) Representing the obstacle position; lambda (lambda) 2 Representing a repulsive force field constant; ρ 0 Representing the obstacle influencing radius; ρ (X, X) 0 ) Is the distance between the intelligent vehicle and the obstacle;
the repulsive force function is used for solving the first derivative of the distance, namely repulsive force:
so the resultant force F applied by the intelligent vehicle in space total (X) is:
F total (X)＝F att (X)+F rep (X)
the problem that the position of the target point cannot be reached is that the repulsive force generated by the obstacle around the target point of the intelligent vehicle is larger than the attractive force of the target point;
therefore, based on the traditional repulsive force function, a distance factor rho (X, X g ) The repulsive force value is reduced according to different situations; the improved repulsive force function is shown below:
when the distance ρ (X, X) between the intelligent vehicle and the end position g ) When the obstacle is less than 1 and the obstacle is close to the robot,and ρ (X, X) g ) 2 The values are all smaller than 1, so that the repulsive force value can be reduced, and the phenomenon that the target point cannot be reached due to overlarge repulsive force around the target point is prevented.
On the one hand, when the position of the robot appears within half of the radius value affected by the obstacle, the intelligent vehicle is very close to the obstacle, and the factors of safety obstacle avoidance are considered, so that the repulsive force cannot be reduced too much, and the square-open coefficient of the distance factor is taken. On the other hand, when the robot is located between the half position of the obstacle influence radius value and the maximum value, the intelligent vehicle just starts to approach the obstacle, and in order to prevent the attractive force from being smaller than the repulsive force and not approaching the target point, the repulsive force should be reduced as much as possible, so that the square coefficient of the distance factor is taken here.
In a more complex environment, the situation that the attractive force and the repulsive force applied by the intelligent vehicle are the same in magnitude and opposite in direction when the intelligent vehicle does not reach the target point position and the resultant force is zero may occur, which causes the intelligent vehicle to oscillate back and forth at the current position.
The invention provides a method for setting a virtual target point by establishing an elliptical model, wherein the attractive force generated by the virtual target point changes the state of a robot at a local minimum value, and the position of the virtual target point is continuously corrected on the elliptical model, so that an intelligent vehicle jumps out of a local balance area.
In step 3, the artificial potential field algorithm improves the repulsive force function thereof, and according to the distance factor ρ (X, X) between the current position of the intelligent vehicle and the target point g ) Setting up three kinds of different circumstances, when the position of intelligent car appears in the obstacle influences the half of radius value, the intelligent car has very near the obstacle, consider the factor of safe obstacle avoidance, can not cut down the repulsion too much, get the square coefficient of opening of distance factor here, on the other hand, the position of robot is in the obstacle when influencing the half position of radius value to the maximum value, the intelligent car just begins to be close to the obstacle, thereby in order to prevent that gravitation is less than repulsion and can't be close to the target point, when distance factor is greater than the obstacle and influences the radius, the intelligent car is not influenced.
In step 6, an elliptical model is constructed by taking the position of the local minimum point and the position of the target point, an X 'axis is established by taking the direction pointing to the target point as the positive direction, a Y' axis perpendicular to the X 'axis is made at the midpoint position of the two points, so that a new coordinate system X' -O-Y 'is established, the virtual target point is set as the intersection point of the positive direction of the X' axis and the elliptical model, the force of the target point on the intelligent vehicle in the attractive force direction is increased, the local minimum state is broken, and the local balance is jumped out.

Claims (5)

1.一种基于椭圆模型人工势场法的智能车寻路方法，其特征在于，包括以下步骤：1. A method for intelligent vehicle pathfinding based on an elliptic model artificial potential field method, characterized by comprising the following steps: 步骤1、根据智能车所在的区域构建相应的地图，将地图网格化并建立坐标系，确定地图中存在障碍物位置的坐标和目标点位置；Step 1: Construct a corresponding map based on the area where the intelligent vehicle is located, grid the map and establish a coordinate system to determine the coordinates of obstacles and target points on the map; 步骤2、判断智能车当前是否到达终点位置；Step 2: Determine whether the intelligent vehicle has reached the destination. 步骤3、若到达终点位置，则完成路径规划，生成最终路径；若未到达终点位置，则用人工势场法进行路径规划；Step 3: If the destination is reached, the path planning is completed and the final path is generated; if the destination is not reached, the path planning is performed using the artificial potential field method. 步骤4、计算智能车当前位置所受到的引力、斥力及合力大小及方向；Step 4: Calculate the magnitude and direction of the gravitational force, repulsive force, and resultant force acting on the intelligent vehicle at its current position; 步骤5、判断是否陷入局部极小值问题；Step 5: Determine if the problem is trapped in a local minimum. 步骤6、若未陷入局部最小值，则返回步骤2继续进行判断和路径规划，若陷入局部最小值问题，则根据局部最小值问题点构建椭圆模型，增设虚拟目标点，增大终点位置方向对智能车的吸引力，从而跳出局部最小值问题；Step 6: If the problem is not trapped in a local minimum, return to step 2 to continue the judgment and path planning. If the problem is trapped in a local minimum, construct an ellipse model based on the local minimum problem point, add virtual target points, and increase the attraction of the destination position direction to the intelligent vehicle, thereby escaping the local minimum problem. 步骤7、再次使用椭圆模型人工势场法进行计算，直到行驶到终点位置；Step 7: Use the elliptical model artificial potential field method to calculate again until the destination is reached; 步骤8、对规划后的路径在进行二次优化，从起点开始依次向后检测路径节点，判断是否能够直线到达，若能直线到达，则删除起点到该节点的路径；若不能直线到达，则将该节点位置当做起点位置继续做能否直线到达的判断，从而简化路径。Step 8: Perform a second optimization on the planned path. Starting from the starting point, check the path nodes sequentially to determine if they can be reached in a straight line. If they can be reached in a straight line, delete the path from the starting point to that node. If they cannot be reached in a straight line, treat that node as the starting point and continue to check if they can be reached in a straight line, thereby simplifying the path. 2.根据权利要求1所述的一种基于椭圆模型人工势场法的智能车寻路方法，其特征在于，采用人工势场法，避免智能车在运动过程中出现目标不可达现象，在含有障碍物的工作环境中为所述智能车规划出一条从起始点到目标点的安全路径，并判断智能车是否陷入局部极小点，包括：2. The intelligent vehicle pathfinding method based on the elliptic model artificial potential field method according to claim 1, characterized in that, the artificial potential field method is used to avoid the phenomenon of the intelligent vehicle being unable to reach the target during its movement, and a safe path from the starting point to the target point is planned for the intelligent vehicle in a working environment containing obstacles, and it is determined whether the intelligent vehicle is trapped in a local minimum point, including: 以地图建立X-O-Y坐标系，假设机器人陷入局部平衡的坐标为Pmin(xmin,ymin)，终点位置坐标为Pgoal(xgoal,ygoal)，则这两点的中点坐标位置Pb为：Establish an XOY coordinate system using the map. Assume the robot's coordinates when it is stuck in local equilibrium are Pmin ( xmin , ymin ) and the final position coordinates are Pgoal ( xgoal , ygoal ). Then the midpoint Pb between these two points is: 以Pmin和Pgoal两点的连线作为新轴X’，以Pb为原点，建立过原点垂直于X’的轴Y’，新的坐标系是由原坐标系经过旋转平移得到，如下式：Using the line connecting points Pmin and Pgoal as the new axis X', and with Pb as the origin, establish an axis Y' perpendicular to X' passing through the origin. The new coordinate system is obtained by rotating and translating the original coordinate system, as shown in the following equation: 其中是旋转矩阵，是平移矩阵；in It is a rotation matrix. It is a translation matrix; 在新坐标系下建立椭圆方程如下：The equation of the ellipse is established in the new coordinate system as follows: 焦距2c＝|Pmin-Pgoal|,a＝ψc，ψ是长轴因子，a2＝b2+c2；Focal length 2c = |P min -P goal |, a = ψc, ψ is the major axis factor, a² = b² + c² ; 以椭圆与X’轴的正交点为初始的虚拟目标点，增大终点位置对智能车的吸引力，从而打破局部最小状态，智能车按此虚构目标点行走，若检测到再次陷入局部平衡时，则构建新的椭圆模型，寻找新的虚构目标点。Using the intersection of the ellipse and the X' axis as the initial virtual target point, the attraction of the endpoint position to the intelligent vehicle is increased, thereby breaking the local minimum state. The intelligent vehicle travels according to this virtual target point. If it is detected that it has fallen into local equilibrium again, a new ellipse model is constructed to find a new virtual target point. 3.根据权利要求2所述的一种基于椭圆模型人工势场法的智能车寻路方法，其特征在于，人工势场法的引力函数如下：3. The intelligent vehicle pathfinding method based on the elliptic model artificial potential field method according to claim 2, characterized in that the gravitational function of the artificial potential field method is as follows: 式中Xg表示目标点的位置(xg,yg)；λ1表示引力常数；ρ(X,Xg)是智能车当前位置和终点位置的几何距离；In the formula, Xg represents the position of the target point ( xg , yg ); λ1 represents the gravitational constant; ρ(X, Xg ) is the geometric distance between the current position and the destination position of the intelligent vehicle; 对引力势力函数中的距离求一阶导所得到的即为引力：Gravity is the force obtained by taking the first derivative of the distance in the gravitational force function. 同理，斥力势函数如下：Similarly, the repulsive potential function is as follows: 式中X0＝(x0,y0)表示障碍物位置；λ2表示斥力场常量；ρ0表示障碍物影响半径；ρ(X,X0)是智能车与障碍物之间的间距；In the formula, X0 = ( x0 , y0 ) represents the position of the obstacle; λ2 represents the repulsive field constant; ρ0 represents the radius of influence of the obstacle; and ρ(X, X0 ) is the distance between the intelligent vehicle and the obstacle. 对斥力函数对距离求一阶导数即斥力：The repulsive force is obtained by taking the first derivative of the repulsive force function with respect to the distance. 所以智能车在空间中所受到的合力Ftotal(X)为：Therefore, the net force F_total (X) acting on the intelligent vehicle in space is: Ftotal(X)＝Fatt(X)+Frep(X)F total (X)＝F att (X)+F rep (X) 目标点位置无法抵达的问题是由于智能车在目标点周围的障碍物产生的斥力大于目标点的引力的原因；The problem of being unable to reach the target point is that the repulsive force generated by the obstacles around the target point by the intelligent vehicle is greater than the attractive force of the target point. 因此，在传统的斥力函数基础上，引入当前点到目标点之间的距离因子ρ(X,Xg)，根据不同的情形降低斥力值的大小；改进的斥力函数如下所示：Therefore, based on the traditional repulsion function, a distance factor ρ(X, Xg ) between the current point and the target point is introduced to reduce the magnitude of the repulsion value according to different situations; the improved repulsion function is shown below: 当智能车到终点位置之间的间距ρ(X,Xg)＜1时，且障碍物离机器人较近时，和ρ(X,Xg)2均是小于1的数值，因此可以降低斥力值，防止在目标点周围因斥力过大引起目标点无法抵达现象。When the distance ρ(X, Xg ) between the intelligent vehicle and the destination is less than 1, and the obstacle is relatively close to the robot, Both ρ(X,X g ) 2 are values less than 1, which can reduce the repulsive force value and prevent the target point from being unreachable due to excessive repulsive force around the target point. 4.根据权利要求1所述的一种基于椭圆模型人工势场法的智能车寻路方法，其特征在于，在步骤3中，人工势场法算法改进其斥力函数，根据智能车当前位置到目标点之间的距离因子ρ(X,Xg)，设置三种不同的情况，智能车的位置出现在障碍物影响半径值的一半以内时，智能车已经十分靠近障碍物，考虑到安全避障的因素，不能够将斥力削减的过多，此处取距离因子的开平方系数，另一方面，机器人的位置在障碍物影响半径值的一半位置到最大值之间时，智能车刚开始接近障碍物，为了防止引力小于斥力从而无法接近目标点，当距离因子大于障碍物影响半径时，智能车不受影响。4. The intelligent vehicle pathfinding method based on the elliptic model artificial potential field method according to claim 1, characterized in that, in step 3, the artificial potential field method algorithm improves its repulsion function. According to the distance factor ρ(X, Xg ) between the current position of the intelligent vehicle and the target point, three different cases are set. When the position of the intelligent vehicle is within half of the obstacle's influence radius, the intelligent vehicle is already very close to the obstacle. Considering the safety obstacle avoidance factor, the repulsion force cannot be reduced too much. Here, the square root coefficient of the distance factor is taken. On the other hand, when the robot's position is between half of the obstacle's influence radius and the maximum value, the intelligent vehicle has just begun to approach the obstacle. In order to prevent the attraction force from being less than the repulsion force and thus unable to approach the target point, when the distance factor is greater than the obstacle's influence radius, the intelligent vehicle is not affected. 5.根据权利要求1所述的一种基于椭圆模型人工势场法的智能车寻路方法，其特征在于，在步骤6中，以局部极小值点位置和目标点位置构建椭圆模型，以指向目标点的方向为正方向建立X’轴，在其两点的中点位置作垂直于X’轴的Y’轴，从而建立新的坐标系X’-O-Y’,设置虚拟目标点为X’轴正方向与椭圆模型的交点，从而增加目标点对智能车在引力方向上的力，从而打破局部极小值状态，跳出局部平衡。5. The intelligent vehicle pathfinding method based on the elliptical model artificial potential field method according to claim 1, characterized in that, in step 6, an elliptical model is constructed with the local minimum point position and the target point position, an X' axis is established with the direction pointing to the target point as the positive direction, and a Y' axis perpendicular to the X' axis is drawn at the midpoint of the two points, thereby establishing a new coordinate system X'-O-Y', and the virtual target point is set as the intersection of the positive direction of the X' axis and the elliptical model, thereby increasing the force of the target point on the intelligent vehicle in the direction of gravity, thereby breaking the local minimum state and jumping out of the local equilibrium.
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