Automatic police system of unmanned aerial vehicle

Abstract

The invention relates to an automatic police system of an unmanned aerial vehicle, which adopts the unmanned aerial vehicle and comprises the following steps: defining obstacles which can be encountered when flying to a target area; simulating and generating different obstacles; analyzing the degree of threat to itself; utilizing an A * algorithm to evaluate an evaluation function value of a path in a patrol space to heuristically patrol nodes to construct an optimal path; the patrol efficiency of the flight destination is improved by using an A * algorithm; determining a next space node for flight patrol; generating a path evaluation cost function and obtaining an optimal path flying to an alarm point; the set starting point coordinate is set, the alarm target point coordinate is obtained, and a track is calculated and generated to go for photographing; after arriving, the camera shoots and alarms; the distance between the unmanned aerial vehicle and the center of the building is obtained by using a formula for calculating the threat degree in the flight process of the unmanned aerial vehicle, so that the distance between the unmanned aerial vehicle and the edge of the building is always controllable, obstacle avoidance flight is realized, and the application scene of the unmanned aerial vehicle is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an automatic alarm system for unmanned aerial vehicles. Background Art

[0002] In recent years, with the rapid development of science and technology and economic progress, drones have been increasingly used in all walks of life. In the government and police fields, drones can be used for daily urban patrols, stability maintenance and control to overcome the blind spot disadvantages of fixed surveillance cameras (surveillance blind spots are easy to be exploited and circumvented in the criminological sense). In addition, surveillance cameras are post-event relief security technologies, which cannot play a role in stopping illegal acts at the first moment of the incident. They can only dispatch nearby police forces through the command center, which means that it is difficult to have real-time action.

[0003] The application announcement number is CN110807887A, which discloses a night patrol drone. Combined with its specification and drawings, its infrared camera can capture thermal radiation images related to temperature, which can be used to determine whether there are people and animals breaking in. The visible light camera is used to capture images under the visible spectrum, which can be used for on-site recording. The processor stores the output images of the visible light camera and the infrared camera in the storage module as on-site records, and turns on the lighting module and the sound module for warning. It can also send information to the set terminal through the communication module. However, the patrol recording method adopted has certain limitations. When an individual reports an alarm, the drone cannot effectively fly to the vicinity of the crime scene, which brings inconvenience to its use. Summary of the invention

[0004] The present invention mainly aims at the problems existing in drone patrols and invents an automatic alarm system for drones. Based on the improved A* search algorithm, the planning time of the drone's path to the alarm point can be effectively shortened, the number of planning steps can be reduced, and the path length can be shortened. During the flight of the drone, the formula for calculating the threat level is used to obtain the distance from the drone to the center of the building, so that the distance between the drone and the edge of the building is always controllable, and obstacle avoidance flight can be achieved.

[0005] The invention objective of the present invention is achieved through the following technical scheme: an automatic alarm system for a drone, using a laser radar, a visible light camera and an infrared temperature measurement camera carried by the drone, comprising the following steps:

[0006] S1: The drone defines the obstacles that it may encounter when flying to the target area;

[0007] S2: The drone simulates and generates different obstacles it encounters;

[0008] S3: The drone analyzes the threat level of obstacles to itself;

[0009] S4: The drone uses the A* algorithm to continuously evaluate the evaluation function value of the path in the patrol space to construct the optimal path;

[0010] S5: Drones use the A* algorithm to improve the efficiency of patrolling the flight destination;

[0011] S6: The drone determines the next space node where it can perform flight patrol;

[0012] S7: The UAV generates a path evaluation cost function and obtains the best path to the alarm point;

[0013] S8: The rationality of the drone’s continuous path adjustment;

[0014] S9: The drone sets the coordinates of the starting point, obtains the coordinates of the alarm target point, and uses the information of the calculated nodes to generate a flight trajectory to take photos;

[0015] S10: After the drone reaches the target point, the camera rotates to shoot the scene and situation around the alarm location and send it to the command center.

[0016] Preferably, the drone defines obstacles that may be encountered when flying to the target area, specifically:

[0017] S11: Generate the formula for calculating obstacles:

[0018] Preferably, the drone simulates and generates different obstacles encountered, specifically:

[0019] S21: The UAV uses the laser radar to obtain the parameter values ​​of the obstacles ahead when flying;

[0020] S22: Substitute the parameters into the obstacle calculation formula to generate different obstacle information.

[0021] Preferably, the drone analyzes the threat level of obstacles to itself, specifically:

[0022] S31: The formula for calculating the threat level of drones is:

[0023]

[0024] S32: Get the distance parameter between the drone and the edge of the building:

[0025] S33: The drone obtains different safety states according to different distance parameters.

[0026] Preferably, the drone uses the A* algorithm to improve the patrol efficiency of the flight destination, specifically:

[0027] S51: The UAV determines the patrol direction and patrol steps;

[0028] S52: The patrol direction of the drone is set to patrol the remaining area in three directions: forward, pitch and yaw;

[0029] S53: The UAV patrols in the pitch and yaw directions to the maximum extent possible, subject to the maneuverability constraints of the UAV;

[0030] S54: When the UAV is on forward patrol, the maximum patrol step length is limited by the detection range of the airborne radar;

[0031] S55: The UAV patrol step length adjustment selects to adjust the patrol between the minimum turning radius and the maximum step length. Preferably, the UAV determines the next space node where it can perform flight patrol, specifically:

[0032] S61: The UAV takes the patrol area of ​​the current node as the feasible search space of the current position, in an area surrounded by a tetrahedral pyramid and two spherical surfaces;

[0033] S62: The drone divides the area into some nodes according to the grid, and divides them into p, q, t equal distances in the pitch direction, yaw direction and forward direction, forming (p+l)*(q+l)*(t+1) patrol nodes;

[0034] S63: The UAV performs a heuristic patrol evaluation on each node and selects the best node as the next node until it reaches the target point.

[0035] Preferably, the UAV generates a path evaluation cost function and obtains the best path to the alarm point, specifically:

[0036] S71: The drone uses the A* algorithm to select the weighted evaluation method, that is, f(n) = ω g g(n)+ω h h(n);

[0037] S72: UAV real-time adjustment g and ω h The value of optimizes the evaluation function,

[0038] Right now

[0039] As a preferred embodiment, the drone continuously adjusts the rationality of the path, specifically:

[0040] S81: The weight ω of g(n) when the drone approaches the target g Become larger, so as to ensure the rationality of the path;

[0041] S82: The drone needs to adjust its flight altitude;

[0042] S83: Path planning for drones to cope with different tasks, introducing height evaluation in the path evaluation function: g(n) = w L L(n)+w C C(n)+w T T(n);

[0043] S84: The Euclidean distance h(n) from the current node of the drone to the next node is expressed as:

[0044]

[0045] Compared with the prior art, the present invention has the following beneficial effects: 1. The drone of the present invention divides the area into some nodes according to rasterization, and divides the area into p, q, t and other distances from the pitch direction, yaw direction and forward direction respectively, to form (p+l)*(q+l)*(t+1) patrol nodes. Based on the improved A* search algorithm, the planning time of the drone's path to the alarm point can be effectively shortened, the number of planning steps can be reduced, and the path length can be shortened. The efficient path planning algorithm can enable the drone to shoot on-site at the first time; 2. During the flight of the drone, the formula for calculating the degree of threat is used to obtain the distance from the drone to the center of the building, so that the distance between the drone and the edge of the building is always controllable, obstacle avoidance flight can be achieved, and the applicable scenarios of the drone are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the present invention;

[0047] Figure 2 Schematic diagram of patrol space of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below with reference to the embodiments shown in the accompanying drawings:

[0049] like Figure 1 to Figure 2 As shown, an automatic alarm system of a UAV includes a UAV for collecting information at the alarm scene. The UAV mainly adopts a (Real-time kinematic) RTK UAV, which can achieve centimeter-level positioning based on RTK carrier phase difference technology, ensuring the accuracy of the UAV. The laser radar, visible light camera and infrared temperature measurement camera carried by the UAV complete the information collection.

[0050] S1: The drone defines the obstacles that it may encounter when flying to the target area;

[0051] In step S1, the target area that the drone is heading to is the alarm point, which can be the alarm person's own location or the alarm location uploaded by other clients;

[0052] S11: Generate the formula for calculating obstacles:

[0053] In step S11, (x, y) is the coordinate value of a point on the building surface projected on the horizontal plane; z is the height of the point; h i is the parameter to control the height; a i and b i Represents the projection coordinate value of the building center; K i To control the parameters of terrain slope, when these parameters take different values, different obstacles will be obtained;

[0054] S2: The drone simulates and generates different obstacles it encounters;

[0055] S21: The UAV uses the laser radar to obtain the parameter values ​​of the obstacles ahead when flying;

[0056] S22: Substituting the parameters into the obstacle calculation formula to generate different obstacle information;

[0057] S3: The drone analyzes the threat level of obstacles to itself;

[0058] S31: Drone generation formula for calculating threat level

[0059]

[0060] In step S31, dm represents the distance from the drone to the center of the building; Rm represents the radius of the building,

[0061] S32: Obtain the distance parameter between the drone and the edge of the building;

[0062] S33: The drone obtains different safety states according to different distance parameters;

[0063] In step S33, whether the distance between the drone and the edge of the building exceeds 10m, if it exceeds, it is considered safe and the risk level is 0; whether the distance between the drone and the edge of the building is between 4m and 10m, the closer the distance, the greater the risk level; whether the distance between the drone and the edge of the building is less than 4m, the risk level is infinite;

[0064] S4: The drone uses the A* algorithm to continuously evaluate the evaluation function value of the path in the patrol space to construct the optimal path;

[0065] S41: Common evaluation function of drone-generated A* algorithm: f(n)=g(n)+h(n);

[0066] In step S41, n is the current node; f(n) is the evaluation function from the initial point via node n to the target point; g(n) is the actual cost from the initial node to node n in the state space, h(n) is the estimated cost from node n to the target node, and the patrol efficiency is determined by the patrol direction and patrol step length.

[0067] S5: Drones use the A* algorithm to improve the efficiency of patrolling the flight destination;

[0068] S51: The UAV determines the patrol direction and patrol step length;

[0069] In step S51, in order to improve patrol efficiency, it is necessary to improve the patrol direction and patrol step length. The quality of the path depends on the design of the evaluation function.

[0070] S52: The patrol direction of the drone is set to patrol the remaining area in three directions: forward, pitch and yaw;

[0071] S53: The UAV patrols in the pitch and yaw directions to the maximum extent possible, subject to the maneuverability constraints of the UAV;

[0072] S54: When the UAV is on forward patrol, the maximum patrol step length is limited by the detection range of the airborne radar;

[0073] In step S54, in order to improve patrol efficiency, variable step length patrol is proposed;

[0074] S55: UAV patrol step length adjustment selection adjusts the patrol between the minimum turning radius and the maximum step length;

[0075] Please refer to Figure 2 :Schematic diagram of patrol space based on A* algorithm, Figure 2 Middle: O is the current node; OA is the minimum step length of the extended patrol, which is related to the minimum turning radius; 0E is the maximum step length, ∠FON=θ, is the maximum yaw angle, ∠HOM=φ, is the maximum climb angle, ∠EOH=2θ, is the yaw range, ∠FOG=2φ, is the pitch range;

[0076] S6: The drone determines the next space node where it can perform flight patrol;

[0077] S61: The UAV takes the patrol area of ​​the current node O as the feasible search space of the current position, in an area surrounded by a tetrahedral pyramid and two spherical surfaces.

[0078] S62: The drone divides the area into some nodes according to the grid, and divides them into p, q, t equal distances in the pitch direction, yaw direction and forward direction, forming (p+l)*(q+l)*(t+1) patrol nodes;

[0079] S63: The UAV performs heuristic patrol evaluation on each node and selects the best node as the next node until it reaches the target point;

[0080] S7: The UAV generates a path evaluation cost function and obtains the best path to the alarm point;

[0081] S71: The drone uses the A* algorithm to select the weighted evaluation method, that is, f(n) = ω g g(n)+ω h h(n);

[0082] In step S71, since the cost of the current node of the A* algorithm is usually expressed as f(n) = g(n) + h(n), and considering that in the process of path search, g(n) and h(n) have different effects on path evaluation, ω g is the weight of the actual cost g(n) from the starting point to the current node, ω h is the weight of the estimated cost of the current node to reach the target, satisfying ω g +ω h =1;

[0083] S72: UAV real-time adjustment g and ω h The value of optimizes the evaluation function, namely:

[0084]

[0085] In step S72, when ω g When ω is too large, the second half of the path planning is more ideal. h When it is too large, the first half is more ideal. gmax ω g The maximum value of , h(n) is the Euclidean distance from the current position to the target point, and D is the Euclidean distance from the starting point to the target point;

[0086] S8: The rationality of the drone’s continuous path adjustment;

[0087] S81: The weight ω of g(n) when the drone approaches the target g Become larger, so as to ensure the rationality of the path;

[0088] S82: The drone needs to adjust its flight altitude;

[0089] In step S82, when planning the path of the drone, there are often requirements for the height of the flight path. When flying long distances, it is necessary to fly smoothly and try to choose a plane with the same height. When the drone wants to achieve a stealth effect, it needs to fly at a low altitude.

[0090] S83: Path planning for drones to cope with different tasks, introducing altitude evaluation in the path evaluation function:

[0091] g(n)=w L L(n)+w C C(n)+w T T(n);

[0092] In step S83, L(n) is the distance cost from the initial node to the current node, C(n) is the altitude cost, T(n) is the threat cost, and the maximum value T(n) is selected. L ,w C and w T is the corresponding weight coefficient,

[0093] S84: The Euclidean distance h(n) from the current node of the drone to the next node is expressed as:

[0094]

[0095] In step S84, the current position coordinates are (x n ,y n ,z n ); the coordinates of the target point are (x g ,y g , z g ),

[0096] S9: The starting point coordinates of the drone are set to (0,0,0), and the coordinates of the alarm target point are obtained to be (10,10,2). The information of the calculated nodes is used to generate a flight trajectory to take photos;

[0097] In step S9: the drone is divided into four equal parts in the pitch direction, the yaw direction and the forward direction to determine the search nodes, so as to realize the three-dimensional path planning;

[0098] S10: After the drone reaches the target point, the camera rotates to shoot the scene and situation around the alarm location and send it to the command center.

[0099] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. An automatic alarm system for drones, characterized in that: Using the laser radar, visible light camera and infrared temperature measurement camera carried by the drone, the following steps are included: S1: The drone defines the obstacles that it may encounter when flying to the target area; S2: The drone simulates and generates different obstacles it encounters; S3: The drone analyzes the threat level of obstacles to itself; S4: The drone uses the A* algorithm to continuously evaluate the evaluation function value of the path in the patrol space to construct the optimal path; S5: Drones use the A* algorithm to improve the efficiency of patrolling the flight destination; S6: The UAV determines the next space node for flight patrol; S7: The UAV generates a path evaluation cost function and obtains the best path to the alarm point; S8: rationality of the drone’s path adjustment; S9: The drone sets the coordinates of the starting point, obtains the coordinates of the alarm target point, and uses the information of the calculated nodes to generate a flight trajectory to take photos; S10: After the drone reaches the target point, the camera rotates to shoot the scene and situation around the alarm location and send it to the command center.

2. The automatic alarm system for drones according to claim 1, characterized in that: The obstacles that the drone may encounter when flying to the target area are defined as follows: S11: Generate the formula for calculating obstacles:

3. The automatic alarm system for drones according to claim 2, characterized in that: The drone simulates and generates different obstacles encountered, specifically: S21: The UAV uses the laser radar to obtain the parameter values ​​of the obstacles ahead when flying; S22: Substitute the parameters into the obstacle calculation formula to generate different obstacle information.

4. The automatic alarm system for drones according to claim 3, characterized in that: The drone analyzes the threat level of obstacles to itself, specifically: S31: The formula for calculating the threat level of drones is: S32: Get the distance parameter between the drone and the edge of the building: S33: The drone obtains different safety states according to different distance parameters.

5. The automatic alarm system for drones according to claim 4, characterized in that: The drone uses the A* algorithm to improve the patrol efficiency of the flight destination, specifically: S51: The UAV determines the patrol direction and patrol steps; S52: The patrol direction of the drone is set to patrol the remaining area in three directions: forward, pitch and yaw; S53: The UAV patrols in the pitch and yaw directions to the maximum extent possible, subject to the maneuverability constraints of the UAV; S54: When the UAV is on forward patrol, the maximum patrol step length is limited by the detection range of the airborne radar; S55: UAV patrol step length adjustment Select to adjust the patrol between the minimum turning radius and the maximum step length.

6. The automatic alarm system for drones according to claim 5, characterized in that: The UAV determines the next space node for flight patrol, specifically: S61: The UAV takes the patrol area of ​​the current node as the feasible search space of the current position, in an area surrounded by a tetrahedral pyramid and two spherical surfaces; S62: The drone divides the area into some nodes according to the grid, and divides them into p, q, t equal distances in the pitch direction, yaw direction and forward direction, forming (p+l)*(q+l)*(t+1) patrol nodes; S63: The UAV performs a heuristic patrol evaluation on each node and selects the best node as the next node until it reaches the target point.

7. The automatic alarm system for drones according to claim 6, characterized in that: The UAV generates a path evaluation cost function and obtains the best path to the alarm point, specifically: S71: The drone uses the A* algorithm to select the weighted evaluation method, that is, f(n) = ω g g(n)+ω h h(n); S72: UAV real-time adjustment g and ω h The value of optimizes the evaluation function, Right now 8. The automatic alarm system for drones according to claim 7, characterized in that: The drone continuously adjusts the path rationality, specifically: S81: The weight ω of g(n) when the drone approaches the target g Become larger, so as to ensure the rationality of the path; S82: The drone needs to adjust its flight altitude; S83: Path planning for drones to cope with different tasks, introducing altitude evaluation in the path evaluation function: g(n)=w L L(n)+w C C(n)+w T T(n); S84: The Euclidean distance h(n) from the current node of the drone to the next node is expressed as:

Images