An air-ground unmanned early warning monitoring system

Abstract

This utility model provides an air-to-ground unmanned early warning and monitoring system, comprising: an unmanned aerial vehicle (UAV) swarm: including multiple UAVs, each UAV further comprising a UAV transmission module, a UAV controller, an UAV onboard processor, a gimbal camera, and a positioning module. The UAV controller is mounted on the UAV, and the UAV onboard processor is an onboard computer (Orin) mounted on the UAV. A GPS antenna is mounted on the top of each UAV. This utility model uses a heterogeneous collaborative architecture as the core of the air-to-ground unmanned early warning and monitoring system. By complementing the wide-area field of view of UAVs with the local details of unmanned vehicles, it eliminates monitoring blind spots. It adopts a lightweight vision solution to achieve accurate perception and avoids high-cost load. Relying on a cross-platform control framework to dynamically schedule the linkage of air-to-ground equipment, it significantly improves the efficiency and real-time performance of early warning.

Description

Technical Field

[0001] This utility model belongs to the field of remote monitoring, specifically an air-to-ground unmanned early warning and monitoring system. Background Technology

[0002] In the field of air-to-ground unmanned early warning and monitoring, unmanned systems are needed to monitor and warn of potential threats (such as fires or abnormal events) in real time, output accurate location information of targets, and be robust in complex and ever-changing environments.

[0003] Current solutions mostly employ a single system to operate independently, primarily using the top-down view of UAVs to acquire large-scale global images for monitoring, or the ground-based view of unmanned vehicles to acquire local high-resolution images for monitoring. These methods all suffer from the drawback that global coverage and local accuracy cannot be achieved simultaneously, leading to technical defects such as target omissions, false alarms, and response delays when unmanned systems perform early warning and monitoring tasks.

[0004] Current mainstream unmanned early warning and monitoring systems often rely on the independent operation of a single type of unmanned platform. In this mode, drones are limited by their high-altitude, top-down perspective, making it difficult to capture detailed features or bottom information of targets in the images they obtain; while unmanned vehicles, due to their ground altitude, have a small field of view and are easily obstructed by terrain undulations, vegetation, or buildings, rendering them ineffective against targets at high altitudes or at great distances. This inherent limitation in perspective makes it difficult for a single system to balance monitoring coverage and local identification accuracy when performing tasks. When facing complex environments or partially obscured targets, the accuracy and stability of identification and positioning decrease significantly, easily leading to missed and false alarms.

[0005] In summary, this utility model provides an air-to-ground unmanned early warning and monitoring system to solve the above problems. Utility Model Content

[0006] To address the aforementioned technical problems, this utility model provides an air-to-ground unmanned early warning and monitoring system, which solves the technical defects in the prior art that cause unmanned systems to easily miss targets, make false alarms, and experience response delays when performing early warning and monitoring tasks.

[0007] An air-to-ground unmanned early warning and monitoring system includes: Drone swarm: includes multiple drones, each drone further including a drone transmission module, a drone controller, a drone onboard processor, a gimbal camera, and a positioning module. The drone controller is mounted on the drone, the drone onboard processor is an Orin onboard computer mounted on the drone, and a GPS antenna is mounted on the top of the drone. Unmanned vehicle swarm: includes multiple unmanned vehicles, each of which further includes an unmanned vehicle transmission module, an unmanned vehicle controller, an unmanned vehicle onboard processor, and a depth camera; Ground station: includes a control terminal, which includes a location information acquisition module, a sensor image acquisition module, a target recognition module, and an early warning module.

[0008] Furthermore, the drone is equipped with four motors, the output shafts of which are respectively connected to propellers. A gimbal is mounted on the bottom of the drone, and a gimbal camera is mounted on the gimbal. The drone controller includes a Wi-Fi module and a magnetic module. The drone is also equipped with a power battery, an electronic speed controller, and an altitude-holding radar.

[0009] Furthermore, the UAV transmission module includes a five-port gigabit switch and a receiver, which are installed on the UAV. The UAV transmission module is connected to the UAV controller, and the UAV controller communicates wirelessly with the ground station through the UAV transmission module.

[0010] Furthermore, the unmanned vehicle controller and the unmanned vehicle onboard processor are installed on the unmanned vehicle. The unmanned vehicle is equipped with a five-port gigabit switch. The unmanned vehicle controller includes a Wi-Fi module and 4G. The unmanned vehicle controller is equipped with the unmanned vehicle main controller. The unmanned vehicle controller is connected to the unmanned vehicle battery. The unmanned vehicle is also equipped with a GPS antenna and a receiver.

[0011] Furthermore, the depth camera is mounted on the unmanned vehicle, and the depth camera is a D435i depth camera.

[0012] Furthermore, the positioning module is connected to the GPS antennas of both the unmanned vehicle and the drone.

[0013] Furthermore, both the unmanned vehicle transmission module and the drone transmission module adopt Wi-Fi communication modules.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This utility model, by setting up a distributed transmission module on each device for real-time networking and data collaborative sharing, a motion control module for autonomous navigation and collaborative motion scheduling, a visual sensor device for multi-angle visual data acquisition, and a high-speed processor for real-time image analysis and early warning decision-making, through seamless cooperation between air and ground devices, successfully solves the key problem of the incompatibility between global coverage and local accuracy, significantly reduces target missed detections, false alarms and response delays, and greatly improves the accuracy and real-time performance of monitoring and early warning in complex environments.

[0015] 2. This utility model uses a heterogeneous collaborative architecture as the core of an air-to-ground unmanned early warning and monitoring system. By complementing the wide-area vision of UAVs with the local details of unmanned vehicles, it eliminates monitoring blind spots; it adopts a lightweight vision solution to achieve accurate perception and avoids high cost load; and it relies on a cross-platform control framework to dynamically schedule the linkage of air and ground equipment, which significantly improves the efficiency and real-time performance of early warning. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of this utility model; Figure 2 This is a structural block diagram of the system control of this utility model; Figure 3 This is a structural block diagram of the unmanned aerial vehicle (UAV) controller of this utility model. Detailed Implementation

[0017] The embodiments of this utility model will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this utility model, but should not be used to limit the scope of this utility model.

[0018] like Figures 1-3 As shown, this utility model provides an air-to-ground unmanned early warning and monitoring system, comprising: Drone swarm: includes multiple drones, each drone further including a drone transmission module, drone controller, drone onboard processor, gimbal camera and positioning module. The drone controller is installed on the drone, the drone onboard processor is an onboard computer Orin installed on the drone, and a GPS antenna is installed on the top of the drone. Autonomous vehicle swarm: includes multiple autonomous vehicles, and each autonomous vehicle further includes an autonomous vehicle transmission module, an autonomous vehicle controller, an autonomous vehicle onboard processor, and a depth camera; Ground station: includes control terminal, which includes location information acquisition module, sensor image acquisition module, target recognition module, and early warning module.

[0019] As one embodiment of this utility model, the drone is equipped with four motors, the output shafts of which are respectively connected to propellers. A gimbal is installed at the bottom of the drone, and a gimbal camera is installed on the gimbal. The drone controller includes a Wi-Fi module and a magnetic module. The drone is also equipped with a power battery, an electronic speed controller, and an altitude-holding radar.

[0020] As one embodiment of this utility model, the UAV transmission module includes a five-port gigabit switch and a receiver. The five-port gigabit switch and receiver are installed on the UAV. The UAV transmission module is connected to the UAV controller, and the UAV controller communicates wirelessly with the ground station through the UAV transmission module.

[0021] In one embodiment of this utility model, an unmanned vehicle controller and an unmanned vehicle onboard processor are installed on the unmanned vehicle. The unmanned vehicle is equipped with a five-port gigabit switch. The unmanned vehicle controller includes a Wi-Fi module and 4G. The unmanned vehicle controller is equipped with an unmanned vehicle main controller. The unmanned vehicle controller is connected to the unmanned vehicle battery. The unmanned vehicle is also equipped with a GPS antenna and a receiver.

[0022] As one embodiment of this utility model, a depth camera is installed on an unmanned vehicle, and the depth camera is a D435i depth camera.

[0023] In one embodiment of this utility model, the positioning module is connected to the GPS antennas of the unmanned vehicle and the drone respectively.

[0024] As one embodiment of this utility model, both the unmanned vehicle transmission module and the drone transmission module adopt a Wi-Fi communication module.

[0025] It adopts an air-ground collaborative architecture consisting of 6 quadcopter drones with a wheelbase of 350mm and 2 differential drive unmanned vehicles.

[0026] The core equipment configuration is unified and highly targeted: all platforms (drones and unmanned vehicles) are equipped with the Finix3000 II flight controller as the core motion control unit, which enables high-precision autonomous navigation, formation flight / driving and coordinated motion scheduling.

[0027] At the data processing level, each device is equipped with an NVIDIA Orin onboard processor, providing powerful edge computing capabilities to support real-time video stream analysis, target identification and localization, threat assessment, and early warning monitoring decisions.

[0028] In terms of the perception system, each drone uses a SIYI A8 mini gimbal camera to ensure that it can acquire stabilized global perspective video data even when maneuvering at high altitudes. Each autonomous vehicle is equipped with a D435i depth camera for high-resolution local image capture and precise depth perception in complex terrain environments.

[0029] All platforms integrate Wi-Fi communication modules to build a high-bandwidth, low-latency self-organizing network, ensuring reliable and real-time information sharing (including sensor data, target location, early warning information, etc.) and rapid distribution of control commands between air and ground platforms.

[0030] Software and status feedback level: The system runs on the ROS2 (Robot Operating System 2) software framework and deeply integrates the PX4 open source flight control system to achieve precise low-level control of the drone's movement and unmanned vehicle navigation scheduling.

[0031] The core application software, built on ROS2, enables the fusion and analysis of sensing data, collaborative task planning, and ultimately, early warning and monitoring functions. The real-time operational status information of the entire air-to-ground unmanned early warning and monitoring system (including platform locations, sensor images, identified targets, warning levels, network topology, etc.) is centrally displayed and fed back through an integrated, visualized ground station and control terminal, providing operators with comprehensive situational awareness and convenient, efficient command and control capabilities.

[0032] By working collaboratively with drone swarms and unmanned vehicle swarms, wide-area coverage is provided in the air and detailed enhancements are provided on the ground. This enables real-time monitoring and high-precision positioning of potential threats (such as fires and abnormal targets) from multiple angles and levels. It effectively solves the problems of blind spots and insufficient accuracy caused by a single perspective, significantly improves the ability to detect and identify complex or partially obscured targets, significantly reduces the risk of missed and false alarms, and shortens response delay time.

[0033] By fully utilizing the computing power of the onboard NVIDIA Orin processor, efficient distributed fusion algorithms (such as improved Kalman filtering) are employed between air and ground platforms to fuse asynchronously acquired multi-source perception data, generating more robust and accurate target location estimates and threat level assessments.

[0034] The ROS2-based software architecture enables flexible task scheduling and unified data management. Combined with real-time visualization of the ground station and control terminal, it provides operators with global situational awareness, greatly improving the accuracy, real-time performance, and overall system task execution efficiency of monitoring and early warning. Meanwhile, the vision-based sensor configuration (gimbal camera + depth camera) significantly reduces system construction and maintenance costs and alleviates platform load compared to solutions relying on high-cost LiDAR.

[0035] The embodiments of this utility model are given for the purpose of illustration and description. Although embodiments of this utility model have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this utility model. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this utility model.

Claims

1. An air-to-ground unmanned early warning and monitoring system, characterized in that, include: Drone swarm: includes multiple drones, each drone further including a drone transmission module, a drone controller, a drone onboard processor, a gimbal camera, and a positioning module. The drone controller is mounted on the drone, the drone onboard processor is an Orin onboard computer mounted on the drone, and a GPS antenna is mounted on the top of the drone. Unmanned vehicle swarm: includes multiple unmanned vehicles, each of which further includes an unmanned vehicle transmission module, an unmanned vehicle controller, an unmanned vehicle onboard processor, and a depth camera; Ground station: includes a control terminal, which includes a location information acquisition module, a sensor image acquisition module, a target recognition module, and an early warning module.

2. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, The drone is equipped with four motors, the output shafts of which are connected to propellers. A gimbal is mounted on the bottom of the drone, and a camera is mounted on the gimbal. The drone controller includes a Wi-Fi module and a magnetic module. The drone is also equipped with a power battery, an electronic speed controller, and an altitude-holding radar.

3. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, The UAV transmission module includes a five-port gigabit switch and a receiver, which are installed on the UAV. The UAV transmission module is connected to the UAV controller, and the UAV controller communicates wirelessly with the ground station through the UAV transmission module.

4. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, The unmanned vehicle controller and the unmanned vehicle onboard processor are installed on the unmanned vehicle. The unmanned vehicle is equipped with a five-port gigabit switch. The unmanned vehicle controller includes a Wi-Fi module and 4G. The unmanned vehicle controller is equipped with the unmanned vehicle main controller. The unmanned vehicle controller is connected to the unmanned vehicle battery. The unmanned vehicle is also equipped with a GPS antenna and a receiver.

5. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, The depth camera is mounted on the unmanned vehicle, and the depth camera is a D435i depth camera.

6. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, The positioning module is connected to the GPS antennas of the unmanned vehicle and the drone, respectively.

7. The air-to-ground unmanned early warning and monitoring system as described in claim 1, characterized in that, Both the unmanned vehicle transmission module and the drone transmission module use Wi-Fi communication modules.

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