Remote control communication system of drone after police dispatch

Abstract

The invention relates to a remote control communication system for an unmanned aerial vehicle after an emergency, comprising an unmanned aerial vehicle and the following steps: the unmanned aerial vehicle establishes a communication channel with a ground user or a command center; the unmanned aerial vehicle starts to adjust its position and couples its three angle states of pitch angle, yaw angle and communication channel; calculates the coupling function between the three angle states of pitch angle, yaw angle and communication channel; obtains real-time data of the system through sensors installed in the system, and calculates the current state of the communication system; uses the communication channel system to execute control intervened by the command center; coordinates with multiple adjacent unmanned aerial vehicles to perform network shooting; returns with the highest priority when the battery is insufficient; whenever the unmanned aerial vehicle adjusts its position so that the transmission antenna rotates and changes, thereby affecting the communication channel, the unmanned aerial vehicle calculates the adjustment angle and optimizes the variable vector according to the conditional function of nonlinear programming, so as to ensure that the unmanned aerial vehicle provides the maximum communication service for the ground user.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a remote control communication system of an unmanned aerial vehicle after a police dispatch. Background Art

[0002] In recent years, the role of drones in the security field has become increasingly prominent. Compared with fixed monitoring, drones have outstanding effects in areas such as urban patrols. For urban patrols, although the navigation technology of drones has become increasingly mature, there are still many immature aspects in the control system of drones. Drones in autonomous flight mode can navigate autonomously according to navigation devices (such as GPS (Global Positioning System)), but since drones may encounter various emergencies during patrols, this will affect the application of drones in actual situations; and when manual intervention is required, the signal communication between drones and control platforms is particularly important in the complex environment of urban buildings.

[0003] The application announcement number is CN105824318A, which discloses a multi-rotor UAV communication and safety monitoring system. Combined with its specification and drawings, the scheme is a remote communication system for wireless communication and data analysis during the flight of a multi-rotor aircraft, which enables the operator to perform real-time ground remote control, parameter adjustment, data analysis, safety monitoring, emergency control in emergency situations, and timely retrieval of the multi-rotor aircraft, so as to achieve the purpose of real-time safety monitoring at any location, at any time, and at any distance within the effective communication range. However, the use of this type of flight control has certain limitations. The angle of the communication antenna of the UAV is constantly changing during flight. If its coordinates cannot be updated accordingly, precise positioning and control of the UAV cannot be achieved. Summary of the invention

[0004] The present invention mainly aims at the problems existing in drone patrols and invents a remote control communication system for drones after they go out on patrol. When the drone adjusts its position so that the transmission antenna rotates and changes, thereby affecting the communication channel, the drone calculates the adjustment angle and optimizes the variable vector according to the conditional function of nonlinear programming, ensuring that the drone can accurately reach the destination and adjust its posture as much as possible to provide the best communication service for ground users.

[0005] The invention objective of the present invention is achieved through the following technical solutions: A remote control communication system for a drone after an alarm, including a drone for collecting information on the alarm scene, the drone mainly uses multiple communication antennas, and is provided with several single-antenna mobile users or command centers, including the following steps:

[0006] S1: The UAV establishes a communication channel with the ground user or command center;

[0007] S2: The drone starts to adjust its position and couples its pitch angle, yaw angle and communication channel.

[0008] S3: The UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel;

[0009] S4: The drone obtains the real-time data of the system through the sensors placed in the system and calculates the current status of the communication system;

[0010] S5: The drone uses the communication channel system to perform control without the intervention of the command center;

[0011] S6: The drone cooperates with multiple adjacent drones to perform networked shooting;

[0012] S7: The drone returns to home with the highest priority when the battery is low.

[0013] Preferably, the UAV establishes a communication channel with a ground user or a command center, specifically:

[0014] S11: The UAV obtains the flight altitude through the air pressure altitude sensor and makes the diameter of the area covered by the ground less than 100m;

[0015] S12: The communication link between the UAV and the ground user node is composed of direct signal components, that is, the communication model is a Rice fading channel;

[0016] S13: The channel gain between the drone and the ground can be expressed as:

[0017]

[0018] S14: The drone obtains the user's GPS location and uses g i[ t ] Follows Rice distribution and has normalized power get:

[0019]

[0020] S15: The drone calculates and obtains a stable communication channel

[0021] Preferably, the UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel, specifically:

[0022] S21: The adjustment position of the drone causes the transmitting antenna to rotate and change;

[0023] S22: The azimuth and elevation angles of the direct link vector between the UAV and the ground user change;

[0024] S23: The drone starts calculating the rotated coordinate axis and defines x to represent the component of the rotating coordinate system of the transmitting antenna;

[0025] S24: The unit direction vector of the new coordinate system of the transmitting antenna array of the UAV after rotation can be expressed as:

[0026]

[0027] S25: The drone calculates the new coordinate system after rotation and obtains the position coordinates of the user receiver (i.e., the i-th user) updated as:

[0028]

[0029] S26: The azimuth and pitch angles of the coordinate axes of the rotating transmitting antenna array calculated by the UAV can be:

[0030]

[0031] Preferably, the UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel, specifically:

[0032] S31: The UAV obtains the steering vector matrix after attitude adjustment based on the updated azimuth and pitch angle:

[0033]

[0034] S32: The UAV obtains the coupling relationship expression between attitude and communication channel quality, and substitutes it into the formula to obtain the channel gain under the above model and the system ASE in time slot t It is used to form the objective function of the problem to be controlled in the subsequent content:

[0035]

[0036] Preferably, the drone obtains real-time data of the system through sensors installed in the system, and calculates the current state of the communication system, specifically:

[0037] S41: Based on the existing MPC online control strategy, it is required to solve in the current MPC iteration time slot t0: The open-loop control problem posed within the field of view:

[0038]

[0039] x[t+1]=f(x[t],u[t]),

[0040] g(x[t],u[t])=0,

[0041] h(x[t],u[t])≤0,

[0042]

[0043] S42: The drone obtains the inequality through the above function:

[0044]

[0045] S43: The drone introduces a smoothing constant τ>0 and a relaxation variable s according to the interior point method. j ,j={1,…,n h}, which is used to improve the sensitivity of the Newton method to the initial value, and the conditions for nonlinear programming are:

[0046]

[0047] G(w * )=0,

[0048] 0(w)-0,

[0049]

[0050] S44: The UAV optimizes the variable vector according to the conditional function of nonlinear programming to ensure that the UAV can accurately reach the destination and adjust its posture as much as possible to provide the maximum communication service for the ground user;

[0051] Preferably, the UAV uses the communication channel system to perform control with the intervention of the command center, specifically:

[0052] S51: Both user status information and drone status information can be accurately obtained through sensors;

[0053] S52: UAV flight control reads PID control algorithm;

[0054] S53: UAV uses PID attitude control algorithm to measure the attitude information of the UAV

[0055] S54: The UAV uses Kalman filtering and particle filtering to obtain the position and speed of the UAV in an environment with poor GPS signals;

[0056] S55: The drone adjusts the motor output of the drone in real time according to the instructions and environmental feedback to achieve the desired flight attitude target.

[0057] Compared with the prior art, the present invention has the following beneficial effects: 1. Whenever the UAV adjusts its position so that the transmitting antenna rotates and changes, thereby affecting the communication channel, the UAV calculates the adjustment angle and optimizes the variable vector according to the conditional function of nonlinear programming, ensuring that the UAV can accurately reach the destination and adjust its posture as much as possible to provide the best communication service for ground users; 2. The UAV uses the PID attitude control algorithm to measure the attitude information of the UAV, and uses Kalman filtering and particle filtering to obtain the position and speed of the UAV in an environment with poor GPS signals, and combines it with PID control, etc. to achieve more accurate and stable flight control. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of the invention;

[0059] Figure 2 It is a schematic diagram of the influence of the change of the posture of the UAV of the present invention on the channel during flight. DETAILED DESCRIPTION

[0060] The present invention will be further described below with reference to the embodiments shown in the accompanying drawings:

[0061] like Figure 1 to Figure 2 As shown, a remote control communication system for a UAV after an alarm is dispatched includes a UAV used for collecting information on the alarm scene. The UAV mainly adopts multiple communication antennas and is equipped with several single-antenna mobile users or command centers. In order to make full use of the communication system gain brought by the multi-antenna array and provide communication services to multiple users on the ground at the same time and frequency resources, a space division multiple access (SDMA) method is adopted to allocate an independent space for each user's data stream to avoid interference between data streams.

[0062] S1: The UAV establishes a communication channel with the ground user or command center;

[0063] S11: The UAV obtains the flight altitude through the air pressure altitude sensor and makes the diameter of the area covered by the ground less than 100m;

[0064] S12: The communication link between the UAV and the ground user node is composed of direct signal components, that is, the communication model is a Rice fading channel;

[0065] S13: The channel gain between the drone and the ground can be expressed as:

[0066]

[0067] In step S13, β i[ t ]represents the large-scale channel gain at time t, including signal attenuation, path loss, and shadowing effect, while g i[ t ] represents the small-scale fading coefficient at time t;

[0068] S14: The drone obtains the user's GPS location and uses g i[ t ] Follows Rice distribution and has normalized power get:

[0069]

[0070] In step S14, g i [t] represents the direct path channel component, represents the non-line of sight (NLoS) channel component, whose elements are independent and identically distributed (iid) random variables;

[0071] S15: The drone calculates and obtains a stable communication channel;

[0072] S2: The drone starts to adjust its position and couples its pitch angle, yaw angle and communication channel.

[0073] S21: The adjustment position of the drone causes the transmitting antenna to rotate and change;

[0074] S22: The azimuth and elevation angles of the direct link vector between the UAV and the ground user change;

[0075] S23: The drone starts calculating the rotated coordinate axis and defines x to represent the component of the rotating coordinate system of the transmitting antenna;

[0076] S24: The unit direction vector of the new coordinate system of the transmitting antenna array of the UAV after rotation can be expressed as:

[0077]

[0078] In step S24, R(φ[t],θ[t],ψ[t]) is a 3x3 rotation matrix. is the unit coordinate in the coordinate system before rotation, is the unit coordinate in the new coordinate system after rotation;

[0079] S25: The drone calculates the new coordinate system after rotation and obtains the position coordinates of the user receiver (i.e., the i-th user) updated as:

[0080]

[0081] S26: The azimuth and pitch angles of the coordinate axes of the rotating transmitting antenna array calculated by the UAV can be:

[0082]

[0083] In step S26, represents the vector connecting the drone and user i at the current time t, ∈[0,2π] represents the updated azimuth angle between the drone and user i at time t (the angle between the projection of the direction vector between the drone and the user on the xy plane of the drone's own coordinate system and the positive semi-axis of the y-axis in the drone's own coordinate system). Indicates the updated pitch angle (the angle between the direction vector between the drone and the user and the negative half-axis of the z-axis in the drone's own coordinate system);

[0084] S3: The UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel;

[0085] S31: The UAV obtains the steering vector matrix after attitude adjustment based on the updated azimuth and pitch angle:

[0086]

[0087] S32: The UAV obtains the coupling relationship expression between attitude and communication channel quality, and substitutes it into the formula to obtain the channel gain under the above model and the system ASE in time slot t It is used to form the objective function of the problem to be controlled in the subsequent content:

[0088]

[0089] In step S32, represents the additive complex Gaussian white noise of user i in time slot t, σ 2 represents the noise power, the variable η i[ t ] is the transmission power of the UAV to user i in time slot t;

[0090] S4: The drone obtains the real-time data of the system through the sensors placed in the system and calculates the current status of the communication system;

[0091] S41: Based on the existing MPC online control strategy, it is required to solve in the current MPC iteration time slot t0:

[0092] The open-loop control problem posed within the field of view:

[0093]

[0094] x[t+1]=f(x[t],u[t]),

[0095] g(x[t],u[t])=0,

[0096] h(x[t],u[t])≤0,

[0097]

[0098] In step S41, l(·) and E(·) are the cost function (corresponding to the first three terms in the objective function of formula (14)) and the terminal constraint function (corresponding to the last term in the objective function of formula (14)), respectively, corresponding to the first three terms and the last term in the objective function of the formula. For the constraints in the above nonlinear programming problem, g(·) describes the dynamic equations of the UAV control system and h(·) describes all the inequality constraints contained therein;

[0099] S42: The drone obtains the inequality through the above function:

[0100]

[0101] S43: Based on the interior point method, the UAV introduces a smoothing constant τ>0 and a relaxation variable sj,j={1,…,nh} to improve the sensitivity of the Newton method to the initial value, and the conditions for nonlinear programming are obtained as follows:

[0102]

[0103] G(w * )=0,

[0104] 0(w)-0,

[0105]

[0106] S44: The UAV optimizes the variable vector according to the conditional function of nonlinear programming to ensure that the UAV can accurately reach the destination and adjust its posture as much as possible to provide the maximum communication service for the ground user;

[0107] S5: The drone uses the communication channel system to perform control without the intervention of the command center;

[0108] S51: Both user status information and drone status information can be accurately obtained through sensors;

[0109] In step S51, the matrix antenna array can provide communication services to multiple ground users simultaneously, while satisfying the minimum communication requirements of the users and the maximum power loss constraints of the UAV;

[0110] S52: UAV flight control reads PID control algorithm;

[0111] In step S52, the PID control algorithm can achieve precise control of the UAV's attitude by adjusting the three parameters of proportion (P), integration (I) and differentiation (D), so that the UAV can quickly and stably reach or maintain a predetermined flight attitude under various external interferences and internal parameter changes;

[0112] S53: UAV uses PID attitude control algorithm to measure the attitude information of the UAV

[0113] In step S53, the attitude information is pitch, roll and yaw angles;

[0114] S54: The UAV uses Kalman filtering and particle filtering to obtain the position and speed of the UAV in an environment with poor GPS signals.

[0115] In step S54, these algorithms are combined with PID control and the like through Kalman filtering and particle filtering to achieve more accurate and stable flight control;

[0116] S55: The drone adjusts the motor output of the drone in real time according to the command and environmental feedback to achieve the desired flight attitude target;

[0117] S6: Collaborate with multiple drones for networked shooting;

[0118] S61: All drones in the drone group send their own serial number (ID) and connectivity (DC) information to neighboring nodes;

[0119] S62: Each drone compares the known DC and the received DC, updates its own maximum DC and sends this information to surrounding neighboring nodes;

[0120] S63: The node with the maximum connectivity in the UAV is used as the first cluster head and together with its neighborhood, it forms the first sub-cluster and continues to divide the next cluster head;

[0121] In step S63, the clusters divided by each cluster head of the drone need to have at least four common nodes with the first cluster, and in order to improve the efficiency of network division and avoid dividing redundant clusters, the second cluster can contain as many nodes that are not divided into clusters as possible;

[0122] S64: continuously selecting the next cluster head to form sub-clusters until all drones in the drone network are divided into corresponding clusters;

[0123] S65: After completing clustering, the UAVs measure distance and angle with each other, and send the measured distance and angle to the cluster head UAV and calculate the vector matrix;

[0124] S66: The drone uses the available GPS coordinates and the global coordinates of all drones to perform coordinate transformation using the Procrustes analysis algorithm;

[0125] S67: The UAV uses absolute coordinates and relative coordinates to photograph the corresponding coordinate position of the first cluster head;

[0126] S7: The highest priority is to return home when the drone is low on battery;

[0127] S71: The drone continuously updates the current battery status based on sensors;

[0128] S72: The drone generates an optimal reference return path based on A* algorithm planning;

[0129] S73: After the drone has a reference return path, it flies according to the minimum turning radius and roll angle restrictions.

[0130] 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. A remote control communication system for a drone after a police call, characterized in that: Including a drone for collecting information on the alarm scene, the drone mainly uses multiple communication antennas, and is equipped with several single-antenna mobile users or command centers, including the following steps: S1: The UAV establishes a communication channel with the ground user or command center; S2: The drone starts to adjust its position and couples its pitch angle, yaw angle and communication channel. S3: The UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel; S4: The drone obtains the real-time data of the system through the sensors placed in the system and calculates the current status of the communication system; S5: The drone uses the communication channel system to perform control without the intervention of the command center; S6: The drone cooperates with multiple adjacent drones to perform networked shooting; S7: The drone returns to home with the highest priority when the battery is low.

2. The remote control communication system for the unmanned aerial vehicle after the police dispatch according to claim 1 is characterized in that: The UAV establishes a communication channel with the ground user or the command center, specifically: S11: The UAV obtains the flight altitude through the air pressure altitude sensor and makes the diameter of the area covered by the ground less than 100m; S12: The communication link between the UAV and the ground user node is composed of direct signal components, that is, the communication model is a Rice fading channel; S13: The channel gain between the drone and the ground can be expressed as: S14: The drone obtains the user's GPS location and uses g i[ t ] Follows Rice distribution and has normalized power get: S15: The drone calculated and acquired a stable communication channel.

3. The remote control communication system for the unmanned aerial vehicle after the police dispatch according to claim 2 is characterized in that: The UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel, specifically: S21: The adjustment position of the drone causes the transmitting antenna to rotate and change; S22: The azimuth and elevation angles of the direct link vector between the UAV and the ground user change; S23: The drone starts calculating the rotated coordinate axis and defines x to represent the component of the rotating coordinate system of the transmitting antenna; S24: The unit direction vector of the new coordinate system of the transmitting antenna array of the UAV after rotation can be expressed as: S25: The drone calculates the new coordinate system after rotation and obtains the position coordinates of the user receiver (i.e., the i-th user) updated as: S26: The azimuth and pitch angles of the coordinate axes of the rotating transmitting antenna array calculated by the UAV can be:

4. The remote control communication system for the unmanned aerial vehicle after the police dispatch according to claim 3 is characterized in that: The UAV calculates the coupling function between the three angle states of pitch angle and yaw angle and the communication channel, specifically: S31: The UAV obtains the steering vector matrix after attitude adjustment based on the updated azimuth and pitch angle: S32: The UAV obtains the coupling relationship expression between attitude and communication channel quality, and substitutes it into the formula to obtain the channel gain under the above model And the system in time slot t It is used to form the objective function of the problem to be controlled in the subsequent content:

5. The remote control communication system for the unmanned aerial vehicle after the police dispatch according to claim 4 is characterized in that: The drone obtains real-time data of the system through sensors installed in the system and calculates the current state of the communication system, specifically: S41: Based on the existing MPC online control strategy, it is required to solve in the current MPC iteration time slot t0: The open-loop control problem posed within the field of view: x[t+1]=f(x[t],u[t]), g(x[t],u[t])=0, h(x[t],u[t])≤0, S42: The drone obtains the inequality through the above function: S43: The drone introduces a smoothing constant τ>0 and a relaxation variable s according to the interior point method. j ,j={1,…,n h }, which is used to improve the sensitivity of the Newton method to the initial value, and the conditions for nonlinear programming are: G(w * )=0, 0(w)-0, S44: The UAV optimizes the variable vector according to the conditional function of nonlinear programming to ensure that the UAV can accurately reach the destination and adjust its posture as much as possible to provide the maximum communication service to the ground users.

6. The remote control communication system for the unmanned aerial vehicle after the police dispatch according to claim 5 is characterized in that: The UAV uses the communication channel system to perform control with the intervention of the command center, specifically: S51: Both user status information and drone status information can be accurately obtained through sensors; S52: UAV flight control reads PID control algorithm; S53: UAV uses PID attitude control algorithm to measure the attitude information of the UAV S54: The UAV uses Kalman filtering and particle filtering to obtain the position and speed of the UAV in an environment with poor GPS signals; S55: The drone adjusts the motor output of the drone in real time according to the instructions and environmental feedback to achieve the desired flight attitude target.

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