CN114442637A - Unmanned vehicle local dynamic obstacle avoidance path planning method - Google Patents

Unmanned vehicle local dynamic obstacle avoidance path planning method Download PDF

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CN114442637A
CN114442637A CN202210126582.2A CN202210126582A CN114442637A CN 114442637 A CN114442637 A CN 114442637A CN 202210126582 A CN202210126582 A CN 202210126582A CN 114442637 A CN114442637 A CN 114442637A
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vehicle
target point
obstacle
force
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CN114442637B (en
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张雪莹
翟丽
王承平
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Beijing Institute of Technology BIT
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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
    • 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
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process

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Abstract

The invention provides a method for planning a local dynamic obstacle avoidance path of an unmanned vehicle, which solves the problems that the target cannot be reached and the local is optimal under partial working conditions by improving an artificial potential field method, so that the unmanned vehicle overcomes local oscillation, and a planned local planning path meeting the obstacle avoidance requirement can be planned; in the method, the requirements of vehicle kinematics and dynamics performance are added in the path planning process, and the collision safety priority is set, so that the smoothness of the planned path is ensured, and the tracking requirements of a lower-layer vehicle tracker are met; by analyzing the working conditions of the complex dynamic obstacles in the road environment, the obstacle avoidance planning method can respectively carry out obstacle avoidance planning on the lateral dynamic obstacles and the homodromous obstacles, and combines the two working conditions, so that the planning algorithm meets the obstacle avoidance of the multiple dynamic obstacles, and the applicability and the effectiveness of the planning algorithm are improved.

Description

Unmanned vehicle local dynamic obstacle avoidance path planning method
Technical Field
The invention belongs to the technical field of unmanned vehicle automatic driving, and particularly relates to a method for planning a local dynamic obstacle avoidance path of an unmanned vehicle.
Background
The path planning is one of key technologies in the field of unmanned vehicles, determines whether the unmanned vehicles can stably and safely run, and plays a role of a bridge between the vehicle environment information perception and the vehicle intelligent control function. The path planning algorithm adopted by the current unmanned vehicle mainly comprises A*、D*And algorithms such as fast random trees, which have been applied to many real vehicle platforms initially, are limited by the disadvantage of high self-calculation amount, and can only be used for static planning at present. The artificial potential field method has simple model structure and can be used without large calculation amountThe system has the advantages of simple structure, good real-time performance, smooth generated path and the like, and is favorable for application in dynamic planning aspects such as real-time obstacle avoidance, smooth track control and the like. However, the existing artificial potential field method has the following problems:
1. the problems of unreachable target, local optimization and the like exist in a complex environment, so that the unmanned vehicle is locally vibrated or cannot stop walking, and the requirements of local obstacle avoidance planning are not met;
2. most potential field method path planning does not consider vehicle kinematics and dynamic performance, so that the planned path does not meet the vehicle tracking requirement;
3. most potential field law and law transfer methods are applicable to static scenes or simple dynamic scenes, and when complex dynamic obstacles exist in a road environment, a planning algorithm cannot effectively plan an obstacle avoidance path in real time.
Disclosure of Invention
In order to overcome the technical problems in the prior art, the invention provides a method for planning a local dynamic obstacle avoidance path of an unmanned vehicle, which specifically comprises the following steps:
the method comprises the steps that firstly, aiming at a local obstacle avoidance driving environment where an unmanned vehicle is located, a driving target point and an attraction force field and repulsion force field function model of the obstacle to the unmanned vehicle in the environment are respectively established, the attraction force field and repulsion force field function model are used for reflecting the relation between the attraction force of the target point to the unmanned vehicle and the position of the unmanned vehicle, the repulsion force of the obstacle to the unmanned vehicle and the position of the unmanned vehicle, and a driving environment risk field force function model is established through the attraction force and the repulsion force;
step two, when the constructed driving environment risk field potential force function model reaches the local minimum value and the resultant force borne by the vehicle makes the vehicle far away from the target point, determining the corresponding specific stress working condition of the vehicle, which causes that the unmanned vehicle cannot drive to the target point;
step three, aiming at different specific stress working conditions of the vehicle, respectively providing corresponding path planning solutions: temporarily changing the gravitation by using a set virtual target point; canceling the repulsion force of the obstacle which is far away from the target point in the driving direction; improving a gravitational potential field function model according to the number of the obstacles; resolving the repulsion force and canceling the repulsion force component in a certain direction to enable the resultant force of the residual repulsion force component and the attraction force to be in the direction approaching the target point;
step four, setting a dynamic path planning cycle, a safe distance threshold value and a collision risk constraint condition by combining the movement speed and the direction of the obstacle based on the solution provided in the step three so as to plan the obstacle avoidance path and obtain the position and the speed control quantity of the vehicle in a rolling manner in real time;
and step five, smoothing the vehicle position in the planned obstacle avoidance path by using the Bezier curve so as to obtain a complete unmanned vehicle local obstacle avoidance planned path.
Further, the building of the driving environment risk potential function model in the first step specifically includes the following processes:
the model of the gravitational potential field function of the target point to the unmanned vehicle is in the following form:
Figure BDA0003500609250000021
wherein eta is the direct proportional coefficient of the gravitational potential field, d (x, x)goal) The current position coordinate x and the target point position coordinate x of the vehiclegoalA distance vector in the direction from the vehicle to the target point;
deriving the gravitational potential field function to obtain a gravitational function Fatt，FattThe direction of (2) is the direction in which the gravitational potential energy is fastest to decline, namely, the negative gradient of the gravitational potential field, and the expression is as follows:
Figure BDA0003500609250000022
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003500609250000023
representing a gradient function. The above formula shows that the magnitude of the attractive force is proportional to the distance between the vehicle and the target point, and the farther the vehicle is from the target point, the more the vehicle is subjected toThe greater the attraction force, and conversely the smaller the attraction force. The gravitation F can be adjusted by the positive proportionality coefficient eta of the gravitation potential fieldattThe value of (c).
The repulsion potential field function model of the barrier to the unmanned vehicle specifically adopts the following form:
Figure BDA0003500609250000024
wherein k is a repulsive force potential field positive proportionality coefficient, d0The maximum influence range of the repulsion force generated by the obstacle on the vehicle is only when the vehicle moves to d0When the distance between the two is within the range, the repulsive force field is acted by the obstacle, and the acting force of the repulsive force field on the vehicle is larger along with the approach of the distance between the two; when the distance between the two is larger than d0Time, repulsive force field Urep＝0；xobsIs the position coordinate of the obstacle, d (x, x)obs) Is the distance between the vehicle and the obstacle;
the repulsive force function F can be obtained by derivation of the repulsive force potential field functionrep，FrepThe direction of (1) is the direction in which the repulsive force potential energy is most rapidly reduced, namely the negative gradient of the attractive force potential field, and the expression is as follows:
Figure BDA0003500609250000025
the driving environment risk field function is formed by overlapping a target point gravitational potential field function model and an obstacle repulsive force potential field function model, and the expression of the driving environment risk field function is as follows:
Figure BDA0003500609250000026
wherein n is the number of obstacles influencing the repulsion force of the vehicle;
and obtaining the driving environment risk field function derivative to obtain the driving risk field potential force function model, wherein the expression is as follows:
Figure BDA0003500609250000031
further, the specific stress condition of the vehicle, which causes the unmanned vehicle not to be able to travel to the target point in the step two, specifically includes:
the working condition I is as follows: the obstacle is positioned between the vehicle and the target point, the attraction force of the target point to the vehicle is equal to the repulsion force of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is stressed in a balanced manner and cannot move to the target point continuously;
working conditions are as follows: the obstacle is positioned in the driving direction but far away from the target point, the attraction force of the target point to the vehicle is equal to the repulsion force of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is stressed in balance and cannot move to the target point continuously;
working conditions are as follows: two or more obstacles are arranged on two sides of the vehicle in the direction towards the target point, and the resultant force of the repulsion force of the obstacles and the attraction force of the target point enables the vehicle to be stressed in balance and can not move to the target point continuously;
working conditions are as follows: more than two obstacles are arranged on two sides of the vehicle in the direction towards the target point, and the resultant force of the repulsive force of the obstacles and the attractive force of the target point enables the vehicle to be far away from the target point.
Further, the path planning solution provided in step three specifically performs the following operations for the various operating conditions:
providing a solution to the first working condition, namely providing a virtual target point near the target point, so that the vehicle can temporarily escape from a local minimum trap under the attraction of the virtual target point, and withdrawing the virtual target point at a proper time;
providing a solution II aiming at the working condition II, and directly canceling the repulsive force of the obstacle when the obstacle is positioned in the driving direction but far away from the target point, so that the driving risk field potential force function escapes from a local minimum trap;
aiming at the third working condition, firstly, judging whether the uninfluenced space between the obstacles meets the lateral safe passing distance S of the vehicle according to the influence range of the obstaclessafeIf the vehicle is satisfied, a solution is providedAccording to the number n of obstacles, the gravitational potential field function is improved in the following way to improve the gravitational magnitude:
Figure BDA0003500609250000032
if the lateral safe passing distance S is not satisfiedsafeProviding a solution that the potential force function of the driving risk field temporarily escapes from a local minimum trap;
and a solution is provided for the working condition four, the repulsive force is decomposed, the repulsive force component in a certain direction is cancelled, and the resultant force of the residual repulsive force component and the attractive force is changed into a direction approaching the target point.
Further, the following process is specifically executed in step four to perform obstacle avoidance path planning:
in order to improve the efficiency and accuracy of the planning algorithm, firstly, the obstacles in the driving local obstacle avoidance environment are primarily screened. Firstly, a virtual straight line from the self vehicle to a target point is established, and the expression is as follows:
LST:y＝kSTx+bST，x∈(xego,xtarget)
wherein the content of the first and second substances,
Figure BDA0003500609250000033
is a straight line LSTThe slope of (a) of (b) is,
Figure BDA0003500609250000034
is a straight line LSTIntercept of (x)ego,yego)、(xtarget,ytarget) Respectively are the position coordinates of the vehicle and the target point;
let the distance d from the obstacle to the vehicleobs-egoFrom obstacle to line LSTA distance of dobs-STWhen d isobs-egoOr dobs-STWhen any item is smaller than the respective safety threshold value, the corresponding barrier is judged to influence the subsequent path planning of the vehicle, and the barriers which possibly influence the path planning are screened outFor subsequent planning calculations. The obstacle pre-screening method ignores the influence of the obstacle with a longer distance on the self expected track, can effectively shorten the time of a path planning algorithm, and greatly improves the efficiency of path planning. dobs-egoOr dobs-STThe expression of (a) is as follows:
Figure BDA0003500609250000041
Figure BDA0003500609250000042
wherein d isobs-egoAnd dobs-STThe corresponding safety thresholds are respectively dobs-ego *And dobs-ST *；
Aiming at the lateral dynamic barrier, establishing a ray which takes the current position coordinate of the barrier as a starting point and points to the motion direction of the barrier and is marked as L according to the motion direction of the lateral dynamic barrierobs，LobsThe time changes along with the change of the coordinates and the movement direction of the obstacle, so that the movement track of the obstacle can be roughly predicted in a short time domain, and whether the obstacle possibly collides with the self vehicle is judged according to the movement intention of the obstacle. Ray LobsThe expression of (a) is as follows:
Figure BDA0003500609250000043
wherein, thetaobsThe included angle between the moving direction of the obstacle and the horizontal axis is a dynamic variable, and the position coordinate (x) of the obstacleobs,yobs) Updating at any moment along with the movement of the barrier;
let ray LobsAnd the straight line LSTHas a virtual intersection point of P (x)P,yP) The virtual intersection point P (x) can be obtained by combining two straight linesP,yP) The position coordinates of (a); the horizontal and vertical coordinate expressions are as follows:
Figure BDA0003500609250000044
respectively calculating the time required for the vehicle and the barrier to reach the virtual collision point P from the current position based on the virtual collision point P; let the current speed of the vehicle be vegoAcceleration of aegoThe current speed of the obstacle is vobsAcceleration of aobsThe time spent by the two respectively driving to the point P in the current motion state is tego-P、tobs-PThe expression is as follows:
Figure BDA0003500609250000051
Figure BDA0003500609250000052
Figure BDA0003500609250000053
Figure BDA0003500609250000054
wherein S isego-PDistance S from the present position of the ego-vehicle to the virtual intersection point Pobs-PThe distance from the current position of the obstacle vehicle to the virtual intersection point P;
by comparing tobs-PAnd tego-PDetermining the risk of collision between the vehicle and the lateral obstacle and maintaining or adjusting the running state of the vehicle by combining the distance between the vehicle and the obstacle;
and aiming at the equidirectional dynamic barrier, judging the risk of collision between the vehicle and the lateral barrier and maintaining or adjusting the running state of the vehicle according to the current speed of the unmanned vehicle, the speed of the barrier and the distance between the unmanned vehicle and the barrier.
Further, in the step five, the vehicle position in the planned obstacle avoidance path is smoothed by adopting a bezier curve of the following form:
Figure BDA0003500609250000055
wherein P(s) is a control point, s is a variable, P (i) represents coordinates of a position point, Bi,n(s) is a bernstein polynomial of degree n, which can be expressed as:
Figure BDA0003500609250000056
in the formula, n represents the order of the Bezier curve, the number of the position points is n +1, and the number of the control points is n-1.
Preferably, a third-order Bessel curve is selected to carry out the smoothing process, so that a complete unmanned vehicle local obstacle avoidance planning path is obtained.
Compared with the prior art, the unmanned vehicle local dynamic obstacle avoidance path planning method provided by the invention at least has the following beneficial effects:
(1) by improving the artificial potential field method, the problems that the target cannot be reached and the local optimum is realized under partial working conditions in the traditional artificial potential field method are solved, so that the unmanned vehicle can overcome local oscillation, and a planned local planning path meeting the obstacle avoidance requirement can be planned;
(2) in the path planning process, the requirements of vehicle kinematics and dynamic performance are added, and the collision safety priority is set, so that the smoothness of the planned path is ensured, and the tracking requirements of a lower-layer vehicle tracker are met;
(3) according to the method, the working conditions of the complex dynamic obstacles in the road environment are analyzed, obstacle avoidance planning is respectively carried out on the lateral dynamic obstacles and the homodromous obstacles, and the two working conditions are combined, so that the planning algorithm meets the obstacle avoidance of the multiple dynamic obstacles, and the applicability and the effectiveness of the planning algorithm are improved.
Drawings
FIG. 1 is a block flow diagram of a method provided by the present invention;
FIG. 2 is a schematic diagram and a solution diagram for a local minimum operating condition according to the present invention;
FIG. 3 is a diagram illustrating a second exemplary embodiment of the present invention for a local minimum operating condition and a solution thereof;
FIG. 4 is a diagram of the present invention for a local minimum operating condition;
FIG. 5 is a schematic diagram and a solution diagram of the present invention for a target unreachable condition;
fig. 6 is a schematic diagram of obstacle avoidance planning of a lateral dynamic obstacle in the solution of the present invention;
fig. 7 is a schematic diagram of the same-direction dynamic obstacle avoidance planning in the solution of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for planning a local dynamic obstacle avoidance path of an unmanned vehicle, which specifically comprises the following steps as shown in fig. 1:
the method comprises the steps that firstly, aiming at a local obstacle avoidance driving environment where an unmanned vehicle is located, a driving target point and an attraction force field and repulsion force field function model of the obstacle to the unmanned vehicle in the environment are respectively established, the attraction force field and repulsion force field function model are used for reflecting the relation between the attraction force of the target point to the unmanned vehicle and the position of the unmanned vehicle, the repulsion force of the obstacle to the unmanned vehicle and the position of the unmanned vehicle, and a driving environment risk field force function model is established through the attraction force and the repulsion force;
step two, when the constructed driving environment risk field potential force function model reaches the local minimum value and the resultant force borne by the vehicle makes the vehicle far away from the target point, determining the corresponding specific stress working condition of the vehicle, which causes that the unmanned vehicle cannot drive to the target point;
step three, aiming at different specific stress working conditions of the vehicle, respectively providing corresponding path planning solutions: temporarily changing the gravitation by using a set virtual target point; canceling the repulsion force of the obstacle which is far away from the target point in the driving direction; improving a gravitational potential field function model according to the number of the obstacles; resolving the repulsion force and canceling the repulsion force component in a certain direction to enable the resultant force of the residual repulsion force component and the attraction force to be in the direction approaching the target point;
step four, setting a dynamic path planning cycle, a safe distance threshold value and a collision risk constraint condition by combining the movement speed and the direction of the obstacle based on the solution provided in the step three so as to plan the obstacle avoidance path and obtain the position and the speed control quantity of the vehicle in a rolling manner in real time;
and fifthly, smoothing the vehicle position in the planned obstacle avoidance path by using the Bessel curve so as to obtain a complete unmanned vehicle local obstacle avoidance planned path.
In a preferred embodiment of the present invention, the building of the driving environment risk potential function model in the first step specifically includes the following processes:
the model of the gravitational potential field function of the target point to the unmanned vehicle is in the following form:
Figure BDA0003500609250000061
wherein eta is the proportional coefficient of the gravitational potential field, d (x, x)goal) The current position coordinate x and the target point position coordinate x of the vehiclegoalA distance vector in the direction from the vehicle to the target point;
deriving the gravitational potential field function to obtain a gravitational function Fatt，FattThe direction of (1) is the direction in which the gravitational potential energy is most rapidly reduced, namely the negative gradient of the gravitational potential field, and the expression is as follows:
Figure BDA0003500609250000071
wherein the content of the first and second substances,
Figure BDA0003500609250000072
representing a gradient function. The above equation shows that the magnitude of the attractive force value is proportional to the distance between the vehicle and the target point, and the farther the vehicle is from the target point, the greater the attractive force is, and conversely, the smaller the attractive force is. The gravitation F can be adjusted by the positive proportionality coefficient eta of the gravitation potential fieldattThe value of (c).
The repulsion potential field function model of the barrier to the unmanned vehicle specifically adopts the following form:
Figure BDA0003500609250000073
wherein k is a repulsive force potential field positive proportionality coefficient, d0The maximum influence range of the repulsion force generated by the obstacle on the vehicle is only when the vehicle moves to d0When the distance between the two is within the range, the repulsive force field generated by the barrier acts on the vehicle, and the acting force of the repulsive force field on the vehicle is larger along with the approach of the distance between the two; when the distance between the two is larger than d0Time, repulsive force field Urep＝0；xobsIs the position coordinate of the obstacle, d (x, x)obs) Is the distance between the vehicle and the obstacle;
the repulsive force function F can be obtained by derivation of the repulsive force potential field functionrep，FrepThe direction of (1) is the direction in which the repulsive force potential energy is most rapidly reduced, namely the negative gradient of the attractive force potential field, and the expression is as follows:
Figure BDA0003500609250000074
the driving environment risk field function is formed by overlapping a target point gravitational potential field function model and an obstacle repulsive force potential field function model, and the expression of the driving environment risk field function is as follows:
Figure BDA0003500609250000075
wherein n is the number of obstacles influencing the repulsion force of the vehicle;
and obtaining the driving environment risk field function derivative to obtain the driving risk field potential force function model, wherein the expression is as follows:
Figure BDA0003500609250000076
in a preferred embodiment of the present invention, the specific stressed conditions of the vehicle that cause the unmanned vehicle to be unable to travel to the target point in the second step specifically include, as shown in fig. 2 to 5:
the working condition I is as follows: the obstacle is located between the vehicle and the target point, as shown in fig. 2(a), the attraction of the target point to the vehicle is equal to the repulsion of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is balanced in stress and cannot move to the target point continuously;
working conditions are as follows: the obstacle is located in the driving direction but far from the target point, as shown in fig. 3, the attraction force of the target point to the vehicle is equal to the repulsion force of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is balanced in stress and cannot move to the target point continuously;
working conditions are as follows: two or more obstacles exist on two sides of the vehicle in the direction towards the target point, as shown in fig. 4(a), the resultant force of the repulsive force of the obstacles and the attractive force of the target point makes the vehicle stressed in balance and cannot move to the target point continuously;
working conditions are as follows: two or more obstacles are present on both sides of the vehicle in the direction toward the target point, and as shown in fig. 5(a), the resultant force of the repulsive force of the obstacles and the attractive force of the target point causes the vehicle to move away from the target point.
In a preferred embodiment of the present invention, the path planning solution provided in step three specifically performs the following operations for the various operating conditions:
providing a solution to the first working condition, namely providing a virtual target point near the target point, as shown in fig. 2(b), so that the vehicle temporarily escapes from a local minimum trap under the attraction of the virtual target point, and then withdrawing the virtual target point at a proper time;
providing a solution II aiming at the working condition II, and directly canceling the repulsive force of the obstacle when the obstacle is positioned in the driving direction but far away from the target point as shown in figure 3, so that the driving risk field potential function escapes from a local minimum trap;
for the third working condition, when the vehicle falls into the local minimum value under the combined action of the multiple obstacles and the target point, as shown in fig. 4(b), firstly, whether the uninfluenced distance between the obstacles meets the lateral safe passing distance S of the vehicle is judged according to the influence range of the obstaclessafeIf the vehicle is satisfied, a solution is provided, and the gravitational potential field function is improved according to the number n of the obstacles in the following way, so that the vehicle continues to advance along the current driving direction until the target point is reached, as shown in fig. 3. The improved potential field function is:
Figure BDA0003500609250000081
if the lateral safe passing distance S is not satisfiedsafeProviding a solution that the potential force function of the driving risk field temporarily escapes from a local minimum trap;
providing a solution for the working condition four, dividing the repulsive force into two vectors of a gravitational direction and a vertical gravitational direction, and judging the magnitude of two repulsive force components as shown in fig. 5 (b):
if the absolute value of the component force of the repulsive force along the direction of the attractive force is smaller than the attractive force, the vertical repulsive force is made to be zero, namely:
Frepy＝0
Fcombination of Chinese herbs＝Fatt+Frepx
If the absolute value of the repulsive force component force in the direction of the attractive force is greater than that of the attractive force, the repulsive force component force in the direction of the attractive force is made to be zero, namely:
Frepx＝0
Fcombination of Chinese herbs＝Fatt+Frepy
Therefore, the influence of the obstacles on the vehicles near the target point can be eliminated by the improved potential field method, so that the vehicles can smoothly reach the target point.
In a preferred embodiment of the present invention, the following process is specifically performed in step four to perform obstacle avoidance path planning:
in order to improve the efficiency and accuracy of the planning algorithm, firstly, the obstacles in the driving local obstacle avoidance environment are primarily screened. Firstly, a virtual straight line from the self vehicle to a target point is established, and the expression is as follows:
LST:y＝kSTx+bST，x∈(xego,xtarget)
wherein the content of the first and second substances,
Figure BDA0003500609250000091
is a straight line LSTThe slope of (a) of (b) is,
Figure BDA0003500609250000092
is a straight line LSTIntercept of (x)ego,yego)、(xtarget,ytarget) Respectively are the position coordinates of the vehicle and the target point;
let the distance d from the obstacle to the vehicleobs-egoFrom obstacle to line LSTA distance of dobs-STWhen d isobs-egoOr dobs-STAnd when any item is smaller than the respective safety threshold, judging that the corresponding barrier can influence the subsequent path planning of the vehicle, and screening the barriers which can influence the path planning so as to carry out subsequent planning calculation. The obstacle pre-screening method ignores the influence of the obstacle with a longer distance on the self expected track, can effectively shorten the time of a path planning algorithm, and greatly improves the efficiency of path planning. dobs-egoOr dobs-STThe expression of (a) is as follows:
Figure BDA0003500609250000093
Figure BDA0003500609250000094
wherein d isobs-egoAnd dobs-STThe corresponding safety thresholds are respectively dobs-ego *And dobs-ST *；
For the lateral dynamic obstacle, as shown in fig. 6, according to the moving direction of the lateral dynamic obstacle, a ray is established which takes the current position coordinate of the obstacle as a starting point and points to the moving direction of the obstacle, and the ray is marked as Lobs，LobsThe time changes along with the change of the coordinates and the movement direction of the obstacle, so that the movement track of the obstacle can be roughly predicted in a short time domain, and whether the obstacle possibly collides with the self vehicle is judged according to the movement intention of the obstacle. Ray LobsThe expression of (a) is as follows:
Figure BDA0003500609250000095
wherein, thetaobsThe included angle between the moving direction of the obstacle and the horizontal axis is a dynamic variable, and the position coordinate (x) of the obstacleobs,yobs) Updating at any moment along with the movement of the barrier;
let ray LobsAnd the straight line LSTHas a virtual intersection point of P (x)P,yP) The virtual intersection point P (x) can be obtained by combining two straight linesP,yP) The position coordinates of (a); the horizontal and vertical coordinate expressions are as follows:
Figure BDA0003500609250000101
respectively calculating the time required for the vehicle and the barrier to reach the virtual collision point P from the current position based on the virtual collision point P; let the current speed of the vehicle be vegoAcceleration of aegoThe current speed of the obstacle is vobsAcceleration of aobsThe time spent by the two respectively driving to the point P in the current motion state is tego-P、tobs-PThe expression is as follows:
Figure BDA0003500609250000102
Figure BDA0003500609250000103
Figure BDA0003500609250000104
Figure BDA0003500609250000105
wherein S isego-PDistance S from the present position of the ego-vehicle to the virtual intersection point Pobs-PThe distance from the current position of the obstacle vehicle to the virtual intersection point P;
comparing the time of arrival of the ego-vehicle and the obstacle vehicle at point P, if tobs-P<tego-PIn this case, it is necessary to determine in advance whether or not the position of the obstacle vehicle affects the self vehicle. When the obstacle vehicle is located in a certain fixed range of the intersection point P, if the distance from the obstacle to the self-vehicle is greater than the safe distance, the obstacle has no influence on the movement of the self-vehicle, and the self-vehicle keeps running in an original state under the action of the attraction of the target point. If the distance from the obstacle to the self-vehicle is smaller than the safe distance, the collision risk is increased, and the self-vehicle needs to decelerate in advance and even brake at the moment so as to avoid the occurrence of collision. When the obstacle vehicle drives away from the point P, the collision risk is reduced, and the self vehicle returns to the original speed and drives to the target point under the action of the gravitational potential field.
If tobs-P＝tego-PSince the movement state of the obstacle is not controllable, it is determined what measure should be taken to avoid the collision based on the kinematics and dynamics constraints of the ego-vehicle. Measures to avoid collisions mainly include braking, deceleration, acceleration, and steering. On the premise of ensuring driving safetyAnd setting a collision decision priority, wherein the priority of the deceleration and braking working conditions is higher than the priority of the acceleration and steering working conditions. According to the arrangement of decision priorities, when collision risks exist, on the premise that the safety distance is guaranteed, the self vehicle firstly takes deceleration measures, and if the collision risks cannot be reduced after deceleration, braking measures are taken to avoid the obstacle vehicle. After the obstacle vehicle gradually gets away from the ego vehicle and the collision risk is reduced, the ego vehicle starts to slowly accelerate until the given vehicle speed is recovered.
If tobs-P>tego-PWhen the self-vehicle approaches the virtual intersection point P, if the distance from the obstacle to the self-vehicle is smaller than the safe distance, the collision risk is increased, and at the moment, the self-vehicle deviates from the initial track under the action of the repulsive force of the obstacle, namely, the self-vehicle deviates from the linear track to avoid the dynamic obstacle; secondly, after the self-vehicle senses that the collision risk is increased, the self-vehicle can increase the speed of the vehicle while avoiding, so that the self-vehicle can be quickly separated from the influence range of the barrier, and the safety of path planning is further guaranteed. When the self-vehicle approaches the virtual intersection point P, if the distance from the obstacle to the self-vehicle is greater than the safe distance, no collision risk exists, and the self-vehicle can keep running in the original state under the action of the potential field until reaching the target point.
For the equidirectional dynamic barrier, as shown in fig. 7, the unmanned vehicle needs to timely judge the driving state of the front vehicle and make a decision: car following, braking or steering.
Following the vehicle working condition: the unmanned vehicle and the front vehicle are moderate in running speed, the distance between the unmanned vehicle and the front vehicle meets the requirement of safe distance, and the unmanned vehicle keeps the current speed to run with the vehicle.
Braking condition: the speed of the unmanned vehicle is greater than that of the vehicle in front, the distance between vehicles is smaller than the safe distance, meanwhile, obstacles exist on the side of the unmanned vehicle and the steering requirement is not met, and then the unmanned vehicle performs braking operation.
And (3) steering working condition: the speed of the unmanned vehicle is greater than that of the vehicle in front, the inter-vehicle distance meets the lane changing distance, and meanwhile, the road environment on the side of the unmanned vehicle meets the lane changing condition, so that the unmanned vehicle performs the lane changing operation.
And (3) selecting a corresponding planning control algorithm in real time by analyzing the motion state of the dynamic barrier, so as to realize real-time barrier avoidance planning.
In a preferred embodiment of the present invention, in step five, the vehicle position in the planned obstacle avoidance path is smoothed by using a bezier curve of the following form:
Figure BDA0003500609250000111
wherein P(s) is a control point, s is a variable, P (i) represents coordinates of a position point, Bi,n(s) is a bernstein polynomial of degree n, which can be expressed as:
Figure BDA0003500609250000112
in the formula, n represents the order of the Bezier curve, the number of the position points is n +1, and the number of the control points is n-1.
In this embodiment, a third-order bezier curve is preferably selected to perform the smoothing process, so as to obtain a complete unmanned vehicle local obstacle avoidance planning path.
It should be understood that, the sequence numbers of the steps in the embodiments of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. A method for planning a local dynamic obstacle avoidance path of an unmanned vehicle is characterized by comprising the following steps: the method specifically comprises the following steps:
the method comprises the steps that firstly, aiming at a local obstacle avoidance driving environment where an unmanned vehicle is located, a driving target point and an attraction force field and repulsion force field function model of the obstacle to the unmanned vehicle in the environment are respectively established, the attraction force field and repulsion force field function model are used for reflecting the relation between the attraction force of the target point to the unmanned vehicle and the position of the unmanned vehicle, the repulsion force of the obstacle to the unmanned vehicle and the position of the unmanned vehicle, and a driving environment risk field force function model is established through the attraction force and the repulsion force;
step two, when the constructed driving environment risk field potential force function model reaches the local minimum value and the resultant force borne by the vehicle makes the vehicle far away from the target point, determining the corresponding specific stress working condition of the vehicle, which causes that the unmanned vehicle cannot drive to the target point;
step three, aiming at different specific stress working conditions of the vehicle, respectively providing corresponding path planning solutions: temporarily changing the gravitation by using a set virtual target point; canceling the repulsion force of the obstacle which is far away from the target point in the driving direction; improving a gravitational potential field function model according to the number of the obstacles; resolving the repulsion force and canceling the repulsion force component in a certain direction to enable the resultant force of the residual repulsion force component and the attraction force to be in the direction approaching the target point;
step four, setting a dynamic path planning cycle, a safe distance threshold value and a collision risk constraint condition by combining the movement speed and the direction of the obstacle based on the solution provided in the step three so as to plan the obstacle avoidance path and obtain the position and the speed control quantity of the vehicle in a rolling manner in real time;
and fifthly, smoothing the vehicle position in the planned obstacle avoidance path by using the Bessel curve so as to obtain a complete unmanned vehicle local obstacle avoidance planned path.
2. The method of claim 1, wherein: establishing the driving environment risk potential function model in the first step, which specifically comprises the following processes:
the model of the gravitational potential field function of the target point to the unmanned vehicle is in the following form:
Figure FDA0003500609240000011
wherein eta is the direct proportional coefficient of the gravitational potential field, d (x, x)goal) The current position coordinate x and the target point position coordinate x of the vehiclegoalA distance vector in the direction from the vehicle to the target point;
deriving the gravitational potential field function to obtain a gravitational function FattThe expression is as follows:
Figure FDA0003500609240000012
wherein ∑ represents a gradient function;
the repulsion potential field function model of the barrier to the unmanned vehicle specifically adopts the following form:
Figure FDA0003500609240000013
wherein k is a repulsive potential field positive proportionality coefficient, d0Maximum influence range of the repulsive force of the obstacle to the vehicle, xobsIs the position coordinate of the obstacle, d (x, x)obs) Is the distance between the vehicle and the obstacle;
the repulsive force function F can be obtained by derivation of the repulsive force potential field functionrepThe expression is as follows:
Figure FDA0003500609240000021
the driving environment risk field function is formed by overlapping a target point gravitational potential field function model and an obstacle repulsive force potential field function model, and the expression of the driving environment risk field function is as follows:
Figure FDA0003500609240000022
wherein n is the number of obstacles influencing the repulsion force of the vehicle;
and obtaining the driving environment risk field function derivative to obtain the driving risk field potential force function model, wherein the expression is as follows:
Figure FDA0003500609240000023
3. the method of claim 1, wherein: further, the specific stress condition of the vehicle, which causes the unmanned vehicle not to be able to travel to the target point in the step two, specifically includes:
the working condition I is as follows: the obstacle is positioned between the vehicle and the target point, the attraction of the target point to the vehicle is equal to the repulsion of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is balanced in stress and cannot move to the target point continuously;
working conditions are as follows: the obstacle is positioned in the driving direction but far away from the target point, the attraction force of the target point to the vehicle is equal to the repulsion force of the obstacle to the vehicle in magnitude and opposite in direction, so that the vehicle is stressed in balance and cannot move to the target point continuously;
working conditions are as follows: two or more obstacles are arranged on two sides of the vehicle in the direction towards the target point, and the resultant force of the repulsion force of the obstacles and the attraction force of the target point enables the vehicle to be stressed in balance and can not move to the target point continuously;
working conditions are as follows: more than two obstacles are arranged on two sides of the vehicle in the direction towards the target point, and the resultant force of the repulsive force of the obstacles and the attractive force of the target point enables the vehicle to be far away from the target point.
4. The method of claim 3, wherein: the path planning solution provided in step three specifically performs the following operations for the various working conditions:
providing a solution to the first working condition, namely providing a virtual target point near the target point, so that the vehicle can temporarily escape from a local minimum trap under the attraction of the virtual target point, and withdrawing the virtual target point at a proper time;
providing a solution II aiming at the working condition II, and directly canceling the repulsive force of the obstacle when the obstacle is positioned in the driving direction but far away from the target point, so that the driving risk field potential function escapes from a local minimum trap;
aiming at the third working condition, firstly, judging whether the uninfluenced space between the obstacles meets the lateral safe passing distance S of the vehicle according to the influence range of the obstaclessafeIf the vehicle is satisfied, a solution is provided, and the gravitational potential field function is improved according to the number n of obstacles in the following way to improve the gravitational magnitude:
Figure FDA0003500609240000031
eta is the direct proportional coefficient of gravitational potential field, d (x, x)goal) The current position coordinate x and the target point position coordinate x of the vehiclegoalA distance vector between the two points, wherein the direction of the distance vector is from the vehicle to a target point, and n is the number of obstacles which have the influence of repulsion on the vehicle;
if the lateral safe passing distance S is not satisfiedsafeProviding a solution that the potential force function of the driving risk field temporarily escapes from a local minimum trap;
and a solution is provided for the working condition four, the repulsive force is decomposed, the repulsive force component in a certain direction is cancelled, and the resultant force of the residual repulsive force component and the attractive force is changed into a direction approaching the target point.
5. The method of claim 1, wherein: in the fourth step, the following processes are specifically executed to perform obstacle avoidance path planning:
firstly, a virtual straight line from the self vehicle to a target point is established, and the expression is as follows:
LST:y＝kSTx+bST，x∈(xego,xtarget)
wherein the content of the first and second substances,
Figure FDA0003500609240000032
is a straight line LSTThe slope of (a) of (b) is,
Figure FDA0003500609240000033
is a straight line LSTIntercept of (x)ego,yego)、(xtarget,ytarget) Respectively are the position coordinates of the vehicle and the target point;
let the distance d from the obstacle to the vehicleobs-egoFrom obstacle to line LSTA distance of dobs-ST，dobs-egoOr dobs-STThe expression of (a) is as follows:
Figure FDA0003500609240000034
Figure FDA0003500609240000035
wherein d isobs-egoAnd dobs-STThe corresponding safety thresholds are respectively dobs-ego *And dobs-ST *；
Aiming at the lateral dynamic barrier, establishing a ray which takes the current position coordinate of the barrier as a starting point and points to the motion direction of the barrier and is marked as L according to the motion direction of the lateral dynamic barrierobsThe expression is as follows:
Lobs:
Figure FDA0003500609240000036
wherein, thetaobsIs the included angle between the moving direction of the barrier and the horizontal axis;
let ray LobsAnd the straight line LSTHas a virtual intersection point of P (x)P,yP) The virtual intersection point P (x) can be obtained by combining two straight linesP,yP) The position coordinates of (a); the horizontal and vertical coordinate expressions are as follows:
Figure FDA0003500609240000041
respectively calculating the time required for the vehicle and the barrier to reach the virtual collision point P from the current position based on the virtual collision point P; let the current speed of the vehicle be vegoAcceleration of aegoThe current speed of the obstacle is vobsAcceleration of aobsThe time spent by the two respectively driving to the point P in the current motion state is tego-P、tobs-PThe expression is as follows:
Figure FDA0003500609240000042
Figure FDA0003500609240000043
Figure FDA0003500609240000044
Figure FDA0003500609240000045
wherein S isego-PDistance S from the present position of the ego-vehicle to the virtual intersection point Pobs-PThe distance from the current position of the obstacle vehicle to the virtual intersection point P;
by comparing tobs-PAnd tego-PDetermining the risk of collision between the vehicle and the lateral obstacle and maintaining or adjusting the running state of the vehicle by combining the distance between the vehicle and the obstacle;
and aiming at the equidirectional dynamic barrier, judging the risk of collision between the vehicle and the lateral barrier and maintaining or adjusting the running state of the vehicle according to the current speed of the unmanned vehicle, the speed of the barrier and the distance between the unmanned vehicle and the barrier.
6. The method of claim 1, wherein: and step five, smoothing the vehicle position in the planned obstacle avoidance path by adopting a Bessel curve in the following form:
Figure FDA0003500609240000046
wherein P(s) is a control point, s is a variable, P (i) represents coordinates of a position point, Bi,n(s) is a bernstein polynomial of degree n, which can be expressed as:
Figure FDA0003500609240000047
in the formula, n represents the order of the Bezier curve, the number of the position points is n +1, and the number of the control points is n-1.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115167470A (en) * 2022-08-18 2022-10-11 深圳市欧铠智能机器人股份有限公司 Automatic cargo conveying distribution system and method for carrying robot
CN115328152A (en) * 2022-09-05 2022-11-11 北京理工大学 Unmanned vehicle local real-time obstacle avoidance path planning method based on improved artificial potential field method
CN115469665A (en) * 2022-09-16 2022-12-13 广东工业大学 Intelligent wheelchair target tracking control method and system suitable for dynamic environment
CN115480591A (en) * 2022-10-20 2022-12-16 广东电网有限责任公司云浮供电局 Safety obstacle avoidance method for unmanned aerial vehicle for power distribution network equipment environment inspection
CN116202550A (en) * 2023-05-06 2023-06-02 华东交通大学 Automobile path planning method integrating improved potential field and dynamic window
CN116339347A (en) * 2023-04-24 2023-06-27 广东工业大学 Unmanned vehicle running path planning method, device and equipment

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010076488A (en) * 2008-09-24 2010-04-08 Equos Research Co Ltd Vehicle
KR20150086065A (en) * 2014-01-17 2015-07-27 전남대학교산학협력단 System and method for path planning for autonomous navigation of driverless ground vehicle
CN106843235A (en) * 2017-03-31 2017-06-13 深圳市靖洲科技有限公司 It is a kind of towards the Artificial Potential Field path planning without person bicycle
CN110567478A (en) * 2019-09-30 2019-12-13 广西科技大学 unmanned vehicle path planning method based on artificial potential field method
CN110908373A (en) * 2019-11-11 2020-03-24 南京航空航天大学 Intelligent vehicle track planning method based on improved artificial potential field
CN111665846A (en) * 2020-06-24 2020-09-15 哈尔滨工程大学 Water surface unmanned ship path planning method based on rapid scanning method
CN112099493A (en) * 2020-08-31 2020-12-18 西安交通大学 Autonomous mobile robot trajectory planning method, system and equipment
CN112577491A (en) * 2020-12-14 2021-03-30 上海应用技术大学 Robot path planning method based on improved artificial potential field method
CN113190004A (en) * 2021-05-07 2021-07-30 河南科技大学 Unmanned vehicle collision avoidance path planning method based on algorithm fusion
CN113467472A (en) * 2021-07-27 2021-10-01 山东科技大学 Robot path planning method in complex environment
CN113515125A (en) * 2021-07-05 2021-10-19 中国石油大学(华东) Unmanned vehicle full-working-condition obstacle avoidance control method and performance evaluation method
CN113720346A (en) * 2021-09-02 2021-11-30 重庆邮电大学 Vehicle path planning method and system based on potential energy field and hidden Markov model
CN113848914A (en) * 2021-09-28 2021-12-28 福州大学 Collision coefficient artificial potential field method local path planning method in dynamic environment

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010076488A (en) * 2008-09-24 2010-04-08 Equos Research Co Ltd Vehicle
KR20150086065A (en) * 2014-01-17 2015-07-27 전남대학교산학협력단 System and method for path planning for autonomous navigation of driverless ground vehicle
CN106843235A (en) * 2017-03-31 2017-06-13 深圳市靖洲科技有限公司 It is a kind of towards the Artificial Potential Field path planning without person bicycle
CN110567478A (en) * 2019-09-30 2019-12-13 广西科技大学 unmanned vehicle path planning method based on artificial potential field method
CN110908373A (en) * 2019-11-11 2020-03-24 南京航空航天大学 Intelligent vehicle track planning method based on improved artificial potential field
CN111665846A (en) * 2020-06-24 2020-09-15 哈尔滨工程大学 Water surface unmanned ship path planning method based on rapid scanning method
CN112099493A (en) * 2020-08-31 2020-12-18 西安交通大学 Autonomous mobile robot trajectory planning method, system and equipment
CN112577491A (en) * 2020-12-14 2021-03-30 上海应用技术大学 Robot path planning method based on improved artificial potential field method
CN113190004A (en) * 2021-05-07 2021-07-30 河南科技大学 Unmanned vehicle collision avoidance path planning method based on algorithm fusion
CN113515125A (en) * 2021-07-05 2021-10-19 中国石油大学(华东) Unmanned vehicle full-working-condition obstacle avoidance control method and performance evaluation method
CN113467472A (en) * 2021-07-27 2021-10-01 山东科技大学 Robot path planning method in complex environment
CN113720346A (en) * 2021-09-02 2021-11-30 重庆邮电大学 Vehicle path planning method and system based on potential energy field and hidden Markov model
CN113848914A (en) * 2021-09-28 2021-12-28 福州大学 Collision coefficient artificial potential field method local path planning method in dynamic environment

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
LUIS GARROTE 等: "An RRT based navigation approach for mobile robot sand automated vehicles", 2014 12TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) *
熊博: "基于模糊神经网络的移动机器人路径规划技术研究", 中国优秀博硕士学位论文全文数据库(硕士)信息科技辑, no. 01 *
王迪 等: "改进人工势场法的移动机器人局部路径规划", 山东理工大学学报（自然科学版）, vol. 35, no. 3 *
陈伟华 等: "基于双重A* 算法的移动机器人动态环境路径规划", 组合机床与自动化加工技术, no. 4 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115167470A (en) * 2022-08-18 2022-10-11 深圳市欧铠智能机器人股份有限公司 Automatic cargo conveying distribution system and method for carrying robot
CN115167470B (en) * 2022-08-18 2023-11-28 深圳市欧铠智能机器人股份有限公司 Automatic cargo delivery distribution system and method for delivery robot
CN115328152A (en) * 2022-09-05 2022-11-11 北京理工大学 Unmanned vehicle local real-time obstacle avoidance path planning method based on improved artificial potential field method
CN115469665A (en) * 2022-09-16 2022-12-13 广东工业大学 Intelligent wheelchair target tracking control method and system suitable for dynamic environment
CN115469665B (en) * 2022-09-16 2023-07-04 广东工业大学 Intelligent wheelchair target tracking control method and system suitable for dynamic environment
CN115480591A (en) * 2022-10-20 2022-12-16 广东电网有限责任公司云浮供电局 Safety obstacle avoidance method for unmanned aerial vehicle for power distribution network equipment environment inspection
CN115480591B (en) * 2022-10-20 2023-09-12 广东电网有限责任公司云浮供电局 Safety obstacle avoidance method for unmanned aerial vehicle in power distribution network equipment environment inspection
CN116339347A (en) * 2023-04-24 2023-06-27 广东工业大学 Unmanned vehicle running path planning method, device and equipment
CN116339347B (en) * 2023-04-24 2023-10-31 广东工业大学 Unmanned vehicle running path planning method, device and equipment
CN116202550A (en) * 2023-05-06 2023-06-02 华东交通大学 Automobile path planning method integrating improved potential field and dynamic window

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