Unmanned aerial vehicle nest centering method

Abstract

The invention provides an unmanned aerial vehicle nest centering method, which is applied to the technical field of unmanned aerial vehicles and comprises the following steps: judging a nest position and calculating a return path by an unmanned aerial vehicle; a nest entering program is started when a preset landing position is reached; determining whether to stop to a target position or not through nest entering induction; starting a clamping, centering and fixing program; the nest is closed, and landing is completed; the technical problems that when an existing unmanned aerial vehicle flies outside to execute a task and returns to an unmanned aerial vehicle nest, the wind direction, the gravity center and the pressure are different, so that the unmanned aerial vehicle returns to the nest unstably, the unmanned aerial vehicle cannot be aligned and inductively charged, the unmanned aerial vehicle cannot return to an inner bin of the nest in severe cases, and repeated aligned landing is needed are solved.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicles, and in particular to a method for returning an unmanned aerial vehicle to a nest. Background Art

[0002] Unmanned aerial vehicle (UAV) is an unmanned aircraft controlled by radio remote control equipment and self-contained program control devices, or operated completely or intermittently autonomously by an onboard computer.

[0003] Compared with manned aircraft, drones are often more suitable for tasks that are too "dull, dirty or dangerous". Drones can be divided into military and civilian applications according to their application areas. In the military, drones are divided into reconnaissance aircraft and target aircraft. In the civilian application, drones + industry applications are the real rigid demand for drones. The applications in aerial photography, agriculture, plant protection, micro selfies, express delivery, disaster relief, wildlife observation, infectious disease monitoring, surveying and mapping, news reporting, power inspection, disaster relief, film and television shooting, creating romance, etc. have greatly expanded the use of drones themselves.

[0004] The drone nest can realize automatic return, charging, and data transmission without 24-hour duty, which is convenient for the drone to have better endurance in locations with poor communication and no power transmission. However, when the drone is flying outside to perform a mission, it will be affected by the different wind direction, center of gravity and pressure when returning to the drone nest, resulting in unstable return of the drone to the nest, making it impossible for the drone to align for induction charging. In severe cases, it cannot return to the warehouse in the nest and needs to be repeatedly aligned and landed. Summary of the invention

[0005] The present application aims to solve the technical problem that when a drone is flying outside to perform a mission, when it returns to the drone nest, the drone may be unstable when returning to the nest due to different wind directions, center of gravity and pressure, resulting in the drone being unable to align for induction charging. In severe cases, the drone cannot return to the nest and needs to be repeatedly aligned and landed. A method for returning a drone to the nest is provided.

[0006] This application adopts the following technical means to solve the technical problem: a method for returning a drone to its nest,

[0007] A method for returning a drone to a nest, the method comprising:

[0008] The drone determines the location of the nest and calculates the return path;

[0009] Arrive at the scheduled landing position and start the nesting procedure;

[0010] Nesting sensing determines whether to stop at the target position;

[0011] Start the clamping and centering fixing procedure;

[0012] Close the nest and complete the landing.

[0013] Furthermore, in the step of determining the location of the drone nest and calculating the return path,

[0014] The return path is calculated according to the algorithm and is divided into fast path, economic path and safe path.

[0015] Furthermore, before the step of arriving at the predetermined landing position and starting the nesting procedure,

[0016] The nest senses whether the drone has reached the top of the nest and determines whether the drone's hovering state is stable.

[0017] Furthermore, in the step of determining whether the hovering state of the drone is stable,

[0018] If the forward and backward amplitude of the sensing drone in the air state within the predetermined time is greater than the threshold, the drone will readjust its fuselage. If it is less than the threshold, the drone will land immediately.

[0019] Furthermore, in the step of arriving at the predetermined landing position and starting the nesting procedure,

[0020] The machine nest control center opens the top cover and determines whether the top cover opening position touches the sensing position.

[0021] Furthermore, before the step of determining whether to stop at the target position by sensing the nest entry,

[0022] The drone adjusts its wings and rotation speed based on the environmental wind direction, center of gravity and pressure, and slowly descends to the machine nest platform until the drone's feet touch the platform, then stops rotating and enters standby mode.

[0023] Furthermore, in the step of the drone entering the nest and stopping in the standby state,

[0024] The nest senses whether the four legs and center position of the drone have stopped at the preset position of the machine. If so, it transmits a command to the drone to shut down. If not, the drone will rise again and repeat the nesting procedure.

[0025] Furthermore, in the step of starting the clamping and centering fixing procedure,

[0026] The machine nest senses that the drone has stopped steadily on the platform and the four legs and the center are connected to the preset position, and the platform begins to clamp the drone and limit the position.

[0027] Furthermore, before the step of closing the nest and completing the landing,

[0028] If the machine does not clamp the drone, the clamping and centering fixing procedure will be restarted. If the machine clamps the drone, the machine nest will be opened to charge the drone.

[0029] The present application provides a method for returning a drone to a nest, which has the following beneficial effects: the drone determines the nest position and calculates the return path; the nest entry procedure is started when the drone reaches the predetermined landing position; the nest entry sensing determines whether the drone stops at the target position; the clamping and centering fixing procedure is started; the nest is closed and the landing is completed;

[0030] In order to deal with the stability problem of UAVs returning to the nest during flight missions, the autonomy of UAVs can be further improved. By improving the computing power, path planning and flight control algorithms of UAVs, they can be made more intelligent and autonomous, better adapted to different environments and conditions, and thus improve the stability and success rate of returning to the nest.

[0031] When the drone is returning to the nest during an outbound mission, the usage scenario and mission planning can be optimized. For example, the mission can be performed under more favorable weather and environmental conditions to reduce the impact of wind direction and pressure. In addition, the mission path and nest position can be optimized to better meet the requirements of the drone returning to the nest.

[0032] Solving the stability problem of the drone returning to the nest can improve flight safety. A stable return process can reduce unexpected situations during the landing or charging process of the drone and reduce the risk of accidents and damage. This will provide higher guarantees for the reliability and availability of drones and further promote the application and development of drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of an embodiment of the drone nest centering method of the present application.

[0034] The implementation, functional features and advantages of the present application will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0035] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] It should be noted that the terms "include", "comprises" and "have" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In the claims, specification and drawings of the present application, relational terms such as "first" and "second" are merely used to distinguish one entity / operation / object from another entity / operation / object, and do not necessarily require or imply any such actual relationship or order between these entities / operations / objects.

[0038] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] Reference Figure 1 , is a flow chart of a method for returning a drone to a nest in an embodiment of the present application;

[0040] Embodiment 1

[0041] A method for returning a drone to a nest, the method comprising:

[0042] The drone determines the location of the nest and calculates the return path;

[0043] Arrive at the scheduled landing position and start the nesting procedure;

[0044] Nesting sensing determines whether to stop at the target position;

[0045] Start the clamping and centering fixing procedure;

[0046] Close the nest and complete the landing.

[0047] In this embodiment, in the step of determining the location of the drone nest and calculating the return path,

[0048] The return path is calculated according to the algorithm and is divided into fast path, economic path and safe path.

[0049] The fast path is as follows:

[0050] data:

[0051] Maximum speed of drone: 15m / s

[0052] Drone energy consumption rate: 8 watt-hours / km

[0053] Drone flight altitude: 100 meters

[0054] Mountain terrain data: including mountain height, terrain changes, etc.

[0055] Weather conditions: including wind speed, wind direction, etc.

[0056] Obstacle detection: The drone is equipped with obstacle avoidance sensors that can detect and avoid obstacles;

[0057] Process:

[0058] Step 1: Determine the shortest distance and direction between the current position of the drone and the target position using mountain terrain data and real-time sensor data;

[0059] Step 2: Consider weather conditions and determine flight safety; for example, if the current wind speed exceeds the maximum tolerance of the drone, it is considered an unsafe flight condition;

[0060] Wind speed: 7 m / s

[0061] The maximum wind speed that the drone can withstand: 10 m / s

[0062] Step 3: Calculate the flight time and energy consumption of the return path based on the maximum speed and energy consumption rate of the drone;

[0063] Maximum speed of drone: 15m / s

[0064] Drone energy consumption rate: 8 watt-hours / km

[0065] Return distance: 5000 meters

[0066] Flight time: 333 seconds

[0067] Energy consumption: 1333 Wh

[0068] Specifically,

[0069] Calculate the distance and direction between the current position of the drone and the obstacle point:

[0070] Use the drone’s GPS or other positioning system to obtain the latitude and longitude coordinates of the drone’s current location;

[0071] Obstacle detection sensors are used to obtain the longitude and latitude coordinates of obstacle points;

[0072] Calculate the distance and direction between the current position of the drone and the obstacle point based on the latitude and longitude coordinates;

[0073] Compare the drone's path to the obstacle's path to determine if a detour is necessary:

[0074] Represent the path between the current position of the drone and the target position as a line segment or a polyline;

[0075] Represent the location of the obstacle as a point or points;

[0076] Determine whether the path of the drone intersects with the path of the obstacle, that is, determine whether the path between the current position of the drone and the target position intersects with the obstacle point;

[0077] If a detour is required, calculate the detour path and merge it with the existing path:

[0078] Use path planning algorithms (such as A* algorithm, Dijkstra algorithm, etc.) to calculate detour paths;

[0079] Based on the location of obstacles and terrain data, an obstacle avoidance grid or graph is generated, in which obstacle points are marked as inaccessible areas;

[0080] Perform path planning on the obstacle avoidance grid map to find the shortest path around obstacles;

[0081] Merge the detour path with the existing path to ensure that the drone can pass through the detour path smoothly;

[0082] Update the current position and path of the drone and continue to detect the next obstacle point:

[0083] Update the current position of the drone to the last point on the detour path;

[0084] Update the existing path to a detour path;

[0085] in,

[0086] Assume the following scenario:

[0087] Drone current position: (lat1, lon1) = (40.7128, -74.0060)

[0088] Target position: (lat2,lon2)=(40.7589,-73.9851)

[0089] Obstacle points and their coordinates:

[0090] Obstacle point 1: (lat3,lon3) = (40.7225,-73.9981)

[0091] Obstacle point 2: (lat4,lon4) = (40.7290,-73.9896)

[0092] Obstacle point 3: (lat5,lon5) = (40.7199,-73.9855)

[0093] Obstacle point 4: (lat6,lon6) = (40.7265,-73.9964)

[0094] Obstacle point 5: (lat7,lon7) = (40.7312,-73.9868)

[0095] Obstacle point 6: (lat8,lon8) = (40.7157,-73.9912)

[0096] Obstacle point 7: (lat9,lon9) = (40.7243,-73.9989)

[0097] Obstacle point 8: (lat10, lon10) = (40.7191, -73.9926)

[0098] Use Dijkstra's algorithm to calculate the shortest path:

[0099] Construct an undirected graph and add vertices and edges:

[0100] Vertices include the current position of the drone, the target position, and obstacle points;

[0101] The weight of an edge is the Euclidean distance between vertices;

[0102] Initialize the distance array and predecessor node array:

[0103] The distance array is used to store the shortest distance from the starting vertex to other vertices, and the initial value is infinite;

[0104] The predecessor node array is used to store the predecessor nodes that reach each vertex, and the initial value is null;

[0105] Set the shortest distance of the starting vertex to 0 and add it to the list of vertices to be processed;

[0106] Enter the loop until the list of vertices to be processed is empty:

[0107] Select the vertex with the shortest distance from the list of vertices to be processed and mark it as the current vertex;

[0108] Traverse the adjacent vertices of the current vertex:

[0109] Calculate the temporary distance from the starting vertex to the adjacent vertex, that is, the shortest distance of the current vertex plus the weight of the edge from the current vertex to the adjacent vertex;

[0110] If the temporary distance is less than the shortest distance of the adjacent vertices, the shortest distance and predecessor node of the adjacent vertices are updated, and the adjacent vertices are added to the list of vertices to be processed;

[0111] After the loop ends, the shortest path can be obtained by backtracking from the target position to the starting position through the predecessor node array;

[0112] In the economic path and the safety path, the drone's endurance and environmental factors are judged as follows:

[0113] Define nodes: Divide the map into grids and treat each grid unit as a node; each node has a location (latitude and longitude coordinates) and a connection relationship with its adjacent nodes;

[0114] Define heuristic function: According to the requirements of the economic path, define a heuristic function to estimate the cost of each node to the target node; the heuristic function can be evaluated based on factors such as the distance between nodes, height change, energy consumption, etc.

[0115] Initialization algorithm: add the starting node to the open list and set its cost to 0;

[0116] Iterative search: Find the optimal path through iterative search; in each iteration, select the node with the minimum cost from the open list and mark it as the current node;

[0117] Expanding nodes: For the current node, expand its adjacent nodes and calculate the cost of the expanded nodes; add the expanded nodes to the open list and update their costs; if the adjacent nodes are already in the open list, compare the new cost with the original cost and choose the smaller cost;

[0118] Update path information: During the search process, record the parent node of each node so that you can eventually backtrack to find the complete path;

[0119] Termination condition: When the target node is added to the open list, or the open list is empty (no nodes can be expanded), the search is terminated;

[0120] Generate a path: Based on the recorded parent node information, trace back from the target node until you reach the starting node to get the complete economic path.

[0121] In this embodiment, before the step of arriving at the predetermined landing position and starting the nesting procedure,

[0122] The nest senses whether the drone has reached the top of the nest and determines whether the drone's hovering state is stable.

[0123] Assuming that the nest is a fixed target point, the position of the drone is obtained through GPS or other positioning systems. We need to detect whether the drone has reached the top of the nest and determine whether its hovering state is stable.

[0124] Specific data example:

[0125] Drone position: (lat1,lon1)=(40.7200,-73.9970)

[0126] Machine nest position: (lat2,lon2)=(40.7200,-73.9970)

[0127] Height difference threshold: delta_h = 5 meters

[0128] Determine whether the drone has reached the top of the nest:

[0129] Calculate the horizontal distance between the drone and the nest: distance = calculate_distance(lat1, lon1, lat2, lon2). If the distance is less than the preset threshold, it can be considered that the drone has reached the top of the nest.

[0130] Determine whether the drone is in a stable hovering state:

[0131] then,

[0132] Obtaining altitude data from the drone, such as from a pressure sensor or GPS altitude information;

[0133] Calculate the height change of the drone: delta_height = current_height - previous_height. If delta_height is less than a predetermined threshold, it can be considered that the drone's hovering state is stable.

[0134] In this embodiment, in the step of determining whether the hovering state of the drone is stable,

[0135] If the forward and backward amplitude of the sensing drone in the air state within the predetermined time is greater than the threshold, the drone will readjust its fuselage. If it is less than the threshold, the drone will land immediately.

[0136] In the step of arriving at the predetermined landing position and starting the nesting procedure,

[0137] The machine nest control center opens the top cover and determines whether the top cover opening position touches the sensing position.

[0138] In this embodiment, before the step of determining whether to stop at the target position by sensing the nesting,

[0139] The drone adjusts its wings and rotation speed based on the environmental wind direction, center of gravity and pressure, and slowly descends to the machine nest platform until the drone's feet touch the platform, then stops rotating and enters standby mode.

[0140] Assume that

[0141] Drone current position: (lat1, lon1) = (40.7200, -73.9970)

[0142] Machine nest position: (lat2,lon2)=(40.7200,-73.9970)

[0143] Current drone altitude: current_height = 50 meters

[0144] Target machine height: target_height = 10 meters

[0145] Ambient wind direction: wind_direction = 180 degrees

[0146] Center of gravity adjustment: center_of_gravity_adjustment = 0.5

[0147] Pressure adjustment: pressure_adjustment = 0.8

[0148] Determine whether the drone has reached the landing position:

[0149] Calculate the horizontal distance between the drone and the platform: distance = calculate_distance(lat1,lon1,lat2,lon2). If the distance is less than the preset threshold, it can be considered that the drone has reached the landing position of the platform.

[0150] Determine whether the drone touches the machine:

[0151] Compare the current drone height with the target platform height: if current_height is less than or equal to target_height, it is considered that the drone has touched the platform;

[0152] According to the judgment of the environmental wind direction, center of gravity and pressure, adjust the wings and speed:

[0153] Adjust the angle of the wing according to the wind direction and the flight attitude of the drone;

[0154] Adjust the speed of the drone according to the center of gravity adjustment and pressure adjustment;

[0155] Stop rotating and enter standby mode.

[0156] In the step of the drone entering the nest and stopping in the standby state,

[0157] The nest senses whether the four legs and center position of the drone have stopped at the preset position of the machine. If so, it transmits a command to the drone to shut down. If not, the drone will rise again and repeat the nesting procedure.

[0158] In this embodiment, in the step of starting the clamping and centering fixing procedure,

[0159] The machine nest senses that the drone has stopped steadily on the platform and the four legs and the center are connected to the preset position, and the platform begins to clamp the drone and limit the position.

[0160] Before closing the nest and completing the landing,

[0161] If the machine does not clamp the drone, the clamping and centering fixing procedure will be restarted. If the machine clamps the drone, the machine nest will be opened to charge the drone.

[0162] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for returning a drone to a nest, characterized in that: The method comprises: The drone determines the location of the nest and calculates the return path; Arrive at the scheduled landing position and start the nesting procedure; Nesting sensing determines whether to stop at the target position; Start the clamping and centering fixing procedure; Close the nest and complete the landing.

2. The method for centering a drone nest according to claim 1, characterized in that: In the step of determining the location of the drone nest and calculating the return path, The return path is calculated according to the algorithm and is divided into fast path, economic path and safe path.

3. The method for centering a drone nest according to claim 1, characterized in that: Before the step of arriving at the predetermined landing position and starting the nesting procedure, The nest senses whether the drone has reached the top of the nest and determines whether the drone's hovering state is stable.

4. The method for centering a drone nest according to claim 1, characterized in that: In the step of determining whether the hovering state of the drone is stable, If the forward and backward amplitude of the sensing drone in the air state within the predetermined time is greater than the threshold, the drone will readjust its fuselage. If it is less than the threshold, the drone will land immediately.

5. The method for centering a drone nest according to claim 1, characterized in that: In the step of arriving at the predetermined landing position and starting the nesting procedure, The machine nest control center opens the top cover and determines whether the top cover opening position touches the sensing position.

6. The method for centering a drone nest according to claim 1, characterized in that: Before the step of determining whether to stop at the target position by sensing the nesting, The drone adjusts its wings and rotation speed based on the environmental wind direction, center of gravity and pressure, and slowly descends to the machine nest platform until the drone's feet touch the platform, then stops rotating and enters standby mode.

7. The method for centering a drone nest according to claim 6, characterized in that: In the step of the drone entering the nest and stopping in the standby state, The nest senses whether the four legs and center position of the drone have stopped at the preset position of the machine. If so, it transmits a command to the drone to shut down. If not, the drone will rise again and repeat the nesting procedure.

8. The method for centering a drone nest according to claim 1, characterized in that: In the step of starting the clamping and centering fixing procedure, The machine nest senses that the drone has stopped steadily on the platform and the four legs and the center are connected to the preset position, and the platform begins to clamp the drone and limit the position.

9. The method for centering a drone nest according to claim 1, characterized in that: Before closing the nest and completing the landing, If the machine does not clamp the drone, the clamping and centering fixing procedure will be restarted. If the machine clamps the drone, the machine nest will be opened to charge the drone.

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