CN112344943B - Intelligent vehicle path planning method for improving artificial potential field algorithm - Google Patents
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Abstract
The invention discloses an intelligent vehicle path planning method for improving an artificial potential field algorithm, which comprises the steps of collecting position parameters of a starting point, a target point and an obstacle, constructing a gravitational potential field function and a repulsive potential field function, calculating a resultant force borne by an intelligent vehicle at a current position point according to the gravitational potential field function and the repulsive potential field function, and drawing the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path; and a step of escaping from the local minimum value point, wherein a random point is taken from a circle which takes the current point or the obtained motion point as a circle center and the radius as a step length, whether the random point is set as a next motion point is judged by using a structured probability function, whether the random point escapes from the local minimum value point is judged according to the magnitude of the potential field, the iteration times, namely the step number, are limited by a potential energy threshold value, and finally an escaping track with the best smoothness in the limited step number is selected, so that the step length and the track of the affected area are automatically adjusted according to the size of the affected area of the local minimum value point, the smoothness of the track, the moving step number and the calculated amount are difficult to balance, and the problems in the prior art are solved.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and relates to an intelligent vehicle path planning method for improving an artificial potential field algorithm.
Background
The path planning method comprises the steps of planning a collision-free path from an initial state (position and posture) to a target state according to a certain evaluation standard under the environment of an obstacle, mainly considering the geometric relationship between a local moving body and the obstacle, and finding out the path without collision. The path is a static geometric locus, does not contain a time concept, and generally represents the position and posture relation of the intelligent vehicle in Cartesian coordinates.
Because the artificial potential field method is simple and practical, has good real-time performance and simple structure, is convenient for the real-time control of the bottom layer, and is widely applied to the aspects of real-time obstacle avoidance and smooth track control. But tends to fall into a local minimum. When the attractive force and the repulsive force of the intelligent vehicle are equal in magnitude and opposite in direction, the resultant force of the intelligent vehicle is 0, and the intelligent vehicle sinks into a local minimum value point. The resultant force of each position around the local minimum value point points to the local minimum value, so that the intelligent vehicle oscillates around the local minimum value point and cannot go out of the area.
Although some methods for moving the local minimum value point to the outside to be separated from the area are provided in the prior art, the existing methods only control the vehicle to move out of the local minimum value point influence area, the influence of the moving step length of the vehicle on the smoothness of the track is large, the smoothness of the track with large step length is poor, the path is long, the waste of energy consumption and time is caused, the step length is small, the fact that the vehicle moves out of the influence area in a limited number of steps cannot be guaranteed, the step length cannot be adjusted according to the size of the influence area, the number of moving steps is excessive, and the calculation amount is large under the condition of integral path planning.
Disclosure of Invention
The invention aims to provide an intelligent vehicle path planning method for improving an artificial potential field algorithm, and aims to solve the technical problems that in the prior art, the step length and the track of an influence area cannot be automatically adjusted according to the size of the influence area of a local minimum value point, and the smoothness of the track, the number of moving steps and the calculated amount are difficult to balance.
The intelligent vehicle path planning method for improving the artificial potential field algorithm comprises the steps of collecting position parameters of a starting point, a target point and an obstacle, constructing a gravitational potential field function and a repulsive potential field function, calculating a resultant force applied to the intelligent vehicle at the current position point according to the gravitational potential field function and the repulsive potential field function, and drawing the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path;
when the intelligent vehicle falls into the local minimum value point, executing the step of escaping from the local minimum value point: setting X as S, S as local minimum point, taking a random point X on a circle with point X as center of circle and radius as step length delta X 1 Finding out the motion points meeting the requirement from the random points according to the set requirement until the last motion point meets the condition of escaping from the local minimum value point, then forming the previous motion points into alternative paths and temporarily storing the alternative paths, reducing the value of the step length delta X, resetting X to S and repeating the steps to obtain a plurality of alternative paths, wherein in the steps, the potential energy T of the points is introduced after setting X to S, the potential energy T is generated by an iterative formula and is iteratively reduced, and when the potential energy T is less than the set threshold value, the generation of the alternative paths is stopped and the last motion of the previous alternative paths is carried outAnd selecting a path corresponding to the lowest potential energy point as a final result, and continuing to pull the intelligent vehicle to advance to a target point from the last moving point in the result to generate a corresponding path.
Preferably, the step of escaping from the local minimum point is specifically as follows:
s1, setting X as S, wherein S is a local minimum point;
s2, T representing potential energy is set, and the iterative calculation formula of T is T (T) ═ α T (T-1),0.85 < α < 1, and T is set to be in an initial state 0 ，T 0 Is a set value and is more than 0, and t is the iteration times;
s3, Δ X is the set step length, T f For setting the threshold value, when T ≧ T is satisfied f Generating a random point X 1 If not, go to step S9;
s4, calculating U (X) 1 ) and-U (X), point X 1 And the potential field at X, and further calculating a potential field difference value Δ, Δ ═ U (X) 1 )-U(X)；
S5, judging whether delta is less than or equal to 0, if yes, considering X 1 If the point is a valid random point, otherwise, executing the next step;
s6, calculating probability
And setting a random probability a with the value range of 0 to 1, and considering X when P is more than a 1 Resetting random point X to be a valid random point, otherwise, considering invalid 1 Repeating the steps S3-S6;
s7, obtaining the effective random point, setting it as the motion point of the next step, and setting X ═ X 1 ；
S8, determining U (X) 1 ) If the result is not greater than U (S), the result is that the motion points in one step successfully escape from the local minimum value point, the path formed by the previous motion points is recorded as an alternative path, the step length delta X is reduced, the step S1 is returned, and if the result is not greater than U (S), the step S2 is returned;
and S9, according to the potential energy T of the final motion point of each alternative path, selecting the path corresponding to the lowest potential energy point as the path of the final escape local minimum point.
Preferably, the specific steps of towing the intelligent vehicle to advance to the target point to generate the corresponding path are as follows:
collecting position parameters of a starting point, a target point and an obstacle, and setting the starting point, the terminal point and the obstacle point in a planned path;
constructing a gravitational potential field function and a repulsive force potential field function according to the positions of the intelligent vehicle, the target point and the obstacle point respectively;
step three, respectively solving negative gradients of the attraction potential field function and the repulsion potential field function to obtain attraction force and repulsion force borne by the intelligent vehicle;
and step four, calculating resultant force borne by the point according to the attractive force and the repulsive force obtained in the step three, and dragging the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path.
Preferably, a point exists in the process of generating the path, where the resultant force formed by the current attraction force and the current repulsion force is 0, and whether the point is a target point is judged, and if not, it is judged that the smart car is trapped in the local minimum point at the point.
Preferably, in the second step: gravitational potential field function U att The formula of (1) is as follows:
in the above formula, k att Representing the gravity gain coefficient, q is the coordinate point of the intelligent vehicle, q g As coordinate points of the target point, q-q g The distance between the intelligent vehicle and the target point;
repulsive force potential field function U rep The formula of (1) is:
in the above formula, K rep Is the repulsive gain coefficient, ρ obs (q)＝||q-q obs ||，q obs As obstacle coordinates, p 0 For the maximum influence distance, if the distance between the obstacle and the intelligent vehicle is greater than rho 0 The repulsive force field is considered to be 0; potential field of point X
-U(X)＝U att +U rep 。
Preferably, in the third step, a negative gradient is applied to the potential field to obtain a force applied to the intelligent vehicle in the potential field:
intelligent vehicle current gravitation F att The formula of (1) is:
in the above formula
Being a negative gradient of the gravitational potential field, F att The direction of the intelligent vehicle points to a target point, and the linear relation is formed between the direction of the intelligent vehicle and the distance from the current position of the intelligent vehicle to the position of the target point;
repulsion F currently suffered by intelligent vehicle rep The formula of (1) is:
F rep pointing in the opposite direction to the obstacle point.
Preferably, the method for obtaining the force in the third step comprises:
F att can be decomposed into a force in the x-axis direction and a force in the y-axis direction:
F attx ＝-K att (x-x g )，
F atty ＝-K att (y-y g )，
F rep can be decomposed into a force in the x-axis direction and a force in the y-axis direction as follows:
the invention has the technical effects that: 1. the invention automatically starts the step of escaping from the affected area under the condition of entering the local minimum value point, by the method, whether the escape track is taken as a moving point of the escape track or not is estimated according to the calculated probability P, judging whether to escape from the local minimum value point according to the comparison with the potential field size of the local minimum value point, setting a plurality of alternative paths within a certain step number through step length reduction and potential energy threshold control in the steps, selecting one of the alternative paths with the largest step number and the best smoothness to ensure that the escape path is smooth enough within a certain step length, thus, the step length used for the escape path in the local minimum point area is controlled, the calculated amount of the whole path planning is not too large, the escape path is ensured to be smooth enough under the limitation of the step length, the redundant path is reduced, the planned route is shorter than the prior art, a balance between the smoothness of the path to escape from the minima points and the number of steps and calculations required is thus achieved.
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FIG. 1 is a flow chart of an intelligent vehicle path planning method for improving an artificial potential field algorithm according to the invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
As shown in FIG. 1, the invention provides an intelligent vehicle path planning method for improving an artificial potential field algorithm, which comprises the following steps.
Step one, collecting position parameters of a starting point, a target point and an obstacle, and setting the starting point, the terminal point and the obstacle point in a planned path.
And step two, constructing a gravitational potential field function and a repulsive potential field function according to the positions of the intelligent vehicle, the target point and the obstacle point.
Wherein, an attractive force potential field function U is constructed according to the distance between the intelligent vehicle and a target point att The formula of (1) is as follows:
in the above formula, k att Representing the gravitational gain coefficient, q is the coordinate point of the intelligent vehicle, q g As coordinate points of target points, q-q g The distance between the intelligent vehicle and the target point;
constructing a repulsive force potential field function U according to the distance between an intelligent vehicle and a target point and an obstacle rep The formula of (1) is:
in the above formula, K rep Is the repulsive gain coefficient, ρ obs (q)＝||q-q obs ||，q obs As obstacle coordinates, p 0 For the maximum influence distance, if the distance between the obstacle and the intelligent vehicle is greater than rho 0 The repulsive force field is considered to be 0.
After combining the two, the potential field at point X is: -U (x) U att +U rep 。
And step three, respectively solving a negative gradient of the attraction force potential field function and the repulsion force potential field function to obtain the attraction force and the repulsion force borne by the intelligent vehicle.
And obtaining the force applied to the intelligent vehicle in the potential field by solving the negative gradient of the potential field according to the current position of the intelligent vehicle.
Intelligent vehicle current gravitation F att The formula of (1) is:
in the above formula
Being a negative gradient of the gravitational potential field, F att The direction of the intelligent vehicle points to a target point, and the linear relation is formed between the direction of the intelligent vehicle and the distance from the current position of the intelligent vehicle to the position of the target point;
repulsion F currently suffered by intelligent vehicle rep The formula of (1) is:
F rep pointing in the opposite direction of the obstacle point.
Preferably, the method for obtaining the resultant force in the third step comprises:
F att can be decomposed into a force in the x-axis direction and a force in the y-axis direction:
F attx ＝-K att (x-x g )，
F atty ＝-K att (y-y g )，
F rep can be decomposed into a force in the x-axis direction and a force in the y-axis direction as follows:
and step four, calculating resultant force borne by the point according to the attractive force and the repulsive force obtained in the step three, and dragging the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path. And in the process of generating the path, a point with the resultant force of the current attraction force and the current repulsion force being 0 is existed, whether the point is a target point or not is judged, and if not, the intelligent vehicle is judged to be trapped in the local minimum point at the point.
The step of escaping from the local minimum point is specifically as follows.
And S1, setting X as S, wherein S is a local minimum point.
S2, T representing potential energy is set, and the iterative calculation formula of T is T (T) ═ α T (T-1),0.85 < α < 1, and T is set to be in an initial state 0 ，T 0 Is a set value and is more than 0, and t is the iteration times; the number of iterations determines the number of motion points, i.e. the number of steps in the trajectory.
S3, Δ X is the set step length, T f For setting the threshold value, when T ≧ T is satisfied f Generating a random point X 1 If not, go to step S9.
S4, calculating U (X) 1 ) and-U (X), point X 1 And the potential field at X, and further calculating a potential field difference value Δ, Δ ═ U (X) 1 )-U(X)。
S5, judging whether delta is less than or equal to 0, if yes, considering X 1 And if the random point is a valid random point, otherwise, executing the next step.
S6, calculating probability
And setting a random probability a with the value range of 0 to 1, and considering X when P is more than a 1 Resetting random point X to be a valid random point, otherwise, considering invalid 1 And repeating the steps S3-S6. And (4) randomly searching a global optimal solution of the objective function in a solution space by combining with the probability jump characteristic, namely, probabilistically jumping out in a local optimal solution and finally tending to the global optimal.
And S7, setting the obtained effective random point as the moving point of the next step, and setting X to X 1 。
S8, determining U (X) 1 ) If the result is less than or equal to U and S, if so, the moving points in one step successfully escape from the local minimum value points, the path formed by the moving points before is recorded as an alternative path, and the step length delta X is reduced. When Δ X (t) ═ α Δ X (t-1) and 0.85 < α < 1, the process returns to step S1, and if not, the process returns to step S2.
And S9, selecting the path corresponding to the potential energy lowest point as the path of the last escaping local minimum point according to the potential energy T of the last moving point of each alternative path.
The invention is described above with reference to the accompanying drawings, it is obvious that the specific implementation of the invention is not limited by the above-mentioned manner, and it is within the scope of the invention to adopt various insubstantial modifications of the inventive concept and solution of the invention, or to apply the inventive concept and solution directly to other applications without modification.
Claims (6)
1. An intelligent vehicle path planning method for improving an artificial potential field algorithm is characterized by comprising the following steps of: acquiring position parameters of a starting point, a target point and an obstacle, constructing a gravitational potential field function and a repulsive potential field function, calculating a resultant force applied to the intelligent vehicle at the current position point according to the gravitational potential field function and the repulsive potential field function, and drawing the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path;
when the intelligent vehicle falls into the local minimum value point, executing the step of escaping from the local minimum value point: setting X as S, S as local minimum point, taking a random point X on a circle with point X as center of circle and radius as step length delta X 1 Finding out the motion points meeting the requirements from the random points according to the set requirements until the last motion point meets the condition of escaping from the local minimum value point, then forming all the previous motion points into alternative paths and temporarily storing the alternative paths, reducing the value of the step length delta X, resetting X to S and repeating the steps to obtain a plurality of alternative paths, introducing the potential energy T of the point after setting X to S in the steps, generating the potential energy T by an iterative formula and iteratively reducing the potential energy T, stopping generating the alternative paths when the potential energy T is less than the set threshold value, selecting the path corresponding to the lowest potential energy point as a final result according to the potential energy T of the last motion point of all the previous alternative paths, and continuously towing the intelligent vehicle to a target point from the last motion point in the final result to generate a corresponding path;
the steps for escaping from the local minimum value point are as follows:
s1, setting X as S, wherein S is a local minimum point;
s2, T representing potential energy is set, and an iterative calculation formula of T is T (T) ═ α T (T-1),0.85 < α < 1, and T ═ T is set in an initial state 0 ，T 0 Is a set value and is more than 0, and t is the iteration frequency;
s3, Δ X is the set step length, T f For setting the threshold value, when T ≧ T is satisfied f Generating a random point X 1 If not, go to step S9;
s4, calculating U (X) 1 ) And U (X), point X 1 And the potential field at X, and further calculating a potential field difference value Δ, Δ ═ U (X) 1 )-U(X)；
S5, judging whether delta is less than or equal to 0, if yes, considering X 1 If the point is a valid random point, otherwise, executing the next step;
s6, calculating probability
And setting the value range from 0 to1, and X is considered when P > a 1 Resetting random point X to be a valid random point, otherwise, considering invalid 1 Repeating steps S3-S6;
s7, obtaining the effective random point, setting it as the motion point of the next step, and setting X ═ X 1 ；
S8, determining U (X) 1 ) If the result is not greater than U (S), the result is that the motion points in one step successfully escape from the local minimum value point, the path formed by the previous motion points is recorded as an alternative path, the step length delta X is reduced, the step S1 is returned, and if the result is not greater than U (S), the step S2 is returned;
and S9, selecting the path corresponding to the potential energy lowest point as the path of the last escaping local minimum point according to the potential energy T of the last moving point of each alternative path.
2. An intelligent vehicle path planning method for improving an artificial potential field algorithm according to claim 1, characterized in that: the specific steps of drawing the intelligent vehicle to advance to the target point to generate the corresponding path are as follows:
collecting position parameters of a starting point, a target point and an obstacle, and setting the starting point, the terminal point and the obstacle point in a planned path;
constructing a gravitational potential field function and a repulsive force potential field function according to the positions of the intelligent vehicle, the target point and the obstacle point respectively;
step three, respectively solving a negative gradient of the attraction force potential field function and the repulsion force potential field function to obtain the attraction force and the repulsion force borne by the intelligent vehicle;
and step four, calculating resultant force borne by the point according to the attractive force and the repulsive force obtained in the step three, and dragging the intelligent vehicle to advance to the target point according to the resultant force to generate a corresponding path.
3. The intelligent vehicle path planning method for improving the artificial potential field algorithm according to claim 2, characterized by comprising the following steps: and in the process of generating the path, a point with the resultant force of the current attraction force and the current repulsion force being 0 is existed, whether the point is a target point or not is judged, and if not, the intelligent vehicle is judged to be trapped in the local minimum point at the point.
4. An intelligent vehicle path planning method for improving an artificial potential field algorithm according to claim 2, characterized in that: in the second step: gravitational potential field function U att The formula of (1) is:
in the above formula, k att Representing the gravity gain coefficient, q is the coordinate point of the intelligent vehicle, q g As coordinate points of the target point, q-q g The distance between the intelligent vehicle and the target point;
repulsive potential field function U rep The formula of (1) is:
in the above formula, K rep Is the repulsive gain coefficient, ρ obs (q)＝||q-q obs ||，q obs As obstacle coordinates, p 0 For the maximum influence distance, if the distance between the obstacle and the intelligent vehicle is greater than rho 0 Then the repulsive force field is considered to be 0; potential field of point X
-U(X)＝U att +U rep 。
5. An intelligent vehicle path planning method for improving an artificial potential field algorithm according to claim 4, wherein the method comprises the following steps: and in the third step, obtaining the force applied to the intelligent vehicle in the potential field by solving the negative gradient of the potential field:
intelligent vehicle current gravitation F att The formula of (1) is:
in the above formula
Being a negative gradient of the gravitational potential field, F att The direction of the intelligent vehicle points to the target point, and the linear relation is formed between the distance from the current position of the intelligent vehicle to the position of the target point；
Repulsion F currently suffered by intelligent vehicle rep The formula of (1) is:
F rep pointing in the opposite direction to the obstacle point.
6. An intelligent vehicle path planning method for improving an artificial potential field algorithm according to claim 5, wherein the method comprises the following steps: the method for solving the force in the third step comprises the following steps:
F att the force is decomposed into a force in the x-axis direction and a force in the y-axis direction:
F attx ＝-K att (x-x g )，
F atty ＝-K att (y-y g )，
wherein x is g ， y g Corresponding representation target point coordinates q g Coordinate values on the x-axis and y-axis;
F rep the force in the x-axis direction and the force in the y-axis direction are decomposed as follows:
wherein x is obs ，y obs Corresponding representation of obstacle coordinates q obs Coordinate values on the x-axis and y-axis.
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