Underground goaf drilling load recoverable unmanned aerial vehicle group detection system and method

Abstract

The invention relates to the field of underground detection, and discloses an underground goaf drilling load recoverable unmanned aerial vehicle group detection system and method, and the system comprises a drilling load platform and an unmanned aerial vehicle group. The drilling and loading platform is used for launching and recycling the unmanned aerial vehicle group, and performing unmanned aerial vehicle navigation control and data processing; according to the method, the navigation and path planning unit, the multi-modal data fusion unit and the unmanned aerial vehicle scheduling and task distribution unit are mainly used for data processing, and underground goaf three-dimensional modeling is obtained. According to the invention, the efficiency, precision and equipment utilization rate of underground goaf detection are obviously improved. A super-large-scale goaf can be quickly covered; and through a multi-modal data fusion technology, the recognition capability of complex geological features is improved. Besides, the design of the unmanned aerial vehicle recovery device realizes the efficient and repeated utilization of the equipment, the equipment loss and the detection cost are reduced, in addition, the system maintains high stability and adaptability in a changeable underground environment through high-precision navigation and intelligent scheduling, and a reliable detection scheme is provided for underground engineering.

Description

Technical Field

[0001] This invention relates to the field of underground exploration, and in particular to a method, equipment, and medium for drilling and reusable UAV swarms in underground voids. Background Technology

[0002] Detection of underground voids is a crucial technological aspect of geological exploration and underground engineering, especially in mining areas, underground tunnels, and geological disaster management. The accuracy and efficiency of large-scale void detection directly impact construction safety and cost. However, current detection technologies primarily rely on lidar and ground-penetrating radar (GPR). While these technologies can provide high-resolution geological data, their limited range and decreased accuracy in complex terrain are due to the deployment of equipment on fixed platforms. For example, some commercial lidar systems can generate 3D models of voids, but these are expensive and only applicable to specific terrains. Furthermore, while GPR can provide geological information at a certain depth, its accuracy is significantly affected by signal attenuation during void detection. In recent years, some studies have attempted to introduce single-unit UAVs for detection. This approach uses multiple sensors (such as lidar and gas sensors) to collect void data. However, single-unit detection has limited coverage, making it difficult to meet the complex needs of ultra-large-scale voids. Additionally, single-unit UAVs have short endurance, are difficult to recover, and experience high equipment wear and tear.

[0003] Existing technologies, when applied to ultra-large-scale airspace exploration, suffer from major drawbacks such as limited coverage, poor data integrity, low equipment utilization, and insufficient collaborative operation capabilities. In recent years, unmanned aerial vehicle (UAV) technology has demonstrated great potential in the field of exploration, especially the collaborative capabilities of UAV swarms, which provide a new solution for improving exploration efficiency and accuracy. However, applying UAV swarm technology to ultra-large-scale underground airspace exploration still faces technical challenges such as insufficient navigation accuracy, difficulties in data fusion, and complex equipment recovery. Summary of the Invention

[0004] The purpose of this invention is to propose a reusable UAV swarm detection system and method for underground airspace drilling, which solves the technical problems of insufficient navigation accuracy, difficulty in data fusion, and complex equipment recovery in existing underground airspace detection systems.

[0005] Specifically, the present invention provides a reusable unmanned aerial vehicle (UAV) swarm detection system for drilling underground airspace, comprising:

[0006] Drilling platforms and drone swarms; The drilling platform is used for launching and recovering drone swarms, drone navigation control, and data processing.

[0007] A method for detecting underground voids using a swarm of reusable unmanned aerial vehicles (UAVs), applied to the system, includes the following steps: S1. Deploy the drilling platform to the target underground area; S2. Deploy the drone swarm using the drone scheduling and task allocation unit in the control module; S3. The drone launcher in the drilling platform launches the drone swarm, which flies in the airspace according to the preset path or real-time calculated path of the navigation and path planning unit. S4. When the drone swarm is performing a mission, it uses multimodal sensors to collect environmental data. S5. After the environmental data is acquired by the data acquisition unit, it is processed by the multimodal data fusion unit to generate a complete three-dimensional model of the airspace. S6. After completing its mission, the drone swarm returns to the drilling platform and is captured by the drone recovery device. It is then powered by the power supply module for use in the next flight mission.

[0008] The beneficial effects provided by this invention are as follows: It significantly improves the efficiency, accuracy, and equipment utilization of underground airspace detection. Compared with traditional technologies, the system, through multi-UAV collaborative operation, increases detection efficiency by more than 2 times, enabling rapid coverage of ultra-large-scale airspaces; through multimodal data fusion technology, the resolution of the 3D model is improved by approximately 30%, significantly enhancing the ability to identify complex geological features. The design of the UAV recovery device enables efficient reuse of equipment, reducing equipment wear and tear and detection costs, saving approximately 40% of operating expenses. Furthermore, through high-precision navigation and intelligent scheduling, the system maintains high stability and adaptability in the ever-changing underground environment, providing a reliable detection solution for underground engineering. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the catapult structure of the present invention; Figure 3 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0011] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0012] Please refer to Figure 1 The present invention provides a reusable unmanned aerial vehicle (UAV) swarm detection system for drilling underground airspace, comprising: Drilling platforms and drone swarms; The drilling platform is used for launching and recovering drone swarms, drone navigation control, and data processing.

[0013] It should be noted that the drilling platform includes: a drone launch device, a drone recovery device, and a power supply module.

[0014] The launcher releases the drone into the target airspace via a catapult mechanism, and the recovery device (such as a magnetic grab net or a guided capture device) ensures the safe recovery of the drone after it completes its mission.

[0015] Please refer to Figure 2 ,like Figure 2 As shown, the ejection mechanism in this invention adopts a spring compression type ejection mechanism, which mainly includes: Base 201: Fixed to the drilling platform frame, serving as the mounting base for various components; Guide slider 202: slides along guide rail 203 on the base to support the drone; Energy storage spring assembly 204: Located at the rear of the slider, it is pre-tightly connected to the base, and the slider is pushed to the pre-compression position by manual or motor drive; Locking pin 205: It engages with the protrusion on the side of the slider and cooperates with the locking hole of the base to lock the slider. Release actuator 206: In this embodiment, an electromagnet is used. When the control unit outputs a release signal, the electromagnet demagnetizes and the locking pin is released. Buffer and shock absorber 207: Installed at the front end of the guide rail, it is used to absorb the impact of the slider falling to the bottom and protect the structure of the drone.

[0016] The working process of the ejection mechanism mainly includes: 1. Preloading stage: The slider 202 is pushed along the guide rail 203 to a position relatively close to the base by the motor or handle, so that the energy storage spring 204 is in a compressed state. The locking pin 205 is inserted into the slider locking hole to lock the slider.

[0017] 2. Launch Phase: The control module issues a "launch" command, de-energizing and demagnetizing actuator 206, releasing the locking pin 205, and causing spring 204 to rapidly release energy, propelling slider 202 forward at high speed along the guide rail. The UAV mounted on the slider is then ejected from the drilling platform and enters the target airspace along a predetermined flight attitude.

[0018] 3. Reset phase: After the slider 202 reaches the front end of the guide rail, it is slowed down and stopped by the buffer block 207; then the motor or manual pulls the slider back to the back pressure position, reloads the spring 204 and locks it, in preparation for the launch of the next drone.

[0019] This implementation method features a simple structure and a clear power source, making it suitable for scenarios with limited underground space and complex operating environments. Those skilled in the art can select appropriate specifications of springs, guide rails, and electromagnets based on the above component layout and working principle to manufacture and debug the overall catapult mechanism.

[0020] It should be noted that the drilling platform also includes a control module, which consists of a navigation and path planning unit, a multimodal data fusion unit, and a UAV scheduling and task allocation unit.

[0021] It should be noted that the drilling platform also includes a data acquisition unit, which is used to receive data collected by the drone swarm.

[0022] It should be noted that the drone swarm is equipped with multimodal sensors and an inertial navigation and communication module. The inertial navigation and communication module enables autonomous airspace detection and data transmission, and integrates a fast-charging interface to support rapid deployment and maintenance after mission completion.

[0023] It should be noted that the multimodal sensors include: lidar, ground-penetrating radar, and gas sensors.

[0024] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the method flow of the present invention; A method for detecting underground voids using a swarm of reusable unmanned aerial vehicles (UAVs), applied to an underground void detection system, includes the following steps: S1. Deploy the drilling platform to the target underground area; S2. Deploy the drone swarm using the drone scheduling and task allocation unit in the control module; S3. The drone launcher in the drilling platform launches the drone swarm, which flies in the airspace according to the preset path or real-time calculated path of the navigation and path planning unit. S4. When the drone swarm is performing a mission, it uses multimodal sensors to collect environmental data. S5. After the environmental data is acquired by the data acquisition unit, it is processed by the multimodal data fusion unit to generate a complete three-dimensional model of the airspace. S6. After completing its mission, the drone swarm returns to the drilling platform and is captured by the drone recovery device. It is then powered by the power supply module for use in the next flight mission.

[0025] It should be noted that in this invention, the navigation and path planning unit combines inertial navigation and reference beacons provided by the platform to achieve high-precision positioning and path planning for the UAV. The multimodal data fusion unit generates a complete three-dimensional model of the airspace by fusing laser point cloud, radar waveform and gas concentration information; The UAV scheduling and task allocation unit uses intelligent scheduling algorithms to optimize the flight path and task allocation of UAVs based on airspace shape and task priority, thereby improving detection efficiency.

[0026] The navigation and path planning unit employs a path planning method based on the A algorithm (or Dijkstra's algorithm). It constructs a 3D model of the underground airspace using environmental point cloud data acquired by LiDAR, and combines this with an inertial measurement unit (IMU) and local reference beacons deployed on the platform to achieve high-precision positioning and path planning for the UAV. The specific implementation steps are as follows: First, real-time underground environmental data is acquired using LiDAR to form a 3D point cloud; then, based on this 3D environmental model, Through A The algorithm searches for the optimal flight path for the drone; during flight, IMU data is used to correct the drone's position in real time to ensure the accuracy and continuity of the path planning.

[0027] To further improve the system's accuracy in processing environmental data, this embodiment employs an Extended Kalman Filter (EKF) algorithm in the multi-modal data fusion unit to fuse data from multiple sources, including lidar, ground-penetrating radar, and gas sensors. Specifically, the system collects data from each sensor separately; for example, the distance data output by the lidar is... The signal strength of the ground-penetrating radar is S_G; the data fusion process updates the state estimate through weighted averaging and Kalman filtering.

[0028] For the UAV scheduling and task allocation unit, this embodiment adopts a scheduling strategy based on a greedy algorithm. Before a task begins, the system first comprehensively evaluates each task based on its priority, distance to the target area, and the current status of the UAV (such as remaining battery power). Then, the greedy algorithm is used to allocate tasks to the most suitable UAVs. During task execution, the system monitors the status of each UAV in real time and dynamically adjusts the task allocation according to the task progress and recovery time to ensure that each UAV can complete the task in the best condition and be safely recovered.

[0029] In short, the navigation and path planning unit uses an improved A algorithm combined with EKF fusion positioning technology to construct a 3D point cloud map in real time using LiDAR, and then performs path planning after voxel filtering (voxel size 0.2m³). A The algorithm introduces a geological stability weight (1.0-2.0) to optimize path selection, and utilizes EKF to fuse IMU data with ultrasonic beacon ranging to achieve a positioning error of <0.5m. The data fusion unit establishes a state vector (position, attitude, sensor deviation) through EKF, performing spatiotemporal registration and weighted fusion of lidar distance (d_L), ground-penetrating radar signal strength (S_G), and gas concentration (C). The weight coefficients are adaptively adjusted according to environmental complexity. Task scheduling employs a dynamic priority greedy algorithm, comprehensively evaluating hazard (40%), distance (30%), and UAV status (30%), combined with real-time power monitoring (threshold 20%) to implement task handover or reallocation, ensuring system coverage >95%.

[0030] The key technical points of this invention are reflected in the following aspects: 1. Multi-UAV collaborative operation: Through intelligent scheduling algorithms and path planning, the UAV swarm can achieve efficient coverage and dynamic division of labor in ultra-large airspace.

[0031] 2. High-precision navigation and positioning: The method of combining inertial navigation with reference beacons solves the problem of insufficient positioning accuracy in underground environments.

[0032] 3. Multimodal data fusion: Integrating lidar, ground-penetrating radar and gas sensors, fusing multi-source data to generate a high-resolution three-dimensional airspace model.

[0033] 4. Rapid recovery and reuse: A recovery device and fast charging interface based on the drilling platform were designed to ensure the safe recovery and rapid deployment of the drone.

[0034] The beneficial effects of this invention are as follows: It significantly improves the efficiency, accuracy, and equipment utilization of underground airspace detection. Compared with traditional technologies, the system, through multi-UAV collaborative operation, increases detection efficiency by more than 2 times, enabling rapid coverage of ultra-large-scale airspaces. Through multimodal data fusion technology, the resolution of the 3D model is improved by approximately 30%, significantly enhancing the ability to identify complex geological features. The UAV recovery device design enables efficient reuse of equipment, reducing equipment wear and tear and detection costs, saving approximately 40% of operating expenses. Furthermore, through high-precision navigation and intelligent scheduling, the system maintains high stability and adaptability in variable underground environments, providing a reliable detection solution for underground engineering.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A reusable unmanned aerial vehicle (UAV) swarm detection system for drilling underground airspaces, characterized in that: include: Drilling platforms and drone swarms; The drilling platform is used for launching and recovering drone swarms, drone navigation control, and data processing.

2. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 1, characterized in that: The drilling platform includes: a drone launcher, a drone recovery unit, and a power supply module.

3. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 1, characterized in that: The drilling platform includes a control module, which consists of a navigation and path planning unit, a multimodal data fusion unit, and an UAV scheduling and task allocation unit.

4. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 3, characterized in that: The control module also includes a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to realize the functions of the navigation and path planning unit, the multimodal data fusion unit, and the UAV scheduling and task allocation unit.

5. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 1, characterized in that: The drilling platform includes a data acquisition unit for receiving data collected by a swarm of drones.

6. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 1, characterized in that: The drone swarm is equipped with multimodal sensors and inertial navigation and communication modules.

7. The underground airspace drilling and recovery unmanned aerial vehicle swarm detection system as described in claim 6, characterized in that: The multimodal sensors include: lidar, ground-penetrating radar, and gas sensors.

8. A method for detecting underground voids using a swarm of reusable unmanned aerial vehicles (UAVs), applied to an underground void detection system as described in any one of claims 1 to 7, characterized in that: The method includes the following steps: S1. Deploy the drilling platform to the target underground area; S2. Deploy the drone swarm using the drone scheduling and task allocation unit in the control module; S3. The drone launcher in the drilling platform launches the drone swarm, which flies in the airspace according to the preset path or real-time calculated path of the navigation and path planning unit. S4. When the drone swarm is performing a mission, it uses multimodal sensors to collect environmental data. S5. After the environmental data is acquired by the data acquisition unit, it is processed by the multimodal data fusion unit to generate a complete three-dimensional model of the airspace. S6. After completing its mission, the drone swarm returns to the drilling platform and is captured by the drone recovery device. It is then powered by the power supply module for use in the next flight mission.

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