Unmanned aerial vehicle obstacle avoidance method adaptive to wind disturbance

Abstract

The invention belongs to the technical field of unmanned aerial vehicle obstacle avoidance, and particularly relates to a self-adaptive wind disturbance unmanned aerial vehicle obstacle avoidance method, which comprises the following steps of: 1, introducing a gravitational potential field model, 2, introducing a repulsive force potential field model, 3, calculating a wind acceleration vector, 4, improving the repulsive force potential field model, and establishing an improved repulsive force potential field model. And 5, establishing a resultant force potential field model. In the obstacle avoidance process, enough buffer space can be reserved for larger gust which is about to arrive, so that the flight safety of the unmanned aerial vehicle in the autonomous obstacle avoidance process is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle obstacle avoidance, and in particular relates to a unmanned aerial vehicle obstacle avoidance method that is adaptive to wind disturbance. Background Art

[0002] With the rapid development of drone technology, its application in many fields such as commerce, agriculture, rescue, and monitoring is becoming more and more extensive. The autonomous flight capability of drones, especially the obstacle avoidance capability, has become one of the key technologies for their safe operation. Traditional drone flights mostly rely on remote control operations, which have obvious limitations when facing complex and changeable flight environments. Remote control operations cannot respond to environmental changes in real time, and operators cannot predict all potential obstacles, which greatly limits the application scope and safety of drones.

[0003] In order to solve this problem, researchers began to explore autonomous obstacle avoidance technology for drones. By integrating various sensors, such as lidar, cameras, infrared sensors, etc., drones can obtain information about the surrounding environment in real time. These sensor data provide drones with environmental perception capabilities, which is the basis for autonomous obstacle avoidance. On this basis, researchers have developed a variety of algorithms, including obstacle recognition, path planning, dynamic obstacle avoidance, etc., to achieve intelligent decision-making and autonomous flight of drones.

[0004] At present, UAV obstacle avoidance technology has made some progress, and a variety of path planning and obstacle avoidance methods have emerged, which have improved the autonomous obstacle avoidance capabilities of UAVs to a certain extent;

[0005] However, most of the obstacle avoidance algorithms in the prior art are based on the assumption of environmental invariance, that is, the surrounding obstacles are assumed to be static, without considering the interference of actual environmental factors such as wind. In actual flight, drones will inevitably encounter the influence of wind, especially when passing through narrow passages. Wind interference may cause the drone to deviate from the predetermined path or even collide with obstacles. Therefore, the existing obstacle avoidance algorithms do not perform well in complex and dynamic environments.

[0006] In view of this, the present invention provides an autonomous obstacle avoidance method for a UAV taking wind interference into consideration. Summary of the invention

[0007] The purpose of the present invention is to provide a UAV obstacle avoidance method with adaptive wind disturbance, which can reserve sufficient buffer space for the upcoming larger gusts during the obstacle avoidance process to ensure the flight safety of the UAV in autonomous obstacle avoidance.

[0008] The technical solution adopted by the present invention is as follows:

[0009] A method for avoiding obstacles of unmanned aerial vehicles with adaptive wind disturbance comprises the following steps:

[0010] Step 1: Introduce the gravitational potential field model;

[0011] Step 2: Introduce the repulsive potential field model;

[0012] Step 3: Calculate the wind acceleration vector;

[0013] Step 4: Improve the repulsive potential field model and establish the improved repulsive potential field model;

[0014] Step 5: Establish the resultant potential field model.

[0015] The technical effects achieved by the present invention are:

[0016] The present invention measures the direction and size of wind force through an accelerometer and adds the wind force factor into the obstacle avoidance algorithm so as to reserve enough buffer space for the upcoming larger gusts during the obstacle avoidance process, thereby ensuring the flight safety of the UAV in autonomous obstacle avoidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the technical solution of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific embodiments of the present invention, and does not strictly limit the scope of protection of the specific claims of the present invention.

[0019] like Figure 1 As shown in the figure, an adaptive wind disturbance obstacle avoidance method for unmanned aerial vehicles is used. An obstacle repulsion potential field is constructed around the obstacle. At the same time, an equivalent obstacle repulsion potential field is virtually constructed according to the wind vector. A gravitational potential field is constructed around the target point, which is similar to the electromagnetic field in physics. The unmanned aerial vehicle is subjected to repulsion and gravitation in the composite field composed of these two potential fields. The combined force of repulsion and gravitation guides the movement of the controlled object, thereby searching for a collision-free autonomous obstacle avoidance path.

[0020] It includes the following steps:

[0021] S1: Introduce the gravitational potential field model;

[0022] Gravity is usually generated by the target location, causing the robot to be pulled towards the target. The magnitude of this force is usually related to the distance between the drone and the target, with the greater the distance, the greater the gravitational force.

[0023] The gravitational function of the gravitational potential field model is:

[0024]

[0025] Among them, η is the proportional gain function, ρ(q,q g ) is a vector, which represents the position q of the drone and the position q of the target point g The Euclidean distance between |q,q g |, the vector direction is from the position of the drone to the target point position.

[0026] S2: Introduce the repulsive potential field model;

[0027] The factor that determines the repulsive potential field of the obstacle is the distance between the robot and the obstacle. When the robot does not enter the influence range of the obstacle, the potential energy value it receives is zero; after the robot enters the influence range of the obstacle, the greater the distance between the two, the smaller the repulsive force the robot receives, and the smaller the distance, the greater the repulsive force the robot receives.

[0028] The function formula of the repulsive potential field model is:

[0029]

[0030] Where k is a proportional coefficient, ρ(q,q0) is a vector whose direction is from the obstacle to the drone and whose magnitude is the distance between the drone and the obstacle |q-q0|, and ρ0 is a constant, which indicates the maximum distance at which the obstacle affects the drone.

[0031] The corresponding repulsive force is the negative gradient of the repulsive field, and its calculation formula is:

[0032]

[0033] in, represents the unit vector pointing from q0 to q, and its calculation formula is:

[0034]

[0035] S3: Calculate wind acceleration vector;

[0036] During the flight of a drone, due to the interference of wind force, a force will be exerted on the drone. The force generated by the wind speed will produce acceleration, and a certain posture is required to compensate for the thrust.

[0037] The relationship between the attitude inclination angle θ and the generated acceleration a is:

[0038] a=gsinθ

[0039] The air resistance calculation formula for wind acceleration is:

[0040]

[0041] This force will disturb the drone's attitude, but due to the existence of a flight control system, the drone's attitude can still be balanced at an angle after being disturbed, so this angle can reflect the size and direction of the wind speed to a certain extent.

[0042] The drone can estimate the wind speed component using the relationship between airspeed and drag along the X and Y fuselage axes, which can be measured by the IMU accelerometers on the fuselage. The wind force vector is denoted as W d , the size is where a x and a y are the accelerations along the X and Y body axes respectively.

[0043] S4: Improve the repulsive potential field model and establish the improved repulsive potential field model;

[0044] The function formula of the improved repulsive potential field model is:

[0045]

[0046] Among them, λ is the wind disturbance gravity estimation weight coefficient, W d is the wind vector, ρ(q,p0) is the distance between the obstacle and the drone, that is,

[0047] The corresponding repulsive force is the negative gradient of the repulsive field, and the function formula is:

[0048]

[0049] S5: Establish the resultant potential field model.

[0050] According to the gravitational function of the gravitational potential field model and the function of the improved repulsive potential field model, the composite field of the entire operating space can be obtained. The size of the resultant potential field of the UAV is the sum of the repulsive potential field and the gravitational potential field to which the UAV is subjected. Therefore, the total number of rows of the resultant potential field is:

[0051] U(q)=U att (q)+U req (q)

[0052] The resultant force is:

[0053]

[0054] The direction of the resultant force determines the direction of the drone's travel, and the magnitude of the resultant force determines the drone's travel acceleration.

[0055] This application makes up for the lack of wind disturbance estimation in the current obstacle avoidance algorithm, and proposes wind disturbance estimation in the obstacle avoidance algorithm. It only needs to use the acceleration value provided by the IMU of the fuselage to estimate the X and Y components of the wind speed. It regards wind disturbance as a virtual repulsive force, which is equivalent to an obstacle, introduces repulsive force function and repulsive potential field, and integrates the obstacle and wind disturbance into calculation.

[0056] Compared with the existing obstacle avoidance methods for drones, the present application reserves redundant wind space for drones in actual flight. The stronger the wind, the more redundant space there is, to prevent the drone from having insufficient space to avoid obstacles when a higher wind speed is generated later, thereby reducing the risk of the drone colliding with obstacles due to wind. When estimating the wind vector, there is no need to use other wind speed measurement sensors such as an airspeed meter to prevent system redundancy. In addition, for winds of different speeds, mainly lateral crosswinds, different parameters λ are used to cope with different wind forces, so that the obstacle avoidance algorithm is more adaptable to the actual wind environment and the system redundancy is enhanced.

[0057] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. An adaptive wind disturbance obstacle avoidance method for UAVs. It is characterized in that The following steps are involved: S1: Introduce the gravitational potential field model; S2: Introduce the repulsive potential field model; S3: Calculate wind acceleration vector; S4: Improve the repulsive potential field model and establish the improved repulsive potential field model; S5: Establish the resultant potential field model.

2. The unmanned aerial vehicle obstacle avoidance method according to claim 1, Features: The gravitational function of the gravitational potential field model in S1 is: Among them, η is the proportional gain function, ρ(q,q g ) is a vector, which represents the position q of the drone and the position q of the target point g The Euclidean distance between |q,q g |.

3. The unmanned aerial vehicle obstacle avoidance method according to claim 2, Features: The function formula of the repulsive potential field model in S2 is: Among them, k is the proportional coefficient, ρ(q,q 0 ) is a vector, the direction is from the obstacle to the drone, and the magnitude is the distance between the drone and the obstacle. 0 |,ρ 0 is a constant, indicating the maximum distance at which obstacles can affect the drone; The corresponding repulsive force is the negative gradient of the repulsive field, and its calculation formula is: in, Indicates that from q 0 The unit vector pointing to q is calculated as:

4. The unmanned aerial vehicle obstacle avoidance method according to claim 1, Features: The air resistance calculation formula of wind acceleration in S3 is:

5. The unmanned aerial vehicle obstacle avoidance method according to claim 3, Features: The function formula of the improved repulsive potential field model in S4 is: Among them, λ is the wind disturbance gravity estimation weight coefficient, W d is the wind force vector, ρ(q,q 0 ) is the distance between the obstacle and the UAV, that is 6. The unmanned aerial vehicle obstacle avoidance method according to claim 5, Features: The corresponding repulsive force in S4 is the negative gradient of the repulsive field, and the function formula is:

7. The unmanned aerial vehicle obstacle avoidance method according to claim 6, Features: In S5, the composite field of the entire operating space is obtained according to the gravitational function of the gravitational potential field model and the function of the improved repulsive potential field model. The size of the resultant potential field of the drone is the sum of the repulsive potential field and the gravitational potential field to which the drone is subjected. Therefore, the total number of rows of the resultant potential field is: U(q)=U att (q)+U req (q)。 8. The unmanned aerial vehicle obstacle avoidance method according to claim 7, Features: The resultant force in S5 is:

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