Unmanned aerial vehicle autonomous exploration control method based on integrated unmanned aerial vehicle platform

Abstract

The invention provides an unmanned aerial vehicle autonomous exploration control method based on an integrated unmanned aerial vehicle platform. The unmanned aerial vehicle autonomous exploration control method comprises the steps that S1, an unmanned aerial vehicle is controlled to take off on the integrated unmanned aerial vehicle platform; s2, obtaining self pose information of the unmanned aerial vehicle; s3, constructing a scene map; s4, receiving a target viewpoint identified by the unmanned aerial vehicle to generate an optimal polynomial trajectory curve, and controlling the unmanned aerial vehicle to perform region exploration along the optimal polynomial trajectory curve to update a scene map; s5, judging whether the size of the explored area meets a preset condition or not: if so, turning to step S6; if not, turning to step S4; and step S6, obtaining mark information of the integrated unmanned aerial vehicle platform captured by the camera to identify and obtain a landing relative position to generate a landing curve, and controlling the unmanned aerial vehicle to land on the integrated unmanned aerial vehicle platform along the landing curve and to cooperate with the charging assembly for charging. The unmanned aerial vehicle has the beneficial effects that precise autonomous taking-off and landing and intelligent charging can be achieved, and the autonomous exploration requirement in the complex environment is met.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle space environment perception, and in particular to an unmanned aerial vehicle autonomous exploration control method based on an integrated unmanned aerial vehicle platform. Background Art

[0002] The quadcopter unmanned aerial vehicle is a non-coaxial disc-shaped rotor aircraft in terms of rotor layout. The four rotors are symmetrically distributed in a cross shape. Compared with traditional single-rotor aircraft, the quadcopter has many advantages. The four rotors of the quadcopter offset the influence of each other's rotation, and there is no need for the tail rotor of the single-rotor aircraft, which is more energy-efficient and also reduces the size of the aircraft. The quadcopter adjusts the aircraft's attitude by adjusting the speed of the four rotors. There is no need for the propeller angle adjustment device of the single-rotor helicopter, and the mechanical design is simpler: the quadcopter has multiple rotors, so it has a larger load capacity, and the blades can also be made smaller, which is easy to miniaturize. It is precisely because the quadcopter has so many advantages that it has broad application prospects and research value.

[0003] In recent years, quadcopter drones have been widely used in many fields due to their flexibility and ease of manufacturing, including regional surveys, logistics distribution, exploration of unknown environments, and emergency rescue. In emergency rescue, drones have become important technical tools due to their rapid response and versatility. However, existing rescue drones still have significant limitations when performing complex tasks, especially in terms of endurance and operational continuity. Traditional drones rely on manual takeoff and landing and frequent battery replacement, which not only increases the operational burden, but may also delay critical rescue windows due to downtime. Therefore, solving the problems of autonomous takeoff and landing and charging of drones has become a key link in improving their rescue efficiency. Summary of the invention

[0004] The technical problem to be solved by the present invention is to achieve precise autonomous take-off and landing and intelligent charging to meet the needs of autonomous exploration in complex environments. In order to overcome the defects of the above-mentioned prior art (or related technology), the present invention provides a method for autonomous exploration control of a UAV based on an integrated UAV platform.

[0005] The present invention provides a method for autonomous exploration and control of a drone based on an integrated drone platform. An integrated drone platform is pre-built, a charging component is provided on the integrated drone platform, and a three-dimensional laser radar, an inertial sensor and a camera are installed on the drone. The method for autonomous exploration and control of the drone includes the following steps: Step S1, controlling the UAV to take off on the integrated UAV platform; Step S2, continuously acquiring the three-dimensional point cloud data output by the three-dimensional laser radar and the inertial measurement data output by the inertial sensor, and performing tight coupling fusion processing on the three-dimensional point cloud data and the inertial measurement data to obtain the self-pose information of the UAV; Step S3, constructing a scene map based on the three-dimensional point cloud data and the self-pose information; Step S4, receiving the target viewpoint identified by the drone, processing the drone's own pose information and the target pose information at the target viewpoint through a minimum control trajectory planning algorithm to generate an optimal polynomial trajectory curve, and controlling the drone to perform area exploration along the optimal polynomial trajectory curve to update the size of the explored area in the scene map; Step S5, determining whether the size of the explored area meets a preset condition: If yes, go to step S6; If not, go to step S4; Step S6, obtaining the mark information of the integrated UAV platform captured by the camera to identify the relative position of landing, and processing the UAV's own posture information and the target posture information at the relative position through a minimum control trajectory planning algorithm to generate a landing curve, and controlling the UAV to land on the integrated UAV platform along the landing curve and cooperate with the charging component for charging.

[0006] Compared with the prior art, the autonomous exploration control method of a drone based on an integrated drone platform in this application has the following advantages: In this application, visual guidance is used to complete the autonomous takeoff and landing of the drone, combined with the autonomous charging of the integrated drone platform, and multiple autonomous takeoffs and landings are used to increase the repeated exploration of a larger area with the same load. A low-latency positioning algorithm is used in combination with the perception and mapping of a three-dimensional lidar to complete area exploration or target point flight in unfamiliar environments with high real-time performance, and to autonomously complete various exploration tasks in areas that are inaccessible to humans, achieving precise autonomous takeoff and landing and intelligent charging, and meeting the needs of autonomous exploration in complex environments.

[0007] In a possible implementation, in step S2, a set of spatial coordinate points represented by X coordinates, Y coordinates, Z coordinates and intensity values ​​are collected by the three-dimensional laser radar as the three-dimensional point cloud data to provide three-dimensional spatial information of the surrounding environment of the drone.

[0008] In a possible implementation, in step S2, the inertial sensor detects acceleration and angular velocity including a time series as the inertial measurement data to describe the acceleration and rotational motion of the drone in various directions.

[0009] In a possible implementation, step S3 includes: Step S31, converting each point cloud in the three-dimensional point cloud data into a global coordinate system according to the self-pose information to perform spatial alignment of the three-dimensional point cloud data; Step S32, continuously updating the three-dimensional point cloud data during the movement of the drone, and constructing a dynamic three-dimensional environment model as the scene map through the drone's own posture information at each moment.

[0010] In a possible implementation manner, the step S32 further includes: The scene map is divided into occupied space, explored space and unexplored space. The occupied space represents the obstacle point cloud existing in the environment, the explored space represents the area that has been explored and has no obstacle point cloud, and the unexplored space represents the area that has not been explored or has no clear obstacle point cloud. In step S4, the size of the explored space is used as the size of the explored area.

[0011] In a possible implementation manner, in step S32, a boundary area is formed at a dividing line between the explored space and the unexplored space, and step S4 further includes: The optimal exploration trajectory from the current position of the drone to the optimal viewpoint of the boundary area is calculated by the A* algorithm, and the drone is controlled to follow the optimal exploration trajectory to the boundary area to update the scene map.

[0012] In a possible implementation manner, the preset condition in step S5 is that the size of the explored area reaches a preset threshold or the explored space is completely surrounded by the occupied space.

[0013] In a possible implementation, in step S6, the Aruco code on the integrated UAV platform is identified by the camera, and the target pose information at the relative position on the integrated UAV platform is obtained by a PnP transformation algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the steps of the present invention; Figure 2 This is a physical diagram of the overall structure of the drone of the present invention; Figure 3 This is a structural diagram of the UAV take-off and landing platform of the present invention; Figure 4 Establishing scene maps and flight trajectory example maps for autonomous exploration of the drone of the present invention; Figure 5 This is an example diagram of the autonomous landing and centering of the UAV of the present invention; Figure 6 This is an example diagram of autonomous charging of the drone platform of the present invention. DETAILED DESCRIPTION

[0015] First, those skilled in the art should understand that these implementations are only used to explain the technical principles of the embodiments of the present application, and are not intended to limit the protection scope of the embodiments of the present application. Those skilled in the art can make adjustments to them as needed to adapt to specific application scenarios.

[0016] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] See also Figure 1 The present application embodiment discloses a drone autonomous exploration control method based on an integrated drone platform, comprising: Step S1, controlling the UAV to take off on the integrated UAV platform; Step S2, continuously acquiring the three-dimensional point cloud data output by the three-dimensional laser radar and the inertial measurement data output by the inertial sensor, and performing tight coupling fusion processing on the three-dimensional point cloud data and the inertial measurement data to obtain the drone's own posture information; Step S3, constructing a scene map based on the three-dimensional point cloud data and its own posture information; Step S4, receiving the target viewpoint identified by the drone, processing the drone's own posture information and the target posture information at the target viewpoint through a minimum control trajectory planning algorithm to generate an optimal polynomial trajectory curve, and controlling the drone to perform regional exploration along the optimal polynomial trajectory curve to update the size of the explored area in the scene map; Step S5, determining whether the size of the explored area meets a preset condition: If yes, go to step S6; If not, go to step S4; Step S6, obtaining the mark information of the integrated UAV platform captured by the camera to identify the relative position of landing, and processing the UAV's own posture information and the target posture information at the relative position through the minimum control trajectory planning algorithm to generate a landing curve, and controlling the UAV to land on the integrated UAV platform along the landing curve and cooperate with the charging component for charging.

[0018] The introduction of autonomous take-off and landing and automatic charging technology can not only reduce human intervention, but also significantly improve the mission continuity of drones. In rescue scenarios, drones can automatically return to the integrated drone platform to complete charging after completing the mission, and take off autonomously after charging is completed to continue search and rescue or monitoring missions. This cyclic operation mode allows drones to work stably for a long time, thereby covering a larger area and reducing human operation errors. In addition, the autonomous take-off and landing function can adapt to complex and changeable rescue environments without the need for additional reliance on professional technicians, reducing the difficulty of deployment. Through the application of this technology, drones not only have higher autonomy, but can also be combined with real-time data transmission, three-dimensional mapping, autonomous flight and other functions to build a more intelligent rescue system to meet long-term, multi-task, and high-intensity rescue needs, and provide reliable support for emergency response in complex environments.

[0019] In actual operation, the embodiments of the present application are carried out through the following four aspects: First, configure Figure 2 The drone shown is built as follows Figure 3 The integrated drone platform shown in the figure includes sensors such as a flight controller, an edge computing module, a 3D lidar, an inertial sensor, and a downward-looking camera; Second, we provide autonomous exploration methods for drones, including: 1) By tightly coupling and fusing the 3D point cloud data of the 3D LiDAR with the inertial measurement data of the inertial sensor, accurate UAV position information can be obtained; 2) The scene map is built based on the position information obtained by the odometer. During the exploration process, the drone detects and expands the environment boundary based on the real-time collected data, and dynamically updates the map to adapt to unknown scenes. 3) The UAV automatically identifies the target viewpoint, inputs the initial and target poses of the UAV into the minimum control trajectory planning algorithm MINCO, and generates an optimal polynomial trajectory curve from the initial position to the target position; The third aspect is to provide an autonomous take-off and landing method for a UAV, including: 1) By capturing the integrated drone platform’s sign information in real time through the camera, the drone can accurately identify the relative position of landing; 2) Using the minimum control flight trajectory planning algorithm, the drone can avoid obstacles according to the surrounding environment and quickly land at the target location; Fourthly, it provides autonomous centering and charging methods, including: 1) The integrated UAV platform is equipped with an Aruco code to guide the UAV to land in the centering pole of the integrated UAV platform. The position of the centering pole is as follows: Figure 5As shown, after landing is completed, a return charging signal is sent to the integrated UAV platform through the digital transmission sensor; 2) Charging is done by contacting the centering rod with the drone frame to charge the drone battery through the contact points, such as Figure 6 As shown, the system monitors the battery status of the drone in real time, rationally plans charging timing and task execution, and achieves efficient energy utilization.

[0020] The drone generates odometer information by tightly coupling and fusing the 3D point cloud data and the acceleration and angular velocity information provided by the inertial measurement unit (IMU). The 3D point cloud data is usually represented by a set of spatial coordinate points (X, Y, Z coordinates and intensity values), which can provide 3D spatial information of the surrounding environment. The inertial measurement data includes time series acceleration and angular velocity data, which are used to describe the acceleration and rotational motion of the drone in all directions. By fusing this information, the odometer information represents the motion trajectory of the drone in terms of position and attitude (roll angle, pitch angle and heading angle). The drone autonomously explores and establishes scene maps and flight trajectories such as Figure 4 As shown, ultimately, accurate self-position information is obtained through tight coupling optimization processing of 3D point cloud data and inertial measurement data. Usually, extended Kalman filtering is used to eliminate sensor noise and errors, and the real-time position and attitude of the drone are accurately calculated.

[0021] Based on the precise self-pose information and the 3D point cloud data obtained by the 3D lidar, a gradually updated scene map is constructed. First, each point cloud in the 3D point cloud data is converted to the global coordinate system through the precise self-pose information (position and attitude data), so as to achieve spatial alignment of the 3D point cloud data. As the drone moves, new 3D point cloud data is continuously collected, and the map is updated through its own pose information at each moment, gradually constructing a dynamic 3D environment model; next, the scene map is divided into three areas: occupied space, explored space and unexplored space. The occupied space is usually represented by the point cloud data of obstacles in the environment, and these points are considered to be inaccessible. Area; explored space refers to the area that has been explored and has no obstacles, which is usually identified by a certain distance threshold and point cloud density; unexplored space refers to the area that has not been sampled or has no clear obstacle information. A boundary area is formed at the dividing line between the explored space and the unexplored space. On this basis, the trajectory from the current drone position to the best viewpoint on the boundary is calculated by the A* algorithm. This viewpoint is usually the key position where the drone can obtain more unexplored information in order to further enhance its understanding of the environment. When planning the path, the topological structure of the space, dynamic obstacles and the field of view of the sensor are taken into account to ensure that the planned trajectory can reach the boundary viewpoint efficiently and without collision.

[0022] The map and exploration status are updated through a continuous iterative process until one of the following conditions is met: the specified explored area size is reached, or all explored spaces are completely surrounded by occupied spaces, that is, there are no unexplored boundary areas. At each iteration, the updated target data includes: first, updating the division of occupied space and explored space in the map, re-identifying and correcting the space status based on new radar point cloud data and precise posture information; second, updating the boundary of the explored space to ensure that each planned path can cover the new area. The specified explored area size is usually a pre-set area size threshold, which indicates the minimum effective space that needs to be covered during the exploration process. For example, reaching the specified explored area size means that the area or volume of the explored area has reached the preset goal, indicating that most of the environment has been effectively scanned and understood. At this time, the drone can stop further exploration or turn to other tasks.

[0023] Based on the above technical solution, highly real-time autonomous exploration of drones can be achieved. When the drone is in an unfamiliar environment with various obstacles, the positioning and mapping systems have a strong demand for low latency. There are many low-latency autonomous exploration solutions in the prior art, but these methods are all one-time tasks. When the battery is exhausted, larger-scale exploration or reconnaissance missions can no longer be carried out. Therefore, it is urgent to find a way to allow the drone to complete autonomous charging to replenish energy during the exploration mission so that it can achieve a larger-scale exploration mission. The technical solution of the present invention can achieve this demand.

[0024] See also Figure 5 and Figure 6 The present invention provides a method for autonomous take-off, landing and charging of a drone, comprising the following steps: 1) The Aruco code on the integrated UAV platform is recognized by the camera below, the relative position of the target landing platform is obtained by the PnP transformation algorithm, the UAV's own position information is obtained by the above-mentioned odometer, the current position information of the UAV and the target landing position information are input into the minimum control trajectory planning algorithm, and then the UAV is guided to land on the platform according to the control points obtained by the planner; 2) After completing the landing mission, the communication sensor tells the landing platform that it can return to the center for charging. At this time, the platform's centering rod (such as Figure 5 The two return rods are copper rods that can transmit current. The contact points corresponding to the frame feet are in contact for charging. The flight controller monitors the battery power. When the battery is full, it will send a message to the platform that charging is complete and ready for takeoff. At this time, the return rods of the platform will return to the four sides, and then the drone will take off autonomously to a height of 1m to continue the exploration mission. 3) The part of the map that has been explored will be stored in the edge computing module, and the exploration algorithm will continue to complete the exploration task for the explored part in the original map.

[0025] Based on the above implementation cases, it can be clearly seen that an autonomous exploration and control method for a UAV based on an integrated UAV platform provided by an embodiment of the present invention uses a real-time, low-latency multi-sensor fusion positioning algorithm carried by the UAV, which can complete exploration and mapping tasks in unfamiliar environments with high real-time performance. At the same time, the UAV's vision-guided autonomous landing and platform autonomous charging functions can expand the mission scope of the UAV's exploration and reconnaissance missions, and can autonomously complete exploration missions in various areas that are inaccessible to humans. At the same time, it brings autonomous endurance capabilities, greatly expanding the exploration scope of dangerous scenes or the reconnaissance scope of combat environments.

[0026] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "in the present embodiment", "specific example", or "some examples" etc. means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for autonomous exploration and control of a drone based on an integrated drone platform, characterized in that: An integrated UAV platform is pre-built, the integrated UAV platform is provided with a charging component, and the UAV is equipped with a three-dimensional laser radar, an inertial sensor and a camera. The UAV autonomous exploration control method includes the following steps: Step S1, controlling the UAV to take off on the integrated UAV platform; Step S2, continuously acquiring the three-dimensional point cloud data output by the three-dimensional laser radar and the inertial measurement data output by the inertial sensor, and performing tight coupling fusion processing on the three-dimensional point cloud data and the inertial measurement data to obtain the self-pose information of the UAV; Step S3, constructing a scene map based on the three-dimensional point cloud data and the self-pose information; Step S4, receiving the target viewpoint identified by the drone, processing the drone's own pose information and the target pose information at the target viewpoint through a minimum control trajectory planning algorithm to generate an optimal polynomial trajectory curve, and controlling the drone to perform area exploration along the optimal polynomial trajectory curve to update the size of the explored area in the scene map; Step S5, determining whether the size of the explored area meets a preset condition: If yes, go to step S6; If not, go to step S4; Step S6, obtaining the mark information of the integrated UAV platform captured by the camera to identify the relative position of landing, and processing the UAV's own posture information and the target posture information at the relative position through a minimum control trajectory planning algorithm to generate a landing curve, and controlling the UAV to land on the integrated UAV platform along the landing curve and cooperate with the charging component for charging.

2. The autonomous exploration control method of a UAV according to claim 1, characterized in that: In the step S2, the three-dimensional laser radar collects a set of spatial coordinate points represented by X coordinates, Y coordinates, Z coordinates and intensity values ​​as the three-dimensional point cloud data to provide three-dimensional spatial information of the surrounding environment of the drone.

3. The autonomous exploration control method of a UAV according to claim 1, characterized in that: In the step S2, the acceleration and angular velocity including a time series are detected by the inertial sensor as the inertial measurement data to describe the acceleration and rotational motion of the drone in various directions.

4. The autonomous exploration control method of a UAV according to claim 1, characterized in that: The step S3 comprises: Step S31, converting each point cloud in the three-dimensional point cloud data into a global coordinate system according to the self-pose information to perform spatial alignment of the three-dimensional point cloud data; Step S32, continuously updating the three-dimensional point cloud data during the movement of the drone, and constructing a dynamic three-dimensional environment model as the scene map through the drone's own posture information at each moment.

5. The autonomous exploration control method of a UAV according to claim 4, characterized in that: The step S32 further includes: The scene map is divided into occupied space, explored space and unexplored space. The occupied space represents the obstacle point cloud existing in the environment, the explored space represents the area that has been explored and has no obstacle point cloud, and the unexplored space represents the area that has not been explored or has no clear obstacle point cloud. In step S4, the size of the explored space is used as the size of the explored area.

6. The autonomous exploration control method of a UAV according to claim 5, characterized in that: In step S32, a boundary area is formed at the dividing line between the explored space and the unexplored space, and step S4 further includes: The optimal exploration trajectory from the current position of the drone to the optimal viewpoint of the boundary area is calculated by the A* algorithm, and the drone is controlled to follow the optimal exploration trajectory to the boundary area to update the scene map.

7. The autonomous exploration control method of a UAV according to claim 5, characterized in that: The preset condition in step S5 is that the size of the explored area reaches a preset threshold or the explored space is completely surrounded by the occupied space.

8. The autonomous exploration control method of a UAV according to claim 1, characterized in that: In step S6, the Aruco code on the integrated UAV platform is identified by the camera, and the target pose information at the relative position on the integrated UAV platform is obtained by a PnP transformation algorithm.

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