A path planning method for obstacle avoidance on curved roads for unmanned vehicles based on artificial potential field

Abstract

The present invention relates to a method for planning an obstacle avoidance path for an unmanned vehicle on a curved road based on an artificial potential field, and belongs to the technical field of unmanned vehicle driving. The existing artificial potential field method has a local optimal problem when planning an obstacle avoidance path for an unmanned vehicle. The present invention, based on the construction of an artificial resultant force potential field, determines whether the main vehicle is trapped in the local optimal state according to the resultant force, solves the local optimal problem by means of a circle obstacle avoidance 1, solves the problem that the planned next trajectory point is within the expansion range of the obstacle by means of a circle obstacle avoidance 2, and in the process of circle obstacle avoidance, adjusts the speed of the main vehicle according to the resultant force, thereby ensuring the safety of the vehicle.

Description

Technical Field

[0001] The invention relates to a curve obstacle avoidance path planning method for an unmanned vehicle based on an artificial potential field, and belongs to the technical field of unmanned vehicle driving. Background Art

[0002] Path planning, as a core technology in the field of unmanned vehicle research, refers to finding a collision-free path from the starting point to the target point in a dynamic environment. It can be specifically described as follows: given the vehicle's geometric shape and dynamic model, the environmental information, an initial state and a target state set, a feasible trajectory that is collision-free and satisfies the vehicle's dynamic and geometric constraints is calculated, and the planning results are output to the motion control layer.

[0003] The artificial potential field method was initially applied to the path planning of robots, and there are problems of unreachable targets and local optimality. There are two main types of solutions to this problem in the prior art. One is to introduce the distance of the target point into the repulsive field to escape the local optimality. The other is to add additional virtual forces to break the equilibrium state and force the unmanned vehicle to jump out of the local optimality when trapped in the local optimality. However, when the artificial potential field is applied to the obstacle avoidance of unmanned vehicles, it is necessary to consider not only the limitations of the algorithm, but also the size of the obstacle and the kinematic and dynamic constraints of the unmanned vehicle. For example, a Chinese patent document with publication number CN112344943A discloses an intelligent vehicle path planning method that improves the artificial potential field algorithm. The moving point that escapes the local optimal problem is determined by probability estimation, and whether the local minimum point can be escaped is judged based on the potential field size comparison of the minimum point. Summary of the invention

[0004] The purpose of the present invention is to provide a method for planning an unmanned vehicle's obstacle avoidance path on a curve based on an artificial potential field, so as to solve the local optimal problem existing in planning an unmanned vehicle's obstacle avoidance path on a curve using the artificial potential field method.

[0005] In order to achieve the above object, the present invention provides a method for unmanned vehicle curve obstacle avoidance path planning based on artificial potential field, comprising the following steps:

[0006] S1. Obtaining the main vehicle information and obstacle information, and determining the target point position; the main vehicle information includes the main vehicle position and the main vehicle speed, and the obstacle information includes the obstacle position;

[0007] S2, establishing a follow-up rectangular coordinate system that follows the movement of the main vehicle, and in the follow-up rectangular coordinate system, establishing a target point gravitational potential field for characterizing the influence of the target point position on the movement of the main vehicle according to the position of the main vehicle and the position of the target point, and establishing an obstacle repulsive potential field for characterizing the influence of the obstacle on the movement of the main vehicle according to the position of the main vehicle, the position of the obstacle and the position of the target point;

[0008] S3, calculating the resultant force of the main vehicle in the composite field composed of the gravitational potential field of the target point and the repulsive potential field of the obstacle;

[0009] S4, S4, if the resultant force is not 0, a first reference circle is made with the position of the main vehicle as the center and the step length as the radius, and the main vehicle is taken as the starting point, and the main vehicle is directed along the resultant force direction to a point on the first reference circle as the next trajectory point p1 of the main vehicle movement, and an obstacle avoidance path of the main vehicle is determined according to the trajectory point p1, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid obstacles;

[0010] If the resultant force is 0, a second reference circle is made with the position of the main vehicle as the center and the step length as the radius, and a third reference circle is made with the obstacle as the center and the distance between the main vehicle and the obstacle as the radius. The intersection of the second reference circle and the third reference circle is taken as the next trajectory point p2 of the main vehicle. The obstacle avoidance path of the main vehicle is determined according to the trajectory point p2, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid the obstacle.

[0011] The existing artificial potential field method has a local optimal problem caused by the potential field force being 0 when planning the obstacle avoidance path of the unmanned vehicle. After judging that the resultant potential field is 0, the present invention activates the circle obstacle avoidance 1 to make the main vehicle escape from the local optimal. Circle obstacle avoidance 1 means: take the obstacle as the center of the circle and the distance between the main vehicle and the obstacle as the radius to draw a circle, determine the point on the circle with a set step length away from the main vehicle as the next trajectory point, and the main vehicle travels along the arc between the current position and the trajectory point. The circle obstacle avoidance 1 can solve the local optimal problem and improve the reliability of the obstacle avoidance path planning of the unmanned vehicle.

[0012] Furthermore, in the above method, in step S4, when the resultant force is not 0, if the trajectory point p1 is within the expansion range where the obstacle is located, the point of the obstacle that is located in the direction pointing to the trajectory point p1 and on the third reference circle is taken as the next trajectory point p3 of the main vehicle's movement, and the obstacle avoidance path of the main vehicle is determined according to the trajectory point p3, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid the obstacle; the size of the obstacle is enlarged according to the set ratio to obtain the expansion range.

[0013] Taking into account the uncertainty of obstacle movement, the expansion range is obtained by enlarging the obstacle size. If the trajectory point p1 planned by the artificial potential field method is within the expansion range, the obstacle may affect the obstacle avoidance process of the main vehicle. By activating the circle obstacle avoidance 2, the trajectory point of the main vehicle obstacle avoidance is replanned. Circle obstacle avoidance 2 means: take the position of the obstacle as the center of the circle and the distance between the main vehicle and the obstacle as the radius to draw a circle, and take the intersection of the straight line passing through the center of the circle and the trajectory point p1 and the circle as the new trajectory point p3. The main vehicle travels along the arc between the current position and the trajectory point p3. Circle obstacle avoidance 2 can improve the safety of the unmanned vehicle when avoiding obstacles.

[0014] Furthermore, in the above method, the obstacle information also includes obstacle speed. In step S2, an obstacle speed repulsion potential field for characterizing the influence of the obstacle speed on the motion of the main vehicle is established in the follow-up rectangular coordinate system according to the obstacle speed information, and in step S3, the resultant force of the main vehicle in the composite field composed of the target point gravitational potential field, the obstacle repulsion potential field and the obstacle speed repulsion potential field is calculated.

[0015] Furthermore, in the above method, the obstacle velocity repulsive force potential field U rev (X) is expressed by the following formula:

[0016]

[0017] In the formula, k v is the proportional gain coefficient, v-v0 is the relative speed between the main vehicle and the obstacle, v is the current speed of the main vehicle, v0 is the current speed of the obstacle, the direction of the obstacle speed repulsion is opposite to the relative speed, α is the angle between the direction of the relative speed and the line connecting the main vehicle position and the obstacle position, ρ ob is the distance between the main vehicle and the obstacle, and A is the longitudinal impact distance of the obstacle.

[0018] Furthermore, in the above method, road boundary information is also obtained. In step S2, a road boundary repulsion potential field is established in the follow-up rectangular coordinate system according to the road boundary information to characterize the influence of the road boundary on the movement of the main vehicle, and in step S3, the resultant force of the main vehicle in the composite field composed of the target point gravitational potential field, the obstacle repulsion potential field, the obstacle velocity repulsion potential field and the road boundary repulsion potential field is calculated.

[0019] Taking into account the influence of the road boundary on the main vehicle during the obstacle avoidance process, the road boundary potential field is set according to the road boundary information, and then a composite field is constructed, which can improve the reliability of the main vehicle's obstacle avoidance path planning.

[0020] Furthermore, in the above method, the road boundary repulsive potential field U road (X) is expressed by the following formula:

[0021]

[0022] In the formula, k road is the road boundary repulsion coefficient, ||r i -r l || is the vertical distance from the main vehicle to the road boundary, r i is the horizontal coordinate of the position of the main vehicle in the follower rectangular coordinate system, r l is the ordinate of the position of the main vehicle in the follow-up rectangular coordinate system, W is the body width of the main vehicle, and k0 is the road boundary influence distance.

[0023] Furthermore, in the above method, the sensor blind spot boundary information is also obtained. In step S2, in the follow-up rectangular coordinate system, a sensor blind spot repulsion potential field is established according to the sensor blind spot boundary to characterize the influence of the sensor acquisition blind spot on the movement of the main vehicle, and in step S3, the resultant force of the main vehicle in the composite field composed of the target point gravitational potential field, the obstacle repulsion potential field, the obstacle speed repulsion potential field, the road boundary repulsion potential field and the sensor blind spot repulsion potential field is calculated.

[0024] Considering that there are blind spots when the sensor collects lane environment information during the obstacle avoidance process of the main vehicle, and there may be other obstacles in the sensor blind spot, setting the sensor blind spot repulsive potential field and then constructing a composite field can improve the reliability of the main vehicle's obstacle avoidance path planning.

[0025] Furthermore, in the above method, the sensor blind area repulsive potential field U r-blind (X) is expressed by the following formula:

[0026]

[0027] In the formula, K r-blind is the blind zone repulsion coefficient of the front sensor, ||r i -r b || is the vertical distance between the sensor blind spot boundary and the main vehicle, v is the current speed of the main vehicle, and its direction is the same as the direction of the obstacle repulsion, t hr is the threat level of dangerous obstacles in the sensor blind area.

[0028] Furthermore, in the above method, a target speed of the main vehicle for obstacle avoidance is determined according to the combined force, and the target speed is used as a control target for obstacle avoidance of the main vehicle, so that the main vehicle is controlled to travel along an obstacle avoidance path to avoid obstacles.

[0029] During the obstacle avoidance process of the main vehicle, the speed of the main vehicle is controlled according to the combined force acting on the main vehicle, so that the speed is more consistent with the planned obstacle avoidance path, thereby improving safety during obstacle avoidance.

[0030] Furthermore, in the above method, before the main vehicle avoids an obstacle, if the distance between the main vehicle and the obstacle is less than a set safety distance, the main vehicle is controlled to decelerate.

[0031] Before obstacle avoidance driving, the main vehicle speed is controlled according to the distance between the main vehicle and the obstacle to avoid collision between the main vehicle and the obstacle. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field in an embodiment of the method of the present invention;

[0033] Figure 2A schematic diagram of a follow-up rectangular coordinate system in an embodiment of the method of the present invention;

[0034] Figure 3 Schematic diagram of the blind area of ​​sensor detection in the embodiment of the method of the present invention;

[0035] Figure 4 This is a schematic diagram of the force on the main vehicle in the embodiment of the method of the present invention;

[0036] Figure 5 Schematic diagram of a circle obstacle avoidance method 1 in an embodiment of the method of the present invention;

[0037] Figure 6 Schematic diagram of the circle obstacle avoidance method 2 in the method embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0039] Method Example:

[0040] The present invention provides a method for unmanned vehicle to avoid obstacles on curved roads based on artificial potential field. Figure 1 As shown, the following steps are included:

[0041] S1. Obtain information about the driving environment, obstacles, and main vehicle of the main vehicle (i.e., unmanned vehicle) through visual sensors, on-board equipment, vehicle-to-infrastructure communication technology (V2I), and differential GPS / INS navigation system.

[0042] The information of the driving environment of the main vehicle is the road environment information obtained in real time through roadside sensing equipment and V2I technology, including the width of the lane lines on the road where the main vehicle is located, the number of lane lines, and the distance between the main vehicle and the lane lines on both sides. Through the cameras installed around the main vehicle body, the laser radar on the top of the vehicle body and the millimeter-wave radar on the bumper, the obstacle information around the driving environment of the main vehicle is collected, and the collected obstacle information is classified and fused. Obstacles are usually other vehicles on the road. Obstacle information includes the number of obstacles, obstacle speed and obstacle location. The main vehicle information includes the main vehicle location and the current speed of the main vehicle. Since there are acquisition blind spots, that is, sensor blind spots, in devices such as cameras, laser radars and millimeter-wave radars, the main vehicle information also includes the distance between the main vehicle and the boundary of the sensor blind spot.

[0043] S2. Determine whether the main vehicle needs to avoid obstacles by using the safety distance. If the distance between the main vehicle and the obstacle is less than the safety distance, it is considered that the main vehicle is not able to avoid the obstacle, and the main vehicle is controlled to decelerate. When the speed of the main vehicle after deceleration satisfies the formula, it is considered that the distance between the main vehicle and the obstacle reaches the safety distance, and it is determined that the main vehicle needs to avoid the obstacle, and step S3 is executed.

[0044]

[0045] In the formula, X0 is the safety distance, v P (0) is the initial speed of the vehicle before deceleration, v Q (0) Initial velocity of the obstacle, a P (τ) is the acceleration of the main vehicle during deceleration, a Q (τ) is the acceleration of the obstacle, and t is the time from the detection of the obstacle to the collision between the host vehicle and the obstacle.

[0046] S3. Establish a follow-up rectangular coordinate system, and establish the target point gravitational potential field, obstacle repulsive potential field, obstacle velocity repulsive potential field, road boundary repulsive potential field and sensor blind area repulsive potential field in the follow-up rectangular coordinate system.

[0047] S3.1, if Figure 2 As shown in the figure, a new rectangular coordinate system x'o'y' is established as a follow-up rectangular coordinate system based on the projection point of the rear axle center of the main vehicle on the ground and the outer lane boundary line. The follow-up rectangular coordinate system moves with the movement of the main vehicle. Through the following formula, any position (x, y) in the ground coordinate system xoy can be transformed to the follow-up rectangular coordinate system to obtain the position (x', y') in the follow-up rectangular coordinate system.

[0048]

[0049]

[0050]

[0051] Where θ is the angle between the x-axis and the x′-axis, (x start ,y start ) is the starting point of the lane line at the outer edge of the left lane.

[0052] S3.2. Establish the obstacle repulsion potential field. rep (X) is expressed by the following formula:

[0053]

[0054] Where η is the gain of the repulsive field function, ρ ob is the distance between the main vehicle and the obstacle, ρ g is the distance between the main vehicle and the target point, n is an arbitrary real number greater than zero, and A is the longitudinal impact distance of the obstacle.

[0055] In order to improve the safety of obstacle avoidance for unmanned vehicles, the value of η is further designed considering the influence of the size, mass and shape of obstacles, and η is designed as:

[0056]

[0057] Where M is the mass of the obstacle. Here, the obstacle is other vehicles, so M is the mass of the obstacle vehicle, e is the natural logarithm, p is the type of the front vehicle, and G is the adjustment base, which is used to prevent the gain of the repulsive field function from being too large.

[0058] Obstacle repulsion F rep (X) is expressed by the following formula:

[0059]

[0060] Where a0 is the unit vector of the main vehicle pointing to the obstacle, p ob is the distance between the main vehicle and the obstacle, A is the longitudinal impact distance of the obstacle, B is the lateral impact distance of the obstacle, and (x1, y1) is the position coordinate of the obstacle in the follow-up rectangular coordinate system. rep1 and F rep2 It is expressed by the following formula:

[0061]

[0062] Where η is the gain of the repulsive field function, p ob is the distance between the main vehicle and the obstacle, ρ g is the distance between the main vehicle and the target point, n is an arbitrary real number greater than zero, and A is the longitudinal impact distance of the obstacle.

[0063] The longitudinal impact distance B of the obstacle is expressed by the following formula:

[0064] B=β(Hc) / 2

[0065] Where β is the safety factor, H is the distance between the geometric centerline of the lane where the unmanned vehicle is located and the geometric centerline of the lane where the obstacle is located, and c is the width of the obstacle vehicle.

[0066] When the unmanned vehicle is driving in a two-way lane, the vehicles in the adjacent lanes do not pose a driving hazard to the unmanned vehicle. At this time, the repulsion of the vehicles in the adjacent lanes is ignored, and only the repulsion of the same-direction lanes is considered.

[0067] S3.3. Establish the gravitational potential field of the target point. att (X) is expressed by the following formula:

[0068]

[0069] In the formula, k att is the gravitational field function gain, ρ g is the distance between the main vehicle and the target point, ρ0 is the gravitational threshold of the target point, and U′att is the maximum gravitational potential field at the target point.

[0070] In order to improve the safety of unmanned vehicle obstacle avoidance, the method of designing segmented gravity is adopted. When the distance between the main vehicle and the target point is greater than the target point gravity threshold p0, the gravitational force of the target point on the main vehicle is the maximum gravitational force F′ of the target point. att , the gravitational direction changes with the direction of the main vehicle's movement; when the distance between the main vehicle and the target point is less than the target point's gravitational action threshold p0, the target point's gravitational force on the main vehicle changes with ρ g The direction changes from the main vehicle to the target point. The target point gravity F att (X) is expressed by the following formula:

[0071]

[0072] In the formula, k att is the gravitational field function gain, ρ g is the distance between the main vehicle and the target point, p0 is the gravitational threshold of the target point, and F′ att is the maximum gravitational force at the target point.

[0073] S3.4. Establish road boundary repulsion potential field. The lane information is collected and processed by visual sensors, including image preprocessing, image segmentation and edge detection, as well as lane line recognition, to obtain road boundary information, and establish a road boundary potential field based on the main vehicle position and road boundary information. Road boundary potential field U road (X) is expressed by the following formula:

[0074]

[0075] In the formula, k road is the road boundary repulsion coefficient, ||r i -r l || is the vertical distance from the main vehicle to the road boundary, r i is the horizontal coordinate of the center of mass of the main vehicle, r l is the ordinate of the center of mass of the main vehicle, W is the width of the main vehicle, and k0 is the road boundary influence distance.

[0076] Road boundary repulsion F road (X) is expressed by the following formula:

[0077]

[0078] S3.5. Establish the repulsive potential field of the sensor blind area. Establish the repulsive potential field of the sensor blind area according to whether there are obstacles in the detected blind area. Figure 3 As shown in the figure, when there is a dangerous obstacle in the blind spot, the threat level of the potential dangerous obstacle is designed to be thr When t is 1, the repulsive potential field model of the sensor blind area is added; when there is no dangerous obstacle in the blind area, the threat level of the potential dangerous obstacle is designed. hr If it is 0, this repulsive potential field model is ignored.

[0079] Sensor blind zone repulsive potential field model U r-blind (X) is expressed by the following formula:

[0080]

[0081] Sensor blind zone repulsion F r-blind (X) is expressed by the following formula:

[0082]

[0083] In the formula, K r-blind is the blind zone repulsion coefficient of the front sensor, ||r i -r b || is the vertical distance between the sensor blind spot boundary and the main vehicle, v is the current speed of the main vehicle, and its direction is the same as the direction of the obstacle repulsion. If the main vehicle does not decelerate in step S2, the current speed of the main vehicle v is v P (0), if the main vehicle decelerates in step S2, then according to the initial speed v before deceleration of the main vehicle P (0), and the acceleration a of the main vehicle during deceleration P (τ), and calculate the current speed v of the host vehicle.

[0084] S3.6. Establish the obstacle velocity repulsion potential field. rev (X) is expressed by the following formula:

[0085]

[0086] Obstacle velocity repulsion F rev (X) is expressed by the following formula:

[0087]

[0088] In the formula, k v is the proportional gain coefficient, v-v0 is the relative speed between the main vehicle and the obstacle, v is the current speed of the main vehicle, v0 is the current speed of the obstacle, the obstacle speed repulsion is opposite to the direction of the relative speed, and α is the angle between the direction of the relative speed and the vector formed by the position points of the main vehicle and the obstacle.

[0089] S4. Generate the potential field of the resultant force on the main vehicle according to the virtual potential fields obtained in step S3, such as Figure 4 As shown, the resultant potential field U total (X) is expressed by the following formula:

[0090]

[0091] Where n is the number of obstacles.

[0092] According to the generated resultant force potential field, the magnitude of the resultant force is calculated and the direction of the resultant force is determined, so that the main vehicle can avoid obstacles along the direction of the resultant force, and the main vehicle information and obstacle information are refreshed in real time according to the refresh frequency of each sensor. total (X) is expressed by the following formula:

[0093]

[0094] S5. The current position of the main vehicle is p0. A judgment is made based on the resultant force obtained in step S4, and a corresponding obstacle avoidance method is used to avoid the obstacle.

[0095] If the resultant force is not 0, the vehicle is not trapped in the local optimum. The next trajectory point p1 of the vehicle is planned based on the resultant force. Draw a circle with the vehicle position p0 as the center and the unit step length h as the radius, and determine the point on the circle where the vehicle points to the direction of the resultant force and is located as the next trajectory point p1.

[0096] If the resultant force is 0, the vehicle will fall into a local optimum. The resultant forces at all positions around the local optimum point all point to the local optimum, causing the vehicle to oscillate around the local optimum and unable to leave the area on its own. At this time, the obstacle avoidance function 1 is activated to escape from the local optimum. Figure 5 As shown, circle obstacle avoidance 1 means: a circle is drawn with the current position of the obstacle as the center and the distance from the main vehicle to the obstacle as the radius. According to the unit step length h, a point on the circle with a distance h from the current position of the main vehicle is determined as the next trajectory point p2. The main vehicle drives along the arc between the current position and the next trajectory point p2 to achieve circle obstacle avoidance 1. An interval Δt is also specified, and the position coordinates of the main vehicle and the obstacle are updated in real time according to the newly collected information to ensure the real-time performance of the planned path.

[0097] When the next trajectory point p1 of the main vehicle is planned by the artificial potential field method, the obstacle is also expanded. At the same time, the size of the main vehicle is considered and the obstacle is expanded by 1.25 times the length and width of the obstacle to determine the obstacle range. If p1 is within the obstacle range, the circle obstacle avoidance 2 is triggered.

[0098] like Figure 6As shown, the circle obstacle avoidance 2 means: taking the position of the obstacle as the center of the circle and the distance between the main vehicle and the obstacle as the radius, the position of the straight line passing through the center of the circle and point p1 on the circle is determined as the next trajectory point p3. The main vehicle drives along the arc from the current position to the next trajectory point p3 to achieve circle obstacle avoidance 2. When reaching point p3, the circle obstacle avoidance 2 is exited, and the next trajectory point is planned according to the artificial potential field.

[0099] During the obstacle avoidance process, a smooth curve is fitted to each trajectory point to obtain the planned path for the main vehicle to avoid obstacles.

[0100] S6. When the main vehicle is avoiding an obstacle, consider constraining the vehicle speed. When the main vehicle enters the repulsion range of the obstacle, the speed of the main vehicle is controlled according to the magnitude of the resultant force acting on the main vehicle to reduce the speed, thereby improving safety during obstacle avoidance. When the main vehicle leaves the repulsion range of the obstacle, the speed is restored to the speed before obstacle avoidance.

[0101] Speed ​​planning is expressed by the following formula:

[0102] F total (X) = KF

[0103] F=ma

[0104]

[0105] Where v′ is the speed of the main vehicle when avoiding obstacles, K is the coefficient that converts the virtual force on the main vehicle in the artificial potential field into the real force in the direction of the speed of the unmanned vehicle, T is the time taken by the main vehicle to avoid obstacles, and ρ ob is the distance between the main vehicle and the obstacle, v is the current speed of the main vehicle before avoiding the obstacle, m is the mass of the main vehicle, and a is the deceleration of the main vehicle when avoiding the obstacle.

[0106] The present invention constructs an artificial potential field and generates a resultant force potential field, plans the next moving trajectory point according to the resultant force potential field, and judges whether the unmanned vehicle falls into a local optimum according to the resultant force. The problem of the artificial potential field falling into a local optimum is solved by the circle obstacle avoidance 1, and the problem of the planned trajectory point being within the expansion range of the obstacle is solved by the circle obstacle avoidance 2. In the process of circle obstacle avoidance, the speed of the main vehicle is planned by the resultant force, so that the vehicle speed is more in line with the planned obstacle avoidance path, thereby improving the safety during obstacle avoidance and realizing safe obstacle avoidance of the unmanned vehicle.

Claims

1. A method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field, characterized in that: The steps include: S1. Obtaining the main vehicle information, obstacle information, road boundary information, and sensor blind spot boundary information, and determining the target point position; the main vehicle information includes the main vehicle position and the main vehicle speed, and the obstacle information includes the obstacle position and the obstacle speed; S2, establishing a follow-up rectangular coordinate system that follows the movement of the main vehicle, and in the follow-up rectangular coordinate system, establishing a target point gravitational potential field for characterizing the influence of the target point position on the movement of the main vehicle according to the main vehicle position and the target point position, establishing an obstacle repulsion potential field for characterizing the influence of the obstacle on the movement of the main vehicle according to the main vehicle position, the obstacle position and the target point position, establishing an obstacle velocity repulsion potential field for characterizing the influence of the obstacle velocity on the movement of the main vehicle according to the obstacle velocity information, establishing a road boundary repulsion potential field for characterizing the influence of the road boundary on the movement of the main vehicle according to the road boundary information, and establishing a sensor blind spot repulsion potential field for characterizing the influence of the sensor acquisition blind spot on the movement of the main vehicle according to the sensor blind spot boundary; S3, calculating the resultant force in the composite field formed by combining the potential fields generated in step S2; S4, if the resultant force is not 0, a first reference circle is made with the position of the main vehicle as the center and the step length as the radius, and the main vehicle is taken as the starting point. The main vehicle is directed to a point on the first reference circle in the direction of the resultant force as the next trajectory point p1 of the main vehicle, and an obstacle avoidance path of the main vehicle is determined according to the trajectory point p1, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid obstacles; If the resultant force is 0, a second reference circle is made with the position of the main vehicle as the center and the step length as the radius, and a third reference circle is made with the obstacle as the center and the distance between the main vehicle and the obstacle as the radius. The intersection of the second reference circle and the third reference circle is taken as the next trajectory point p2 of the main vehicle. The obstacle avoidance path of the main vehicle is determined according to the trajectory point p2, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid the obstacle.

2. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1, characterized in that: In step S4, when the resultant force is not 0, if the trajectory point p1 is within the expansion range where the obstacle is located, the point of the obstacle that is located in the direction pointing to the trajectory point p1 and on the third reference circle is taken as the next trajectory point p3 of the main vehicle movement, and the obstacle avoidance path of the main vehicle is determined according to the trajectory point p3, and the main vehicle is controlled to travel along the obstacle avoidance path to avoid the obstacle; the size of the obstacle is enlarged according to the set ratio to obtain the expansion range.

3. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1, characterized in that: Obstacle velocity repulsion potential field U rev (X) is expressed by the following formula: In the formula, k v is the proportional gain coefficient, v-v0 is the relative speed between the main vehicle and the obstacle, v is the current speed of the main vehicle, v0 is the current speed of the obstacle, the direction of the obstacle speed repulsion is opposite to the relative speed, α is the angle between the direction of the relative speed and the line connecting the main vehicle position and the obstacle position, ρ ob is the distance between the main vehicle and the obstacle, and A is the longitudinal impact distance of the obstacle.

4. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1, characterized in that: Road boundary repulsive potential field U road (X) is expressed by the following formula: In the formula, k road is the road boundary repulsion coefficient, ||r i -r l || is the vertical distance from the main vehicle to the road boundary, r i is the horizontal coordinate of the position of the main vehicle in the follower rectangular coordinate system, r l is the ordinate of the position of the main vehicle in the follow-up rectangular coordinate system, W is the body width of the main vehicle, and k0 is the road boundary influence distance.

5. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1, characterized in that: Sensor blind area repulsive potential field U r-blind (X) is expressed by the following formula: In the formula, K r-blind is the blind zone repulsion coefficient of the front sensor, ||r i -r b || is the vertical distance between the sensor blind spot boundary and the main vehicle, v is the current speed of the main vehicle, and its direction is the same as the direction of the obstacle repulsion, t hr is the threat level of dangerous obstacles in the sensor blind area.

6. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1 or 2, characterized in that: The target speed of the main vehicle for obstacle avoidance is also determined according to the combined force, and the target speed is used as the control target for obstacle avoidance of the main vehicle, so that the main vehicle is controlled to travel along the obstacle avoidance path to avoid the obstacle.

7. The method for obstacle avoidance path planning for an unmanned vehicle on a curved road based on an artificial potential field according to claim 1 or 2, characterized in that: Before the main vehicle avoids an obstacle, if the distance between the main vehicle and the obstacle is less than the set safety distance, the main vehicle is controlled to slow down.

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