CN116202550B - Automobile path planning method integrating improved potential field and dynamic window - Google Patents
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Abstract
The invention provides an automobile path planning method for fusing an improved potential field and a dynamic window, which is characterized in that a relative speed repulsive force function and a road longitudinal force function are added on the basis of a traditional potential field function, so that the problems of local optimum and unreachable targets in the traditional algorithm can be effectively solved; the longitudinal force of the road added in the traditional potential field function can ensure that the automobile runs towards the center of the road, and the safety and stability of the running process of the automobile are improved; in addition, the invention samples the relative speed, the pose, the track and the obstacle distance of the automobile and the obstacle in each critical path node through the improved dynamic window, can dynamically update the preliminary planning path in real time, and can better meet the requirements in the actual driving environment.
Description
Technical Field
The invention relates to the technical field of intelligent automobiles, in particular to an automobile path planning method integrating an improved potential field and a dynamic window.
Background
The intelligent automobile path planning, namely, the intelligent automobile is used for planning a running path, the planned path must meet the basic conditions of running time, collision avoidance effect, smooth and stable route and the like used from the beginning to the end of the automobile, so that the optimal planning path is obtained, and the intelligent automobile can be well helped to complete the running task. Because the intelligent automobile has complex and diverse environments, not only structured urban roads but also unstructured rural roads, the planned path can be well adapted to complex real environments, has good collision avoidance capability, and improves efficiency and safety and reliability as much as possible.
The path planning method can be divided into global planning and local planning according to different use conditions of the planning method. The global path planning method with wider application comprises Dijkstra algorithm, A-algorithm, ant colony algorithm, RRT algorithm and the like, and the local path planning method with wider application comprises artificial potential field method, genetic algorithm, dynamic window method and the like. The artificial potential field method is convenient and efficient, has high calculation speed, is easy to understand and can be developed rapidly.
But local optimization and target unrealizable are the biggest problems of the current artificial potential field method. In addition, some unpredictable complex change conditions exist in the actual path planning, but the existing artificial potential field method cannot dynamically update the planned path in real time, so that the requirements in the actual driving environment are difficult to meet.
Disclosure of Invention
The invention aims to provide an automobile path planning method integrating an improved potential field and a dynamic window, which is used for solving the problems of local optimum and unreachable targets in the prior art, and realizing real-time dynamic updating of a planned path so as to better meet the requirements in an actual driving environment.
A car path planning method integrating an improved potential field and a dynamic window comprises the following steps:
and 6, inputting the critical path nodes from the automobile to each obstacle into an improved dynamic window, sampling the relative speed of the automobile and the obstacle on the critical path nodes, the pose of the automobile, the distance between the automobile track and the obstacle and the minimum safety distance of the automobile running through an evaluation function of the improved dynamic window, and carrying out real-time optimization and updating on the preliminary planning path through the improved dynamic window to obtain the optimized planning path.
The automobile path planning method for fusing the improved potential field and the dynamic window has the following beneficial effects:
(1) On the basis of the traditional potential field function, a relative speed repulsive force function and a road longitudinal force function are added, so that the problems of local optimum and unreachable targets in the traditional algorithm can be effectively solved;
(2) The longitudinal force of the road added in the traditional potential field function can ensure that the automobile runs towards the center of the road, and the safety and stability of the running process of the automobile are improved;
(3) The relative speed, the pose, the track and the obstacle distance of the automobile and the obstacle in each critical path node are sampled through the improved dynamic window, so that the preliminary planning path can be dynamically updated in real time, and the requirements in the actual driving environment can be better met.
In addition, the automobile path planning method for fusing the improved potential field and the dynamic window has the following technical characteristics:
further, in step 2, the expressions of the target attraction function, the obstacle repulsive function, and the relative velocity repulsive function are respectively:
wherein,,
for target attraction->
For obstacle repulsive force, < >>
For the repulsive force of the relative velocity, d 1 D is the distance between the automobile and the target point 2 For the distance between the car and the obstacle +.>
，
X and y are the position coordinates of the automobile, x rep And y rep Is the obstacle position coordinates, < >>
、
、/>
The gravity gain coefficient, the repulsion gain coefficient and the relative speed repulsion gain coefficient, v rel For the relative speed of the car and the obstacle, +.>
Is the angle between the attractive and repulsive forces.
Further, in step 2, the expression of the road longitudinal force function is:
wherein,,
represents the longitudinal force of the road, d is the longitudinal distance between the automobile and the road, d 0 For the road longitudinal force effective distance, < > and->
Is the longitudinal force gain coefficient of the road.
Further, the step 3 specifically includes:
step 301, establishing a coordinate system by using a road boundary in a driving environment model;
step 302, selecting position coordinates of an automobile, a target point and an obstacle based on the established coordinate system, and setting automobile parameters;
step 303, controlling the vehicle to advance according to the set vehicle parameters, detecting whether the road longitudinal force effective distance is reached in real time in the vehicle advancing process, if the road longitudinal force effective distance is reached, entering step 304, and if the road longitudinal force effective distance is not reached, entering step 305;
step 304, the road longitudinal force takes effect, and the automobile moves forward under the combined action of the target gravitation function, the obstacle repulsive force function, the relative speed repulsive force function and the road longitudinal force function to obtain a preliminary planning path;
step 305, the car goes forward under the combined action of the target gravitation function, the obstacle repulsive force function and the relative speed repulsive force function, and a preliminary planning path is obtained.
Further, in step 5, the evaluation function and the weighting coefficient of the dynamic window are modified as follows:
wherein,,
evaluation function representing improved dynamic window, < ->
Representing the improved weighting coefficients of the model,
for pose function, add->
For the distance function of the driving track and the target point, +.>
As a function of the linear speed of the driving track, +.>
As a function of the distance between the driving trajectory and the obstacle, < > and the distance between the obstacle and the driving trajectory>
Weighting coefficients for pose functions, +.>
Weighting coefficient for the distance function of the driving track and the target point, < ->
Weighting coefficient for the linear speed function of the driving trajectory, < ->
The min represents the weighting coefficient of the function of the distance between the driving track and the obstacleMinimum, max represents the maximum value.
Drawings
FIG. 1 is a flow chart of a method for planning a vehicle path by fusing an improved potential field with a dynamic window according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the potential field stress of an automobile according to the present invention;
FIG. 3 is a diagram showing the comparison between the method of the present embodiment and the conventional potential field algorithm in solving the problem of target unreachability;
fig. 4 is a schematic diagram showing the comparison between the method of the present embodiment and the result of the conventional potential field algorithm in solving the local optimum problem.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a method for planning an automobile path by fusing an improved potential field and a dynamic window, comprising the following steps:
and step 1, modeling a driving environment by adopting a potential field method to obtain a driving environment model.
And 2, obtaining coordinate positions of the automobile, the target point, the obstacle and the road boundary in the driving environment model, and establishing a plurality of potential field functions according to the position relation between the automobile and the target point, between the automobile and the obstacle and between the automobile and the road boundary, wherein the plurality of potential field functions specifically comprise a target gravitation function, an obstacle repulsive force function, a relative speed repulsive force function and a road longitudinal force function.
The expressions of the target gravitation function, the obstacle repulsive force function and the relative speed repulsive force function are respectively as follows:
wherein,,
for target attraction->
For obstacle repulsive force, < >>
For the repulsive force of the relative velocity, d 1 D is the distance between the automobile and the target point 2 For the distance between the car and the obstacle +.>
，
X and y are the position coordinates of the automobile, x rep And y rep Is the obstacle position coordinates, < >>
、
、/>
The gravity gain coefficient, the repulsion gain coefficient and the relative speed repulsion gain coefficient, v rel For the relative speed of the car and the obstacle, +.>
Is the angle between the attractive and repulsive forces.
The expression of the road longitudinal force function is:
wherein,,
represents the longitudinal force of the road, d is the longitudinal distance between the automobile and the road, d 0 For the road longitudinal force effective distance, < > and->
Is the longitudinal force gain coefficient of the road.
The problem of local optimum is typically trapped in conventional potential fields because the repulsive and attractive forces are in a straight line and equal, and the problem of target unreachable may be due to excessive repulsive forces. In this embodiment, referring to fig. 2, the longitudinal force and the repulsive force of the relative speed are added to effectively solve the two problems. When the vertical distance between the automobile and the road is within the effective distance range of the longitudinal force of the road, the current planned path of the automobile is indicated to possibly run out of the preset road, the newly added longitudinal force of the road can ensure that the automobile runs towards the center of the road, and the safety and stability of the running process of the automobile are improved.
And step 3, obtaining a preliminary planning path based on the driving environment model and a plurality of potential field functions.
The step 3 specifically includes:
step 301, establishing a coordinate system by using a road boundary in a driving environment model;
step 302, selecting position coordinates of an automobile, a target point and an obstacle based on the established coordinate system, and setting automobile parameters;
step 303, controlling the vehicle to advance according to the set vehicle parameters, detecting whether the road longitudinal force effective distance is reached in real time in the vehicle advancing process, if the road longitudinal force effective distance is reached, entering step 304, and if the road longitudinal force effective distance is not reached, entering step 305;
step 304, the road longitudinal force takes effect, the automobile moves forward under the combined action of the target gravitation function, the obstacle repulsive force function, the relative speed repulsive force function and the road longitudinal force function, and a preliminary planning path is obtained, wherein the direction of the road longitudinal force is vertical to the center of the road, and the automobile is prevented from moving away from the road;
step 305, the car goes forward under the combined action of the target gravitation function, the obstacle repulsive force function and the relative speed repulsive force function, and a preliminary planning path is obtained.
And 4, acquiring a key path node from the automobile to each obstacle based on the preliminary planning path.
And 5, improving the evaluation function and the weighting coefficient of the dynamic window to obtain an improved dynamic window.
Wherein, the evaluation function and the weighting coefficient of the dynamic window are improved as follows:
wherein,,
evaluation function representing improved dynamic window, < ->
Representing the improved weighting coefficients of the model,
a pose function for enabling the advancing direction of the automobile to always face the target point; />
The method comprises the steps of estimating the distance between the end point of a planned path and a target point for a distance function of a driving track and the target point; />
As a function of the linear velocity of the travel path,for detecting the relative speed of the vehicle to the obstacle; />
The obstacle avoidance system is a function of the distance between a driving track and an obstacle and is used for enabling the automobile to avoid the obstacle accurately; />
Weighting coefficients for pose functions, +.>
Weighting coefficient for the distance function of the driving track and the target point, < ->
Weighting coefficient for the linear speed function of the driving trajectory, < ->
The weighting coefficient of the function of the distance between the driving track and the obstacle is min, min and max.
And 6, inputting the critical path nodes from the automobile to each obstacle into an improved dynamic window, sampling the relative speed of the automobile and the obstacle on the critical path nodes, the pose of the automobile, the distance between the automobile track and the obstacle and the minimum safety distance of the automobile running through an evaluation function of the improved dynamic window, and carrying out real-time optimization and updating on the preliminary planning path through the improved dynamic window to obtain the optimized planning path.
In the process of optimizing and updating the preliminary planning path in real time by utilizing the improved dynamic window, judging whether the automobile reaches a target point in real time, and if so, inputting the optimized planning path; if not, returning to the step of inputting the critical path nodes from the automobile to each obstacle into the improved dynamic window, and continuing to optimize and update until the automobile reaches the target point.
The method of this embodiment is compared with the planned path of a conventional potential field algorithm as follows.
As shown in fig. 3, fig. 3 is a schematic diagram showing the comparison of the results of the method of the present embodiment and the conventional potential field algorithm when solving the problem of target unreachability.
The MatlabR2022a version is used in the simulation experiment, and compared with the method of the embodiment and the traditional potential field algorithm, the method solves the problem that the target is unreachable. In the simulation, the black solid squares represent cars, coordinates (1,0.25); the circles are obstacles, and the coordinates of partial obstacles are (2,2.58), (3.9,3.5), (4.8,5.6), (7.5,7); the white hollow square represents the target point, and the coordinates are (9.5,9); through simulation verification, compared with the traditional potential field algorithm, the method of the embodiment can effectively solve the problem that the target is unreachable.
Further, as shown in fig. 4, fig. 4 is a schematic diagram showing the comparison between the method of the present embodiment and the result of the conventional potential field algorithm when solving the local optimum problem.
The simulation experiment uses MatlabR2022a version, and the method of the embodiment is compared with the traditional potential field algorithm to solve the problem of local optimum. In the simulation, the black solid squares represent cars, coordinates (1,0.25); the circle is an obstacle, and the coordinates of partial obstacles are (2, 2), (4.6,2), (3.9,4), (6.2,4.7); the white hollow square represents the target point, and the coordinates are (8.5,9.5); through simulation verification, compared with the traditional potential field algorithm, the method of the embodiment can effectively solve the problem of local optimum.
Therefore, the path planned by the method can smoothly avoid the obstacle to reach the target point, and the planned path is shorter, smoother and more stable, and meets the requirements in the actual driving environment.
In summary, the automobile path planning method for fusing the improved potential field and the dynamic window has the following beneficial effects:
(1) On the basis of the traditional potential field function, a relative speed repulsive force function and a road longitudinal force function are added, so that the problems of local optimum and unreachable targets in the traditional algorithm can be effectively solved;
(2) The longitudinal force of the road added in the traditional potential field function can ensure that the automobile runs towards the center of the road, and the safety and stability of the running process of the automobile are improved;
(3) The relative speed, the pose, the track and the obstacle distance of the automobile and the obstacle in each critical path node are sampled through the improved dynamic window, so that the preliminary planning path can be dynamically updated in real time, and the requirements in the actual driving environment can be better met.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. The automobile path planning method integrating the improved potential field and the dynamic window is characterized by comprising the following steps of:
step 1, modeling a driving environment by adopting a potential field method to obtain a driving environment model;
step 2, in a driving environment model, coordinate positions of an automobile, a target point, an obstacle and a road boundary are obtained, and then a plurality of potential field functions are established according to the position relation between the automobile and the target point, between the automobile and the obstacle and between the automobile and the road boundary, wherein the plurality of potential field functions specifically comprise a target gravitation function, an obstacle repulsive force function, a relative speed repulsive force function and a road longitudinal force function;
step 3, obtaining a preliminary planning path based on a driving environment model and a plurality of potential field functions;
step 4, acquiring key path nodes from the automobile to each obstacle based on the preliminary planning path;
step 5, improving the evaluation function and the weighting coefficient of the dynamic window to obtain an improved dynamic window;
and 6, inputting the critical path nodes from the automobile to each obstacle into an improved dynamic window, sampling the relative speed of the automobile and the obstacle on the critical path nodes, the pose of the automobile, the distance between the automobile track and the obstacle and the minimum safety distance of the automobile running through an evaluation function of the improved dynamic window, and carrying out real-time optimization and updating on the preliminary planning path through the improved dynamic window to obtain the optimized planning path.
2. The method for planning an automobile path by fusing an improved potential field and a dynamic window according to claim 1, wherein in step 2, expressions of a target attraction function, an obstacle repulsive force function, and a relative speed repulsive force function are respectively:
wherein,,
for target attraction->
For obstacle repulsive force, < >>
For the repulsive force of the relative velocity, d 1 D is the distance between the automobile and the target point 2 For the distance between the car and the obstacle +.>
，
X and y are the position coordinates of the automobile, x rep And y rep Is the obstacle position coordinates, < >>
、
、/>
The gravity gain coefficient, the repulsion gain coefficient and the relative speed repulsion gain coefficient, v rel For the relative speed of the car and the obstacle, +.>
D is the angle between the attractive force and the repulsive force 0 Is the effective distance of the longitudinal force of the road.
3. The method for planning a vehicle path by fusing an improved potential field and a dynamic window according to claim 2, wherein in step 2, the expression of the road longitudinal force function is:
wherein,,
represents the longitudinal force of the road, d is the longitudinal distance of the car from the road, < >>
Is the longitudinal force gain coefficient of the road.
4. The method for planning a path of an automobile by fusing an improved potential field with a dynamic window according to claim 3, wherein step 3 specifically comprises:
step 301, establishing a coordinate system by using a road boundary in a driving environment model;
step 302, selecting position coordinates of an automobile, a target point and an obstacle based on the established coordinate system, and setting automobile parameters;
step 303, controlling the vehicle to advance according to the set vehicle parameters, detecting whether the road longitudinal force effective distance is reached in real time in the vehicle advancing process, if the road longitudinal force effective distance is reached, entering step 304, and if the road longitudinal force effective distance is not reached, entering step 305;
step 304, the road longitudinal force takes effect, and the automobile moves forward under the combined action of the target gravitation function, the obstacle repulsive force function, the relative speed repulsive force function and the road longitudinal force function to obtain a preliminary planning path;
step 305, the car goes forward under the combined action of the target gravitation function, the obstacle repulsive force function and the relative speed repulsive force function, and a preliminary planning path is obtained.
5. The method for planning a vehicle path by fusing an improved potential field and a dynamic window as claimed in claim 4, wherein in step 5, the evaluation function and the weighting coefficient of the dynamic window are improved as follows:
wherein,,
evaluation function representing improved dynamic window, < ->
Representing the improved weighting coefficients of the model,
for pose function, add->
For the distance function of the driving track and the target point, +.>
As a function of the linear speed of the driving track, +.>
As a function of the distance between the driving trajectory and the obstacle, < > and the distance between the obstacle and the driving trajectory>
Weighting coefficients for pose functions, +.>
Weighting coefficient for the distance function of the driving track and the target point, < ->
Weighting coefficient for the linear speed function of the driving trajectory, < ->
The weighting coefficient of the function of the distance between the driving track and the obstacle is min, min and max.
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