CN111857168A

CN111857168A - Unmanned aerial vehicle positioning method and device and unmanned aerial vehicle parking attitude adjusting method and device

Info

Publication number: CN111857168A
Application number: CN202010635502.7A
Authority: CN
Inventors: 崔鹏; 龚玉帅; 刘新民; 刘宝旭; 孙凯
Original assignee: Beijing Airlango Technology Co ltd
Current assignee: Beijing Airlango Technology Co ltd
Priority date: 2020-07-03
Filing date: 2020-07-03
Publication date: 2020-10-30

Abstract

The application discloses an unmanned aerial vehicle positioning method and device and an unmanned aerial vehicle parking attitude adjusting method and device, wherein the positioning method acquires point cloud data of a characteristic cross section of an unmanned aerial vehicle through a TOF sensor based on flight time; determining a plurality of first position coordinates according to the point cloud data, wherein each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located; and determining unmanned aerial vehicle positioning information according to the first position coordinate, wherein the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle. The method has the advantages that based on the flight time ranging method, the position and pose of the unmanned aerial vehicle can be quickly and accurately positioned by acquiring the point cloud data of the characteristic section of the unmanned aerial vehicle and combining the point cloud data with the corresponding characteristic points of the unmanned aerial vehicle, the algorithm is simple, the accuracy is high, the cost of required hardware is low, and reliable data information can be provided for requirements of adjustment of the parking attitude of the unmanned aerial vehicle and the like.

Description

Unmanned aerial vehicle positioning method and device and unmanned aerial vehicle parking attitude adjusting method and device

Technical Field

The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle positioning method and device and an unmanned aerial vehicle parking attitude adjusting method and device.

Background

At present in the unmanned aerial vehicle field, only when unmanned aerial vehicle stops on appointed parking stall with predetermineeing the gesture, follow-up work such as automatic transport dining box, automatic change battery can realize full automation.

However, in practical situations, the unmanned aerial vehicle cannot land on the designated stand with a highly accurate preset posture when landing, and generally speaking, the unmanned aerial vehicle may have some distance or angle from the designated stand. At this moment, just need adjust unmanned aerial vehicle's gesture with the help of external equipment, at the in-process of adjustment, external equipment at first needs carry out accurate location to the position when unmanned aerial vehicle descends.

However, the positioning technology after the unmanned aerial vehicle falls in the prior art has many defects. Some methods adopt visual positioning, but the camera device is expensive, slow in speed, poor in precision and very high in requirements on ambient light; in other methods, the sensor is directly mounted on a manipulator of external equipment to acquire data at a depression angle, so that the problems of limited acquisition range, distorted picture edges, reduced judgment precision and the like are caused.

Disclosure of Invention

In view of the above, the present application is proposed so as to provide a method and an apparatus for positioning an unmanned aerial vehicle and a method and an apparatus for adjusting a parking attitude of an unmanned aerial vehicle, which overcome or at least partially solve the above problems.

According to an aspect of the present application, there is provided a method for positioning a drone, the method comprising:

acquiring point cloud data of a characteristic cross section of the unmanned aerial vehicle based on a time of flight (TOF) sensor;

determining a plurality of first position coordinates according to the point cloud data, wherein each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located;

and determining unmanned aerial vehicle positioning information according to the first position coordinate, wherein the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle.

Preferably, in the positioning method of the unmanned aerial vehicle, the TOF sensor is arranged on a movable component of an apron of the unmanned aerial vehicle and can acquire point cloud data along the horizontal direction;

the characteristic section is a horizontal section of the undercarriage under the parking state of the unmanned aerial vehicle, and each characteristic point corresponds to one cylinder of the undercarriage.

Preferably, in the above method for positioning an unmanned aerial vehicle, acquiring point cloud data of a characteristic cross section of the unmanned aerial vehicle based on a time of flight TOF sensor includes:

driving the movable assembly to drive the TOF sensor to realize scanning movement from an initial acquisition position to a final acquisition position, wherein the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance in the scanning movement process;

And acquiring point cloud data acquired by the TOF sensor in the scanning motion process.

Preferably, in the above method for positioning an unmanned aerial vehicle, determining a plurality of first position coordinates according to the point cloud data includes:

carrying out homogeneity classification on the point cloud data based on a clustering algorithm;

and determining the coordinates of the most probable points corresponding to each type of homogeneity point cloud data.

Preferably, in the above-mentioned method for locating a drone, determining the drone location information according to the first location coordinates includes:

determining the distance between every two most probable points according to the coordinates of the most probable points;

establishing a corresponding relation between each most probable point and the landing gear column body according to the distance;

and determining the positioning information of the unmanned aerial vehicle according to the coordinates and the corresponding relation of the most probable points.

According to a second aspect of the application, a method for adjusting the parking attitude of an unmanned aerial vehicle is provided, and the method comprises the following steps:

determining positioning information of the unmanned aerial vehicle according to the method;

determining an adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle;

and driving the unmanned aerial vehicle adjusting equipment according to the adjusting quantity so as to adjust the parking posture of the unmanned aerial vehicle to be consistent with the preset posture.

Preferably, in the above method for adjusting the parking attitude of the unmanned aerial vehicle, the attitude information of the preset attitude includes a preset yaw angle and a preset coordinate, and determining the adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle includes:

Determining a rotation adjustment amount according to a preset yaw angle and a yaw angle of the unmanned aerial vehicle, and determining a translation adjustment amount according to a preset central point coordinate and a second position coordinate;

drive unmanned aerial vehicle adjusting device according to the adjustment volume includes:

driving the unmanned aerial vehicle adjusting equipment to grab the unmanned aerial vehicle so as to separate the unmanned aerial vehicle from the unmanned aerial vehicle parking plane;

driving a rotating shaft of the unmanned aerial vehicle adjusting equipment to rotate according to the rotation adjusting amount, and driving a translation component of the unmanned aerial vehicle adjusting equipment according to the translation adjusting amount;

after the unmanned aerial vehicle gesture is consistent with the preset gesture, the unmanned aerial vehicle adjusting device is driven to place the unmanned aerial vehicle on the parking plane.

According to a third aspect of the application, there is provided a drone positioning device, the device comprising:

the acquisition unit is used for acquiring point cloud data of the characteristic cross section of the unmanned aerial vehicle based on the time of flight TOF sensor;

the data processing unit is used for determining a plurality of first position coordinates according to the point cloud data, and each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located; and the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle.

Preferably, in the positioning device for the unmanned aerial vehicle, the TOF sensor is arranged on a movable component of an unmanned aerial vehicle parking apron and can acquire point cloud data along the horizontal direction; the characteristic section is a horizontal section of the undercarriage under the parking state of the unmanned aerial vehicle, and each characteristic point corresponds to one cylinder of the undercarriage.

Preferably, in the positioning device for the unmanned aerial vehicle, the obtaining unit is configured to drive the movable assembly to drive the TOF sensor to realize a scanning movement from an initial acquisition position to a final acquisition position, and the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance in the scanning movement process; and is used for acquiring point cloud data acquired by the TOF sensor in the scanning motion process.

Preferably, in the positioning device for the unmanned aerial vehicle, the data processing unit is configured to perform homogeneity classification on the point cloud data based on a clustering algorithm; and is used for determining the coordinates of the most probable points corresponding to each type of homogeneity point cloud data.

Preferably, in the positioning device for the unmanned aerial vehicle, the data processing unit is configured to determine a distance between every two most probable points according to coordinates of the most probable points; and is used for establishing the corresponding relation between each most probable point and the landing gear column body according to the distance; and the positioning information of the unmanned aerial vehicle is determined according to the coordinates and the corresponding relation of the most probable points.

According to the fourth aspect of the application, an unmanned aerial vehicle parking posture adjusting device is provided, the device includes:

the positioning unit is used for determining positioning information of the unmanned aerial vehicle according to the unmanned aerial vehicle positioning device;

the adjustment quantity determining unit is used for determining the adjustment quantity according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle;

and the driving unit is used for driving the unmanned aerial vehicle adjusting equipment according to the adjustment quantity so as to adjust the parking posture of the unmanned aerial vehicle to be consistent with the preset posture.

Preferably, in the above apparatus for adjusting a parking attitude of an unmanned aerial vehicle, the attitude information of the preset attitude includes a preset yaw angle and a preset coordinate, and the adjustment amount determining unit is configured to determine a rotation adjustment amount according to the preset yaw angle and the yaw angle of the unmanned aerial vehicle, and determine a translation adjustment amount according to the preset center point coordinate and the second position coordinate;

the driving unit is used for driving the unmanned aerial vehicle adjusting equipment to grab the unmanned aerial vehicle so as to separate the unmanned aerial vehicle from the unmanned aerial vehicle parking plane; the translation component is used for driving the rotating shaft of the unmanned aerial vehicle adjusting equipment to rotate according to the rotation adjusting amount and driving the unmanned aerial vehicle adjusting equipment to translate according to the translation adjusting amount; and after the unmanned aerial vehicle posture is consistent with the preset posture, driving the unmanned aerial vehicle adjusting equipment to place the unmanned aerial vehicle on a parking plane.

According to a fifth aspect of the present application, there is provided an electronic apparatus, wherein the electronic apparatus includes: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a drone positioning method as any one of the above.

According to a sixth aspect of the application, there is provided a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the drone positioning method as any one of the above.

According to a seventh aspect of the present application, there is provided an electronic apparatus, wherein the electronic apparatus includes: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the drone parking pose adjustment method as any one of the above.

According to an eighth aspect of the application, there is provided a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the unmanned aerial vehicle parking attitude adjustment method as any one of the above.

According to the technical scheme, the point cloud data of the characteristic cross section of the unmanned aerial vehicle is obtained through the TOF sensor based on the flight time; determining a plurality of first position coordinates according to the point cloud data, wherein each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located; and determining unmanned aerial vehicle positioning information according to the first position coordinate, wherein the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle. The method has the advantages that based on the flight time ranging method, the position and pose of the unmanned aerial vehicle can be quickly and accurately positioned by acquiring the point cloud data of the characteristic section of the unmanned aerial vehicle and combining the point cloud data with the corresponding characteristic points of the unmanned aerial vehicle, the algorithm is simple, the accuracy is high, the cost of required hardware is low, and reliable data information can be provided for requirements of adjustment of the parking attitude of the unmanned aerial vehicle and the like.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 shows a schematic flow diagram of a method for positioning a drone according to an embodiment of the present application;

fig. 2 shows a schematic flow diagram of a method for positioning a drone according to another embodiment of the present application;

figure 3 illustrates a graph of point cloud data for one horizontal cross-section of an unmanned aircraft landing gear acquired by an unmanned aircraft TOF sensor according to another embodiment of the present application;

fig. 4 shows a schematic flow diagram of a drone parking pose adjustment according to one embodiment of the present application;

Fig. 5 shows a schematic structural diagram of a parking attitude adjustment device for an unmanned aerial vehicle according to an embodiment of the present application;

fig. 6 shows a schematic structural diagram of a positioning device of a drone according to an embodiment of the present application;

fig. 7 shows a schematic structural diagram of a parking attitude adjusting apparatus for an unmanned aerial vehicle according to an embodiment of the present application;

FIG. 8 shows a schematic of an electronic structure according to one embodiment of the present application;

FIG. 9 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.

Detailed Description

Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The idea of the application is that: aiming at the current situation that the positioning method of the unmanned aerial vehicle after landing is complex in calculation, low in precision and high in cost, the accurate positioning information of the unmanned aerial vehicle can be obtained by using the time of flight TOF sensor and based on the point cloud data of the characteristic section of the unmanned aerial vehicle, the algorithm is simple, the precision is high, and the cost of required hardware is low.

Fig. 1 shows a schematic structural diagram of a positioning method for a drone according to an embodiment of the present application, the method including:

and step S110, point cloud data of the characteristic cross section of the unmanned aerial vehicle is obtained based on the TOF sensor.

The Time of flight (TOF) method is that a sensor emits modulated near-infrared light, which is reflected after encountering an object, and the sensor converts the distance of a shot scene by calculating the Time difference or phase difference between light emission and reflection to generate depth information. The point cloud data is obtained based on a flight time ranging method.

The time-of-flight sensor may be any one or several of existing lidar, and the application is not limited thereto.

Unmanned aerial vehicle characteristic cross section in this application can realize the cross-section of unmanned aerial vehicle location for combining unmanned aerial vehicle structural feature, for example the ascending unmanned aerial vehicle cross section of horizontal direction, unmanned aerial vehicle horizontal cross section promptly. The different unmanned aerial vehicle horizontal cross-section corresponds the different structural feature of unmanned aerial vehicle, like the edge except the unmanned aerial vehicle fuselage on certain horizontal cross-section, still has the corresponding point of unmanned aerial vehicle horn.

Point Cloud Data (Point Cloud Data) refers to the scanned Data recorded in the form of points, each Point includes three-dimensional coordinates, some may include color information (RGB) or reflection Intensity information (Intensity). The reflection intensity information can represent the geometric position of a scanning object, the intensity information is acquired by the echo intensity collected by the laser scanner receiving device, and the intensity information is related to the surface material, the roughness and the incident angle direction of a target, the emission energy of an instrument and the laser wavelength. Besides the geometric position, some point cloud data also have color information, the color information is usually obtained by a camera, and then color information (RGB) of pixels at corresponding positions is given to corresponding points in the point cloud.

Utilize the arbitrary horizontal plane of time of flight sensor scanning unmanned aerial vehicle, can obtain the point cloud data of this horizontal plane, and this horizontal plane is unmanned aerial vehicle's characteristic cross section promptly, owing to there is specific structure at some horizontal planes of unmanned aerial vehicle, consequently is favorable to the simplification of calculation and the improvement of precision more, can regard as preferred mode.

And step S120, determining a plurality of first position coordinates according to the point cloud data, wherein each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located.

Processing of the point cloud data includes, but is not limited to, point cloud filtering (data pre-processing), point cloud point of interest analysis, and the like.

The point cloud filtering is to filter noise, and the originally acquired point cloud data often includes a large number of hash points and isolated points. The main methods of point cloud filtering include: bilateral filtering, gaussian filtering, conditional filtering, straight-through filtering, random sampling consistent filtering, VoxelGrid filtering, and the like.

The first position coordinates are determined according to the point cloud data, and actually, point cloud points corresponding to the feature points are determined from the point cloud data, and then the position coordinates are determined, so that a plurality of first position coordinates are obtained. Specifically, clustering or attention point extraction and the like are realized.

For example, by using a focus extraction algorithm such as Harris, SIFT, SURF, KAZE, etc., a plurality of focuses are extracted from the original point cloud data, the number of focuses is greatly reduced compared with the data volume of the original point cloud, and the focuses are combined with local feature descriptors to form a focus descriptor, which is commonly used for forming the representation of the original data without losing representativeness and descriptiveness.

The extracted points of interest are the first positions described in this application, and in practice, there is often more than one point of interest, because multiple points of interest are usually required to characterize a geometric structure. These points of interest correspond to the characteristic points on the characteristic cross section where the drone is located.

Further, the coordinates of the point can be obtained from the point cloud data of the point of interest, and the coordinate representation method can adopt any one, such as a cartesian coordinate system, since the point cloud data is two-dimensional, i.e. can be represented as (x, y).

And S130, determining unmanned aerial vehicle positioning information according to the first position coordinate, wherein the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle.

After obtaining the coordinates corresponding to the feature points of the unmanned aerial vehicle, the coordinates are calculated and converted into the coordinates of the unmanned aerial vehicle, and the positioning information of the unmanned aerial vehicle is obtained, and the coordinates of the unmanned aerial vehicle can be expressed by various methods, such as a WGS-84 coordinate system, a NED coordinate system, and a body coordinate system. The application proposes a horizontal coordinate representation method (X, Y, YAW) for a drone, wherein the X and Y coordinates are scalar quantities, i.e. corresponding to an offset on the X axis and an offset on the Y axis in a cartesian two-dimensional coordinate system, and YAW is a heading angle, representing the orientation of the drone.

And calculating the first position coordinates corresponding to the feature points of the unmanned aerial vehicles to obtain the coordinate values of the unmanned aerial vehicles in the coordinate system, so as to obtain the positioning information of the unmanned aerial vehicles.

According to the method shown in the figure 1, based on the flight time ranging method, the position and pose of the unmanned aerial vehicle are quickly and accurately positioned by acquiring the point cloud data of the characteristic section of the unmanned aerial vehicle and combining the point cloud data with the corresponding characteristic points of the unmanned aerial vehicle, the algorithm is simple, the accuracy is high, the cost of required hardware is low, and reliable data information can be provided for requirements of adjustment of the parking attitude of the unmanned aerial vehicle and the like.

In one embodiment of the application, the TOF sensor is arranged on a movable component of the unmanned aerial vehicle parking apron, and point cloud data acquisition can be carried out along the horizontal direction; the characteristic cross section is a horizontal cross section of the undercarriage under the parking state of the unmanned aerial vehicle, and each characteristic point corresponds to one cylinder of the undercarriage.

In the prior art, often with the sensor direct mount on external equipment's the manipulator to depression angle degree carries out data acquisition, leads to collection range limited, picture edge distortion to reduce the problem such as judgement precision.

In this embodiment, set up TOF sensor on the movable assembly of unmanned aerial vehicle air park, can carry out point cloud data acquisition along the horizontal direction, overcome above-mentioned defect among the prior art like this.

The undercarriage is the column, is put down at unmanned aerial vehicle descending in-process to support unmanned aerial vehicle, because its structural feature is showing, recommend in this embodiment to be unmanned aerial vehicle parking state under the horizontal cross section of undercarriage as the characteristic cross section and be an preferred mode, and in some cloud data processing process, a cylinder of undercarriage is corresponded to every characteristic point, can obtain unmanned aerial vehicle's locating information through handling the coordinate that four cylinders correspond respectively like this.

In one embodiment of the present application, acquiring point cloud data of a characteristic cross section of an unmanned aerial vehicle based on a time of flight TOF sensor comprises: driving the movable assembly to drive the TOF sensor to realize scanning movement from an initial acquisition position to a final acquisition position, wherein the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance in the scanning movement process; and acquiring point cloud data acquired by the TOF sensor in the scanning motion process.

In this embodiment, the removal subassembly can drive TOF sensor from initial acquisition position to terminating the scanning movement that carries out of gathering the position, at the in-process of this scanning, only needs a TOF sensor can accomplish the collection of data, and the sensor is the most expensive part of whole subassembly cost, has practiced thrift the hardware cost in very big degree like this.

In one embodiment of the present application, determining a number of first location coordinates from the point cloud data comprises: performing homogeneity classification on the point cloud data based on a clustering algorithm; and determining the coordinates of the most probable points corresponding to each type of homogeneity point cloud data.

In the implementation, when point cloud data is processed, a clustering algorithm, such as a density-based clustering algorithm, is recommended, and the clustering aims are to make the similarity of objects in the same class as large as possible; the similarity between objects of different classes is as small as possible. In the present embodiment, point cloud data for the same feature point is regarded as a type of data having the same property.

In each type of data, a most probable point exists, and the coordinates of the most probable point are used for representing the coordinates of the response characteristic point. The most probable point can be understood as the point with the highest probability in each kind of homogeneous point cloud data, and as in a density-based clustering algorithm, the point with the highest point cloud density can be considered as the most probable point.

In one embodiment of the present application, determining drone positioning information from the first location coordinates comprises: determining the distance between every two most probable points according to the coordinates of the most probable points; establishing a corresponding relation between each most probable point and the landing gear column body according to the distance; and determining the positioning information of the unmanned aerial vehicle according to the coordinates of the most probable points and the corresponding relation.

Starting from the sensor as a starting point, a certain horizontal section when the unmanned aerial vehicle is parked is seen, and according to the geometrical structure of the unmanned aerial vehicle and the perspective relation of the geometry, the distances among all the feature points are different and can be regularly followed, so that according to the distances among the feature points, all the most probable points can be corresponding to the structural features forming the most probable points, and the coordinates of all the most probable points can be jointly solved according to the corresponding relation to obtain the coordinates of the unmanned aerial vehicle.

Fig. 2 shows a schematic flow diagram of a positioning method for a drone according to another embodiment of the present application. After the unmanned aerial vehicle lands, the movable assembly drives the TOF sensor to realize that the horizontal section of the unmanned aerial vehicle undercarriage is subjected to scanning movement from the initial acquisition position to the termination acquisition position, so that point cloud sampling is carried out in a preset time period, and point cloud data acquired in the scanning movement process are acquired.

Based on a clustering method, the point cloud data is subjected to homogeneity classification, the point cloud data can be divided into four classes, the most probable point and the coordinates of the most probable point in each class of point cloud data are determined, and the four most probable points are represented as P₁、P₂、P₃、P₄(ii) a The coordinates of the four most probable points are respectively (x)₁，y₁)、(x₂，y₂)、(x₃，y₃)、(x₄，y₄) As shown in fig. 3.

Calculating the distance between every two most probable points, and generating a distance list, wherein the distance between every two points in the distance list is represented as: p₁P₂、P₁P₃、P₁P₄、P₂P₃、P₂P₄、P₃P₄。

Judging the most probable point P according to the distance list₁～P₄Which point in the plane corresponds to the left front pillar, left rear pillar, right front pillar, right rear pillar of the plane, where it is assumed that P is determined₃Corresponding to the right front pillar, P₄Corresponding to the left front pillar, P₁Corresponding to the right rear pillar, P₂Corresponding to the left and right columns.

Calculating unmanned aerial vehicle coordinates Q according to the feature point coordinates, which may be specifically expressed as Σ P/n, where Σ P is P in this example₁+P₂+P₃+P₄N is 4; direction vector of direction information YAW of drone (P ═ P)₁+P₂)/2-(P₃+P₄)/2。

It should be noted that, in the embodiment corresponding to the method described in fig. 2, capital letters represent vectors, and lower case letters represent scalars.

Fig. 4 is a schematic flow chart illustrating a method for adjusting a parking attitude of an unmanned aerial vehicle according to an embodiment of the present application, where the method for adjusting a parking attitude of an unmanned aerial vehicle can be implemented by the unmanned aerial vehicle adjusting apparatus according to an embodiment of the present application shown in fig. 5, but is not limited to the unmanned aerial vehicle adjusting apparatus, and any apparatus that can implement the method for adjusting a parking attitude of an unmanned aerial vehicle may be implemented, and is described here as an example only, the unmanned aerial vehicle parking attitude adjusting apparatus may be disposed on an unmanned aerial vehicle parking apron and may move on the parking apron, and the unmanned aerial vehicle parking attitude adjusting apparatus specifically includes: including a support shaft 510, a sliding shaft 520, a lifting shaft 530, a rotating shaft 540, a clamping jaw 550, and a TOF sensor 560.

The supporting shaft comprises two sub-shafts which are arranged in parallel relatively and is supported on the unmanned aerial vehicle parking apron; two ends of the sliding shaft are respectively connected with the two sub-shafts of the supporting shaft in a sliding manner and can slide along the supporting shaft; the lifting shaft is vertically arranged on the sliding shaft, is connected with the sliding shaft in a sliding manner and can slide along the vertical direction; the rotating shaft is fixedly connected with the lower end of the lifting shaft and can drive the clamping jaw arranged below the rotating shaft to rotate; the whole device can adjust the unmanned aerial vehicle to a preset parking attitude according to a driving instruction, wherein the driving instruction is obtained according to the unmanned aerial vehicle positioning information obtained by the method and the attitude information of the preset attitude.

The moving parts, including the supporting shaft 510, the sliding shaft 520, the lifting shaft 530, and the rotating shaft 540, may be all electric cylinder modules or other linear motion devices. The support shaft 510, the sliding shaft 520, and the lifting shaft 530 form an orthogonal XYZ coordinate system, and the rotation axis 540 is a rotational joint. End effector adopts clamping jaw mechanism, according to unmanned aerial vehicle structural design, can adopt a plurality of clamping jaws to press from both sides tight horn, also can press from both sides tight organism.

When the unmanned aerial vehicle adjusting equipment is designed, two detail problems can be noticed so as to improve the use convenience, firstly, the size B in the figure 5 is selected according to a section principle and is determined according to the position height of a selected horizontal section on the unmanned aerial vehicle; second, the dimension a in fig. 5 is needed to ensure that the TOF sensor does not interfere with the aircraft when the end effector grasps the aircraft.

Therefore, equipment of a four-axis motion system with three coordinates combined with a rotary joint is constructed, and the unmanned aerial vehicle parked at any position and any angle on the parking apron can be grabbed through the clamping jaw; namely, no matter how large the landing position error of the unmanned aerial vehicle caused by insufficient landing precision is, the unmanned aerial vehicle can be grabbed by the equipment as long as the unmanned aerial vehicle lands on the parking apron; further, above-mentioned equipment can place the unmanned aerial vehicle who snatchs in arbitrary station with the high accuracy, can be automatic trade the electric potential or load and unload the meal case position.

Fig. 4 shows a method for adjusting the parking attitude of an unmanned aerial vehicle according to an embodiment of the present application, including:

and S410, determining positioning information of the unmanned aerial vehicle according to any one of the positioning methods of the unmanned aerial vehicle.

In actual conditions, unmanned aerial vehicle can't be accurate completely when descending and stop to fall on appointed parking stall with predetermineeing the gesture, often with predetermineeing gesture and position and have certain angular deviation or distance deviation, at this moment, need external equipment to carry out the gesture to it and correct. In the prior art, the unmanned aerial vehicle is normally pushed forward by two groups of horizontal push rods, in the process, sliding friction force exists between an undercarriage and a landing plane of the unmanned aerial vehicle, and the undercarriage and the landing plane of the unmanned aerial vehicle are abraded by pushing the unmanned aerial vehicle for multiple times, so that the service lives of the undercarriage and the landing plane are shortened, and the maintenance cost is increased; the requirement on the rigidity of the landing gear of the unmanned aerial vehicle is high, so that the difficulty in weight reduction of the unmanned aerial vehicle is brought; due to sliding friction, the forward speed of the unmanned aerial vehicle cannot be greatly increased, and the distribution efficiency of the unmanned aerial vehicle is reduced; due to sliding friction, the use effect of the equipment is affected by the large noise generated in the straightening process of the unmanned aerial vehicle. The embodiment provides an unmanned aerial vehicle parking attitude adjusting method so as to overcome the problems.

Firstly, the position of the unmanned aerial vehicle landing on the apron is accurately positioned according to the unmanned aerial vehicle positioning method, and a coordinate point Q of the unmanned aerial vehicle can be obtained₁The coordinates of which can be expressed as (x)₅,y₅，YAW₁)。

And S420, determining an adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle.

According to the deviation degree between the positioning information of the unmanned aerial vehicle and the attitude information of the preset attitude, the adjustment quantity can be determined.

And S430, driving the unmanned aerial vehicle adjusting equipment according to the adjusting quantity so as to adjust the parking attitude of the unmanned aerial vehicle to be consistent with the preset attitude.

According to the determined adjustment quantity, the unmanned aerial vehicle parking attitude can be adjusted to be consistent with the preset attitude through the unmanned aerial vehicle adjusting equipment.

By the unmanned aerial vehicle parking attitude adjusting method and the unmanned aerial vehicle parking attitude adjusting equipment shown in the figures 4-5, the unmanned aerial vehicle can be grabbed to a specified place and adjusted to a specified attitude. In the process, the unmanned aerial vehicle does not move in a sliding friction manner, so that the unmanned aerial vehicle and a landing plane are not abraded, and the maintenance cost is reduced; the noise of the equipment is reduced, and the use effect of the equipment is improved; the requirement on the rigidity of the landing gear of the unmanned aerial vehicle is reduced, and the dead weight of the unmanned aerial vehicle can be reduced; unmanned aerial vehicle adjusting equipment does not receive frictional force to influence, can the high-speed motion, promotes the operating efficiency.

In an embodiment of the application, the attitude information of the preset attitude includes a preset yaw angle and a preset coordinate, and determining the adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle includes: and determining a rotation adjustment amount according to a preset yaw angle and the yaw angle of the unmanned aerial vehicle, and determining a translation adjustment amount according to a preset central point coordinate and the second position coordinate. Presetting attitude coordinate point Q for unmanned aerial vehicle according to attitude information of preset attitude₂The coordinates of which can be expressed as (x)₆，y₆，YAW₂) Then the horizontal translation adjustment amount is (x)₆-x₅，y₆-y₅) The YAW angle is YAW₂-YAW₁In this embodiment, capital letters denote vectors and lower case letters denote scalars.

The specific adjustment process can refer to the following steps: driving the unmanned aerial vehicle adjusting equipment to grab the unmanned aerial vehicle so as to separate the unmanned aerial vehicle from the unmanned aerial vehicle parking plane; driving a rotating shaft of the unmanned aerial vehicle adjusting equipment to rotate according to the rotation adjusting quantity, and driving a translation component of the unmanned aerial vehicle adjusting equipment according to the translation adjusting quantity; after the unmanned aerial vehicle gesture is consistent with the preset gesture, the unmanned aerial vehicle adjusting equipment is driven to place the unmanned aerial vehicle on the parking plane. It should be noted here that, in the adjustment process, the attitude of the unmanned aerial vehicle refers to a planar attitude, that is, information in the vertical direction is ignored, so that the adjusted attitude and the preset parking attitude are consistent when the unmanned aerial vehicle is grabbed and suspended in the air.

Fig. 6 shows a schematic structural diagram of a positioning device 600 for a drone according to an embodiment of the present application, comprising:

the acquiring unit 610 is configured to acquire point cloud data of the characteristic cross section of the unmanned aerial vehicle based on the time of flight TOF sensor.

The data processing unit 620 is configured to determine a plurality of first position coordinates according to the point cloud data, where each first position coordinate corresponds to a feature point on the feature cross section where the unmanned aerial vehicle is located; and the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle.

According to the positioning device for the unmanned aerial vehicle, which is shown in the figure 6, the position and pose of the unmanned aerial vehicle can be quickly and accurately positioned by a method of combining point cloud data of characteristic sections of the unmanned aerial vehicle and corresponding characteristic points of the unmanned aerial vehicle based on a flight time distance measuring method, the algorithm is simple, the accuracy is high, the cost of required hardware is low, and reliable data information can be provided for requirements such as adjustment of the parking attitude of the unmanned aerial vehicle.

In one embodiment of the application, in the positioning device of the unmanned aerial vehicle, the TOF sensor is arranged on a movable component of an unmanned aerial vehicle parking apron, and point cloud data acquisition can be performed along the horizontal direction; the characteristic section is a horizontal section of the undercarriage under the parking state of the unmanned aerial vehicle, and each characteristic point corresponds to one cylinder of the undercarriage.

In an embodiment of the present application, in the positioning apparatus for an unmanned aerial vehicle, the obtaining unit 610 is configured to drive the movable assembly to drive the TOF sensor to realize a scan movement from an initial acquisition position to a final acquisition position, and the TOF sensor performs point cloud sampling at a preset time period or a preset distance tolerance during the scan movement; and is used for acquiring point cloud data acquired by the TOF sensor in the scanning motion process.

In an embodiment of the present application, in the above-mentioned unmanned aerial vehicle positioning apparatus, the data processing unit 620 is configured to perform homogeneity classification on the point cloud data based on a clustering algorithm; and is used for determining the coordinates of the most probable points corresponding to each type of homogeneity point cloud data.

In an embodiment of the present application, in the above-mentioned unmanned aerial vehicle positioning apparatus, the data processing unit 620 is configured to determine a distance between every two most-possible-points according to coordinates of the respective most-possible-points; and is used for establishing the corresponding relation between each most probable point and the landing gear column body according to the distance; and the positioning information of the unmanned aerial vehicle is determined according to the coordinates and the corresponding relation of the most probable points.

It should be noted that, the specific implementation of each of the above embodiments of the positioning apparatus for an unmanned aerial vehicle may refer to the specific implementation of the above embodiment of the positioning method for an unmanned aerial vehicle, and is not described herein again.

Fig. 7 shows a schematic structural diagram of a parking attitude adjusting apparatus for an unmanned aerial vehicle according to an embodiment of the present application, where the parking attitude adjusting apparatus 700 includes:

and the positioning unit 710 is used for determining positioning information of the unmanned aerial vehicle according to any unmanned aerial vehicle positioning device.

Firstly, the position of the unmanned aerial vehicle landing on the apron is accurately positioned according to the unmanned aerial vehicle positioning method, and a coordinate point Q of the unmanned aerial vehicle can be obtained ₁The coordinates of which can be expressed as (x)₅,y₅，YAW₁)。

And an adjustment amount determining unit 720, configured to determine an adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle.

And the driving unit 730 is used for driving the unmanned aerial vehicle adjusting equipment according to the adjustment amount so as to adjust the parking posture of the unmanned aerial vehicle to be consistent with the preset posture.

By the unmanned aerial vehicle that figure 7 shows and park gesture adjusting device, can grab unmanned aerial vehicle to appointed place, adjust to appointed gesture. In the process, the unmanned aerial vehicle does not move in a sliding friction manner, so that the unmanned aerial vehicle and a landing plane are not abraded, and the maintenance cost is reduced; the noise of the equipment is reduced, and the use effect of the equipment is improved; the requirement on the rigidity of the landing gear of the unmanned aerial vehicle is reduced, and the dead weight of the unmanned aerial vehicle can be reduced; unmanned aerial vehicle adjusting equipment does not receive frictional force to influence, can the high-speed motion, promotes the operating efficiency.

In an embodiment of the application, in the above unmanned aerial vehicle parking attitude adjusting apparatus, the attitude information of the preset attitude includes a preset yaw angle and a preset coordinate, and the adjustment amount determining unit is configured to determine a rotation adjustment amount according to the preset yaw angle and the unmanned aerial vehicle yaw angle, and determine a translation adjustment amount according to the preset center point coordinate and the second position coordinate; the driving unit 730 is used for driving the unmanned aerial vehicle adjusting equipment to grab the unmanned aerial vehicle so as to separate the unmanned aerial vehicle from the unmanned aerial vehicle parking plane; the translation component is used for driving the rotating shaft of the unmanned aerial vehicle adjusting equipment to rotate according to the rotation adjusting amount and driving the unmanned aerial vehicle adjusting equipment to translate according to the translation adjusting amount; and after the unmanned aerial vehicle posture is consistent with the preset posture, driving the unmanned aerial vehicle adjusting equipment to place the unmanned aerial vehicle on a parking plane.

Presetting attitude coordinate point Q for unmanned aerial vehicle according to attitude information of preset attitude₂The coordinates of which can be expressed as (x)₆，y₆，YAW₂) Then the horizontal translation adjustment amount is (x)₆-x₅，y₆-y₅) The YAW angle is YAW₂-YAW₁In this embodiment, capital letters denote vectors and lower case letters denote scalars.

It should be noted here that, in the adjustment process, the attitude of the unmanned aerial vehicle refers to a planar attitude, that is, information in the vertical direction is ignored, so that the adjusted attitude and the preset parking attitude are consistent when the unmanned aerial vehicle is grabbed and suspended in the air.

According to the technical scheme, the point cloud data of the characteristic cross section of the unmanned aerial vehicle is obtained through the TOF sensor based on the flight time; determining a plurality of first position coordinates according to the point cloud data, wherein each first position coordinate corresponds to a characteristic point on the characteristic section where the unmanned aerial vehicle is located; and determining unmanned aerial vehicle positioning information according to the first position coordinate, wherein the unmanned aerial vehicle positioning information comprises an unmanned aerial vehicle yaw angle and a second position coordinate representing the position of the unmanned aerial vehicle. The method has the advantages that based on the flight time ranging method, the point cloud data of the characteristic cross section of the unmanned aerial vehicle are obtained, the method of combining the point cloud data with the corresponding characteristic points of the unmanned aerial vehicle is used for quickly and accurately positioning the landing pose of the unmanned aerial vehicle, the algorithm is simple, the accuracy is high, the cost of required hardware is low, and reliable data information is provided for adjustment of the subsequent parking pose of the unmanned aerial vehicle.

It should be noted that:

the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various application aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, application is directed to less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that microprocessors or Digital Signal Processors (DSPs) may be used in practice to implement some or all of the functions of some or all of the components of a drone positioning device or drone parking pose adjustment device according to embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

For example, fig. 8 shows a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 800 comprises a processor 810 and a memory 820 arranged to store computer executable instructions (computer readable program code). The memory 820 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 820 has a storage space 830 storing computer readable program code 831 for performing any of the method steps described above. For example, the storage space 830 for storing the computer-readable program code may include respective computer-readable program codes 831 for respectively implementing various steps in the above methods. The computer readable program code 831 may be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 9. FIG. 9 shows a schematic diagram of a computer-readable storage medium according to an embodiment of the present application. The computer readable storage medium 900 stores computer readable program code 831 for executing the steps of the method according to the present application, which is readable by a processor 810 of the electronic device 800, and when the computer readable program code 831 is executed by the electronic device 800, causes the electronic device 800 to execute the steps of the method described above, and in particular, the computer readable program code 831 stored in the computer readable storage medium can execute the drone positioning method or the drone parking pose adjustment method shown in any of the embodiments described above. The computer readable program code 831 may be compressed in a suitable form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims

1. A method for locating a drone, the method comprising:

2. The unmanned aerial vehicle positioning method according to claim 1, wherein the TOF sensor is arranged on a movable component of an unmanned aerial vehicle parking apron, and point cloud data collection can be carried out along a horizontal direction;

the characteristic cross section is a horizontal cross section of the undercarriage under the parking state of the unmanned aerial vehicle, and each characteristic point corresponds to one cylinder of the undercarriage.

3. The unmanned aerial vehicle positioning method of claim 2, wherein the obtaining point cloud data of the characteristic cross section of the unmanned aerial vehicle based on the time of flight TOF sensor comprises:

4. The drone positioning method of claim 2, wherein the determining a number of first location coordinates from the point cloud data comprises:

Performing homogeneity classification on the point cloud data based on a clustering algorithm;

5. The drone positioning method of claim 4, wherein the determining drone positioning information from the first location coordinates comprises:

and determining the positioning information of the unmanned aerial vehicle according to the coordinates of the most probable points and the corresponding relation.

6. An unmanned aerial vehicle parking attitude adjusting method is characterized by comprising the following steps:

determining drone positioning information according to the method of any one of claims 1-5;

determining an adjustment amount according to attitude information of a preset attitude and the positioning information of the unmanned aerial vehicle;

and driving unmanned aerial vehicle adjusting equipment according to the adjusting quantity so as to adjust the parking posture of the unmanned aerial vehicle to be consistent with the preset posture.

7. The method of claim 6, wherein the attitude information of the preset attitude comprises a preset yaw angle and a preset coordinate, and the determining the adjustment amount according to the attitude information of the preset attitude and the positioning information of the unmanned aerial vehicle comprises:

Determining a rotation adjustment amount according to a preset yaw angle and a yaw angle of the unmanned aerial vehicle, and determining a translation adjustment amount according to a preset central point coordinate and the second position coordinate;

according to the adjustment volume drive unmanned aerial vehicle adjusting device includes:

driving a rotating shaft of the unmanned aerial vehicle adjusting equipment to rotate according to the rotation adjusting quantity, and driving a translation component of the unmanned aerial vehicle adjusting equipment according to the translation adjusting quantity;

after the unmanned aerial vehicle gesture is consistent with the preset gesture, the unmanned aerial vehicle adjusting equipment is driven to place the unmanned aerial vehicle on the parking plane.

8. An unmanned aerial vehicle positioner, its characterized in that, the device includes:

9. The utility model provides an unmanned aerial vehicle parks posture adjustment device which characterized in that, the device includes:

a positioning unit for determining drone positioning information according to the drone positioning device of claim 8;

and the driving unit is used for driving the unmanned aerial vehicle adjusting equipment according to the adjusting quantity so as to adjust the parking posture of the unmanned aerial vehicle to be consistent with the preset posture.

10. An electronic device, wherein the electronic device comprises: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the drone positioning method of any one of claims 1-5 or cause the processor to perform the drone parking pose adjustment method of any one of claims 6-7.

11. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the drone positioning method of any one of claims 1-5 or the drone parking pose adjustment method of any one of claims 6-7.