UAV positioning and navigation system and operation method suitable for underground dark environment

Abstract

This invention discloses a UAV positioning and navigation system and operation method suitable for underground dark environments, comprising: a perception subsystem, a control subsystem, and an execution subsystem; the perception subsystem, connected to the control subsystem, is used to assist the UAV in navigation and positioning based on multi-sensor fusion, acquiring environmental information and pose information; the control subsystem, connected to the execution subsystem, is used to acquire control signals for the UAV based on the environmental information and the pose information; the execution subsystem, connected to the perception subsystem, is used to receive the control signals and complete the positioning and navigation task based on the control signals. This system achieves UAV positioning and navigation in underground dark environments by comprehensively utilizing data information from multiple sensors. LED lighting is used for illumination, and a binocular depth camera and a two-dimensional LiDAR are used for environmental perception. The binocular depth camera and inertial sensors are used for map building and state estimation.

Description

Technical Field

[0001] This invention belongs to the field of UAV positioning and navigation, and in particular relates to a UAV positioning and navigation system and its operation method suitable for underground dark environments. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are increasingly used in military and civilian fields, with positioning and navigation being one of their most fundamental functions. The weak GPS signals and limited visibility in dark underground environments pose unique challenges to UAV positioning and navigation, potentially making safe and effective navigation difficult. Currently, with the development of UAV technology, mobile UAVs based on scene modeling, scene recognition, and path planning technologies are widely used in indoor and outdoor environments. However, in dark underground environments, the technologies for achieving positioning and navigation in such conditions, enabling UAVs to fly to designated locations, remain in the research stage and have not yet seen practical application.

[0003] Currently, in enclosed environments such as underground darkness, sensors like LiDAR and infrared sensors are mainly used for environmental perception and map building, followed by planning algorithms for path planning and control command generation. However, in underground darkness, the effectiveness of sensors like LiDAR and infrared sensors is greatly limited. Traditional UAV positioning and navigation systems struggle to achieve accurate environmental perception and state estimation, leading to instability and safety issues in UAV positioning and navigation. Therefore, a new UAV positioning and navigation system is needed to address this problem. Summary of the Invention

[0004] The purpose of this invention is to provide a drone positioning and navigation system and operation method suitable for underground dark environments, so as to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides a drone positioning and navigation system suitable for underground dark environments, comprising:

[0006] Sensing subsystem, control subsystem, execution subsystem;

[0007] The perception subsystem is connected to the control subsystem and is used to provide assistance for the UAV to navigate and position based on multi-sensor fusion, and to acquire environmental information and pose information.

[0008] The control subsystem is connected to the execution subsystem and is used to acquire control signals for the UAV based on the environmental information and the pose information.

[0009] The execution subsystem is connected to the perception subsystem and is used to receive the control signal and complete the positioning and navigation task based on the control signal.

[0010] Preferably, the sensing subsystem includes:

[0011] Illumination source, binocular depth camera, inertial sensor, 2D LiDAR;

[0012] The lighting source is used to illuminate the dark underground environment, providing a stable light environment within a limited range;

[0013] The binocular depth camera is used to capture depth images of the underground dark environment, and to process the depth images to obtain the UAV pose changes and visual images, and to obtain the UAV's position and attitude based on the UAV pose changes.

[0014] The inertial sensor is used to optimize the position and attitude of the UAV based on the visual image and inertial sensor constraints to obtain the pose information;

[0015] The two-dimensional lidar is used to acquire two-dimensional point clouds of underground dark environments, and to filter and extract boundaries of the two-dimensional point clouds of underground dark environments to obtain environmental information.

[0016] Preferably, the lighting source is located at the ends of the four arms of the UAV, illuminating the four directions respectively, and the lighting directions of the lighting sources on adjacent arms are perpendicular to each other;

[0017] The binocular depth camera is located in the middle of the drone body, slightly towards the head, with the lens facing the direction of the drone's head.

[0018] The inertial sensor is located in the middle of the UAV body, and the center of the inertial sensor coincides with the center of the UAV body;

[0019] The two-dimensional lidar is located in the middle of the UAV body. The UAV body and the two-dimensional lidar are connected by a self-stabilizing platform, and the center of the two-dimensional lidar coincides with the center of the UAV body.

[0020] The self-stabilizing platform consists of a support frame, a self-stabilizing mechanism, and a top platform from bottom to top. The support frame is connected to the drone body below, the self-stabilizing mechanism is used to maintain the horizontal stability of the top platform, and the top platform is rigidly connected to the two-dimensional lidar.

[0021] Preferably, the control subsystem includes:

[0022] The decision module is used to solve the environmental information and obtain decision instructions;

[0023] The planning module is used to determine the UAV control signal based on the environmental information, the pose information, and the decision command.

[0024] Preferably, the execution subsystem sends execution information to the UAV based on the control signal, enabling the UAV to avoid local obstacles and track the global path to complete the positioning and navigation task.

[0025] Preferably, the execution information includes direction, speed, acceleration, heading angle, and trajectory.

[0026] The present invention also provides an operation method for a drone positioning and navigation system suitable for underground dark environments, comprising the following steps:

[0027] Initialize the drone to obtain a light-illuminating drone;

[0028] Based on the aforementioned illumination drone, binocular depth camera, and inertial sensor, pose information is obtained;

[0029] Environmental information is obtained based on the aforementioned illumination drone and two-dimensional lidar;

[0030] The pose information and the environmental information are sent to the terminal to obtain the work task;

[0031] Plan a global path based on the work task and the existing two-dimensional map of the underground dark environment;

[0032] Plan a local path based on the environmental information and the pose information;

[0033] The drone completed the aforementioned task.

[0034] Preferably, the process of obtaining light from the drone includes:

[0035] The drone is started by sending a command through the terminal, and the drone is initialized to obtain an initialized drone.

[0036] The initialization drone is configured with lighting to obtain the illuminated drone.

[0037] Preferably, the process of obtaining the pose information includes:

[0038] The binocular depth camera captures images of the dark underground environment to obtain depth images;

[0039] The depth image is processed to obtain the UAV's visual pose data and pose changes;

[0040] Acquire inertial pose data and pose changes of the UAV based on inertial sensors;

[0041] The position and attitude of the illuminated UAV in the underground dark environment are obtained by fusing the UAV's visual pose data and pose changes with the UAV's inertial pose data and pose changes and then performing nonlinear optimization processing.

[0042] The position and attitude of the illuminated UAV in the underground dark environment are optimized and then detected to obtain the pose information.

[0043] Preferably, the process of obtaining the environmental information includes:

[0044] The horizontal stability of the two-dimensional lidar is maintained by a self-stabilizing platform.

[0045] Two-dimensional point clouds of the underground dark environment are obtained based on the aforementioned two-dimensional lidar;

[0046] The two-dimensional point cloud of the underground dark environment is filtered and its boundaries are extracted to obtain a two-dimensional local map of the underground dark environment, i.e., the environmental information.

[0047] Preferably, the process of planning the global path includes:

[0048] The illumination drone uses a hybrid A* algorithm to obtain the globally optimal path for the operation based on the existing two-dimensional map of the underground dark environment and the work task.

[0049] Preferably, the process of planning a local path includes:

[0050] Based on the global optimal path, the environmental information, and the pose information, a local path is planned, and a fifth-order polynomial is used to optimize the local path to obtain a smooth local path.

[0051] The technical effects of this invention are as follows:

[0052] This invention provides a drone positioning and navigation system for use in underground, dark environments. The system achieves drone positioning and navigation in such environments by comprehensively utilizing data from multiple sensors. LED lighting is used for illumination, and a binocular depth camera and a 2D LiDAR are used for environmental perception. The binocular depth camera and inertial sensors are used for map building and state estimation. The positioning and navigation control algorithm employs a multi-sensor data fusion method. By filtering, fusing, and processing sensor data, more accurate and robust environmental perception and state estimation results are obtained. Control commands are then generated based on a control command generation algorithm to achieve drone positioning and navigation in underground, dark environments. The advantages of this invention are that it can adapt to the positioning and navigation requirements in underground, dark environments, improve the accuracy and robustness of environmental perception and state estimation, and enhance the safety and stability of drone positioning and navigation. Attached Figure Description

[0053] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0054] Figure 1 This is a framework diagram of the UAV positioning and navigation system in an embodiment of the present invention;

[0055] Figure 2 This is a diagram of the visual positioning method in an embodiment of the present invention;

[0056] Figure 3 This is a boundary extraction map of the underground environment in an embodiment of the present invention;

[0057] Figure 4 This is a flowchart of the operation method of the UAV positioning and navigation system in an embodiment of the present invention;

[0058] Figure 5 This is a layout diagram of the drone sensors in the drone positioning and navigation system according to an embodiment of the present invention. Detailed Implementation

[0059] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0060] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0061] Example 1:

[0062] like Figure 1 As shown, this embodiment provides a drone positioning and navigation system suitable for underground dark environments, including: a perception subsystem S1, an execution subsystem S2, and a control subsystem S3. The perception subsystem S1 relies on various sensors to provide environmental and pose information for the drone positioning and navigation system, assisting the drone in navigation and positioning. First, the drone uses its own equipped LED lighting source to provide stable illumination to the underground dark environment within a limited range; then, with the help of the LED lighting source, it uses a binocular depth camera S4 mounted on the drone to capture depth images of the underground dark environment, and performs preprocessing, feature point extraction, distortion correction, feature point matching, and other operations to obtain the drone's pose changes and construct a visual map, such as... Figure 2As shown. Simultaneously, based on data measured by the inertial sensor S5, the attitude change between adjacent frames is calculated and converted into camera pose change. Then, the pose changes from the visual and inertial front-ends are fused, and the position and attitude of the UAV in the underground dark environment are estimated through nonlinear optimization. The estimated position and attitude are optimized using visual maps and inertial sensor constraints to improve estimation accuracy. Finally, keyframes from the camera images are used for loop closure detection to reduce drift error, obtaining the final pose information after visual-inertial information fusion. Meanwhile, the 2D LiDAR S8 scans and obtains a 2D point cloud of the underground dark environment and performs filtering. The boundaries are extracted using the AlphaShapes algorithm to obtain a 2D local map of the underground dark environment, i.e., environmental information, facilitating subsequent constraints on local path planning, such as... Figure 3 As shown, the control subsystem S2 consists of a planning module S6 and a decision module S7. Using environmental and pose information from the perception subsystem, it calculates the control signals for the UAV. First, the decision module S7 determines the UAV's flight speed and altitude based on the environmental information. Within the decision-defined range, the planning module S6 combines the UAV's kinematic model, environmental information from the underground dark environment, UAV pose information, and navigation task solutions to obtain a smooth and continuous local flight path, and sends the speed and path information to the execution subsystem. The execution subsystem S2 receives the control signals from the control subsystem and sends execution information such as direction, speed, acceleration, heading angle, and flight path to the UAV, enabling the UAV to avoid local obstacles and track the global path, completing the positioning and navigation task.

[0063] Example 2:

[0064] This embodiment provides an operation method for a drone positioning and navigation system suitable for underground dark environments, including:

[0065] Step T1: The user sends a command through the terminal to start the drone, and the drone initializes.

[0066] Step T2: The drone activates the assembled LED lighting source to provide limited illumination;

[0067] Step T3: Acquire UAV pose data and environmental information, and send the pose data and environmental information to the terminal. This can be divided into the following steps:

[0068] Step K1: With the aid of LED lighting, the UAV captures depth images using a binocular depth camera. The images undergo preprocessing, feature point extraction, distortion correction, and feature point matching to obtain visual pose data and pose changes. Simultaneously, based on data measured by the inertial sensor, the inertial pose data and pose changes between adjacent frames are calculated and converted into camera pose changes. Then, the pose changes from the visual and inertial sensors are fused. Nonlinear optimization is used to estimate the UAV's position and attitude in the underground dark environment. Visual images and inertial sensor constraints are used to optimize the estimated position and attitude, improving estimation accuracy. Finally, keyframes from the camera images are used for loop closure detection to reduce drift errors, resulting in the final pose information after visual-inertial information fusion.

[0069] Step K2: The LED lighting source provides a stable lighting environment. The UAV obtains a two-dimensional point cloud of the underground dark environment through a two-dimensional LiDAR. By filtering and AlphaShapes boundary extraction, a two-dimensional local map of the underground dark environment, i.e., environmental information, is obtained.

[0070] Step K3: The UAV will send the pose information obtained by fusion of visual and inertial information and the environmental information obtained by boundary extraction to the terminal in real time through the mobile communication module;

[0071] Step T4: While executing step T3, the drone enters standby mode and checks if there is a task to execute. If so, it executes the task; otherwise, it remains in standby mode. This can be broken down into the following steps:

[0072] Step Y1: The drone enters standby mode, waiting for the user to issue the drone's work tasks through the terminal;

[0073] Step Y2: The drone checks if there is a work task. If not, it proceeds to step Y1. If there is a work task, the drone enters working mode and executes the following steps.

[0074] Step Y3: The UAV plans a global path based on the task and the existing two-dimensional map of the underground dark environment using a hybrid A* algorithm;

[0075] Step Y4: The UAV plans a local path based on the task, global path, environmental information, and pose information, and optimizes the local path using a fifth-order polynomial to obtain a smooth local path and avoid obstacles.

[0076] Step Y5: The drone reaches the designated target point and completes the positioning and navigation task. The drone then proceeds to step Y1.

[0077] like Figure 5This is the sensor layout of the perception subsystem in the UAV positioning and navigation system of the present invention. LED lighting sources are located at the ends of the four arms of the UAV, illuminating the four directions respectively, with the lighting directions of LED lighting sources on adjacent arms perpendicular to each other. A binocular depth camera is located in the middle of the UAV body, slightly towards the head, with its lens facing the direction the UAV's head is facing. An inertial sensor is located in the middle of the UAV body, with its center coinciding with the center of the UAV body. A two-dimensional LiDAR is located in the middle of the UAV body, connected to the UAV body via a self-stabilizing platform. The center of the two-dimensional LiDAR coincides with the center of the UAV body. The self-stabilizing platform consists of a support frame, a self-stabilizing mechanism, and a top platform from bottom to top. The support frame connects to the lower UAV body, the self-stabilizing mechanism maintains the horizontal stability of the top platform, and the top platform is rigidly connected to the two-dimensional LiDAR.

[0078] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A drone positioning and navigation system suitable for underground dark environments, characterized in that, include: Sensing subsystem, control subsystem, execution subsystem; The perception subsystem is connected to the control subsystem and is used to provide assistance for the UAV to navigate and position based on multi-sensor fusion, and to acquire environmental information and pose information. The control subsystem is connected to the execution subsystem and is used to acquire control signals for the UAV based on the environmental information and the pose information. The execution subsystem is connected to the perception subsystem and is used to receive the control signal and complete the positioning and navigation task based on the control signal; The sensing subsystem includes: Illumination source, binocular depth camera, inertial sensor, 2D LiDAR; The lighting source is used to illuminate the dark underground environment, providing a stable light environment within a limited range; The binocular depth camera is used to capture depth images of the underground dark environment, and to process the depth images to obtain the UAV pose changes and visual images, and to obtain the UAV's position and attitude based on the UAV pose changes. The inertial sensor is used to optimize the position and attitude of the UAV based on the visual image and inertial sensor constraints to obtain the pose information; The two-dimensional lidar is used to acquire two-dimensional point clouds of the underground dark environment, and to filter the two-dimensional point clouds of the underground dark environment. The Alpha Shapes algorithm is used to extract the boundaries to obtain the environmental information. The lighting sources are located at the ends of the four arms of the drone, illuminating the four directions respectively, and the lighting directions of the lighting sources on adjacent arms are perpendicular to each other; The binocular depth camera is located in the middle of the drone body, slightly towards the head, with the lens facing the direction of the drone's head. The inertial sensor is located in the middle of the UAV body, and the center of the inertial sensor coincides with the center of the UAV body; The two-dimensional lidar is located in the middle of the UAV body. The UAV body and the two-dimensional lidar are connected by a self-stabilizing platform, and the center of the two-dimensional lidar coincides with the center of the UAV body. The self-stabilizing platform consists of a support frame, a self-stabilizing mechanism, and a top platform from bottom to top. The support frame is connected to the drone body below, the self-stabilizing mechanism is used to maintain the horizontal stability of the top platform, and the top platform is rigidly connected to the two-dimensional lidar.

2. The UAV positioning and navigation system suitable for underground dark environments according to claim 1, characterized in that, The control subsystem includes: The decision module is used to solve the environmental information and obtain decision instructions; The planning module is used to determine the UAV control signal based on the environmental information, the pose information, and the decision command.

3. The UAV positioning and navigation system suitable for underground dark environments according to claim 1, characterized in that, Based on the control signals, the execution subsystem sends execution information to the UAV, enabling the UAV to avoid local obstacles and track the global path to complete the positioning and navigation task.

4. The UAV positioning and navigation system suitable for underground dark environments according to claim 3, characterized in that, The execution information includes direction, speed, acceleration, heading angle, and trajectory.

5. A method for operating a drone positioning and navigation system suitable for underground dark environments, used to implement the drone positioning and navigation system suitable for underground dark environments as described in any one of claims 1-4, characterized in that, Includes the following steps: The drone is started by sending a command through the terminal, and the drone is initialized to obtain an initialized drone. The initialization drone is configured with lighting to obtain a lit drone; Depth images are obtained by capturing images of the dark underground environment using a binocular depth camera; The depth image is processed to obtain the UAV's visual pose data and pose changes; The inertial pose data and pose changes of the UAV are obtained based on inertial sensors; the UAV visual pose data and pose changes are fused with the UAV inertial pose data and pose changes and then subjected to nonlinear optimization processing to obtain the position and attitude of the illuminated UAV in the underground dark environment. The position and attitude of the illuminated UAV in the underground dark environment are optimized and then detected to obtain pose information; The horizontal stability of the two-dimensional lidar is maintained by a self-stabilizing platform; a two-dimensional point cloud of the underground dark environment is acquired based on the two-dimensional lidar; the two-dimensional point cloud of the underground dark environment is filtered, and the boundaries are extracted by the Alpha Shapes algorithm to obtain a two-dimensional local map of the underground dark environment, i.e., environmental information. The pose information and the environmental information are sent to the terminal to obtain the work task; Based on the work task and the existing two-dimensional map of the underground dark environment, a hybrid A* algorithm is used to plan the global path and obtain the global optimal path for the operation. Based on the global optimal path, the environmental information, and the pose information, a local path is planned, and a fifth-order polynomial is used to optimize the local path to obtain a smooth local path. The drone completed the aforementioned task.

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