CN116736879A - Unmanned aerial vehicle automatic obstacle avoidance method and obstacle avoidance system based on cloud computing - Google Patents
Unmanned aerial vehicle automatic obstacle avoidance method and obstacle avoidance system based on cloud computing Download PDFInfo
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Abstract
The application discloses an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing, wherein the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing comprises the following steps: establishing a space coordinate system based on the flight environment of the unmanned aerial vehicle; acquiring initial data preset by the unmanned aerial vehicle during flight, wherein the initial data comprise flight track information, flight speed information and flight safety threshold information of the unmanned aerial vehicle; acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time; judging whether an obstacle exists on the flight route of the unmanned aerial vehicle based on the surrounding environment data information, if so, generating an avoidance command, and if not, generating a continuous flight command; the method and the device can effectively solve the problem of poor obstacle avoidance effect in the actual flight process of the existing unmanned aerial vehicle.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing.
Background
The unmanned aerial vehicle has strong advantages in the investigation and rescue tasks of the fire disaster of the urban high-rise building, in order to quickly and accurately reach the fire scene, the unmanned fire-fighting vehicle must be ensured to fly to a destination under the conditions of minimum time, fuel consumption, environmental threat and the like, and the unmanned aerial vehicle can be ensured to have quick response capability to suddenly appearing obstacles. Meanwhile, the unmanned aerial vehicle also needs to avoid obstacles on the forward road in the actual flight process so as to avoid collision.
At present, the existing part of unmanned aerial vehicle can utilize optical equipment to position itself, for example uses laser range finder to confirm the distance of self and barrier to make things convenient for the staff to avoid in advance. However, the unmanned aerial vehicle is mostly manually operated, once the unmanned aerial vehicle is too close to an obstacle due to negligence or error of a worker, the unmanned aerial vehicle cannot timely avoid the operation, and therefore the unmanned aerial vehicle collides with the obstacle, and property loss is caused. Meanwhile, for the set track flight, when an obstacle appears on a track route, the unmanned aerial vehicle is easy to avoid the obstacle in time, and then the phenomenon that the unmanned aerial vehicle collides with the obstacle is caused.
Disclosure of Invention
In view of the defects of the prior art, the application aims to disclose an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing, so as to solve the problem that the obstacle avoidance effect is poor in the actual flight process of the existing unmanned aerial vehicle.
To achieve the above and other related objects, the present application discloses an unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing, which includes:
establishing a space coordinate system based on the flight environment of the unmanned aerial vehicle;
acquiring initial data preset by the unmanned aerial vehicle during flight, wherein the initial data comprise flight track information, flight speed information and flight safety threshold information of the unmanned aerial vehicle;
acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time;
judging whether an obstacle exists on the flight route of the unmanned aerial vehicle based on the surrounding environment data information, if so, generating an avoidance command, and if not, generating a continuous flight command;
and sending the avoidance instruction or the continuous flight instruction to the unmanned aerial vehicle.
In one aspect of the present application, the flight trajectory information includes coordinate data of an initial point location, a final point location, and a plurality of intermediate points;
the unmanned aerial vehicle flight safety threshold information comprises a first threshold range and a second threshold range, the first threshold range and the second threshold range are spherical areas taking the central position of the unmanned aerial vehicle as a sphere center, and the radius of the first threshold range is smaller than that of the second threshold range.
In an aspect of the present application, the step of acquiring, in real time, the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle includes:
the position information of the unmanned aerial vehicle and the surrounding environment data information are transmitted through multiple channels; and
the surrounding environment data information is at least six azimuth data information of the unmanned aerial vehicle in the flying process, namely front/back/left/right/up/down, and circumferential data information of the unmanned aerial vehicle in the flying direction;
the azimuth data information and the circumferential data information are acquired through a laser radar.
In an aspect of the present application, in the step of determining whether an obstacle exists on the flight path of the unmanned aerial vehicle based on the surrounding environment data information, if the obstacle exists, generating an avoidance command, and if the obstacle does not exist, generating a continuing flight command, the method includes:
acquiring perception data of the laser radar, and judging whether the distance data perceived by the laser radar is positioned outside the flight safety threshold information;
if the circumferential data information is located outside the safety threshold, the unmanned aerial vehicle runs according to a preset track route;
if the circumferential data information is located within the safety threshold, judging whether the circumferential data is located within the first threshold range or the second threshold range;
when the circumferential data is within the first threshold range, executing an emergency braking program and generating a first early warning signal;
and executing a deceleration braking program and generating a second early warning signal when the circumferential data are within the second threshold range.
In one aspect of the present application, when the deceleration braking program is executed, the acceleration required for braking the unmanned aerial vehicle is obtained by querying an initial speed-acceleration comparison table based on the flight speed data of the unmanned aerial vehicle.
In one aspect of the present application, the method further comprises the steps of:
synchronously transmitting the generated first early warning signal or second early warning signal to a manual server;
the artificial server feedback signal is obtained, and the feedback signal comprises: automatic connection and manual control;
if the feedback signal is manual control, the unmanned aerial vehicle ends an automatic control program and adopts manual control;
and if the feedback signal is an automatic takeover, executing an avoidance program.
In an aspect of the present application, the performing the avoidance procedure includes:
obtaining optimized track data;
according to the preset flight speed, flying along the optimized track data;
the optimized track data comprise an end position, and when the unmanned aerial vehicle is located at the end position, whether the distance data information perceived by the laser radar of the unmanned aerial vehicle is located outside the safety threshold value or not is judged.
In one aspect of the present application, the method further includes:
acquiring space coordinate data of the end point position;
based on the space coordinate data of the end point position and the vector value of the nearest point position of the flight track information;
and executing a reset program based on the vector value, so that the unmanned aerial vehicle returns to a preset flight track.
The application also discloses an obstacle avoidance system applying the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing, which is provided with a cloud computing server end, a manual service end and an execution service end, wherein the cloud computing server end, the manual service end and the execution end are in communication connection, and the obstacle avoidance system comprises:
the space coordinate module is used for establishing a space coordinate system based on the flight environment of the unmanned aerial vehicle;
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring initial data preset when the unmanned aerial vehicle flies, and the initial data comprise flight track information, flight speed information and flight safety threshold information of the unmanned aerial vehicle;
the data receiving module is used for acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time;
the data processing module is used for judging whether an obstacle exists on the flight route of the unmanned aerial vehicle or not based on the surrounding environment data information, generating an avoidance instruction if the obstacle exists, and generating a continuous flight instruction if the obstacle does not exist;
and the data sending module is used for sending the avoidance instruction or the continuous flight instruction to the unmanned aerial vehicle.
In summary, the application discloses an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing, cloud computing resources can be fully utilized by the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing, efficient data processing and path planning are realized, and the obstacle avoidance capability and flight safety of the unmanned aerial vehicle are greatly improved. Meanwhile, as data processing and decision making are completed at the cloud, the calculation resource and load requirements of the unmanned aerial vehicle can be greatly reduced, so that the weight and energy consumption of the unmanned aerial vehicle are lower, and the flight time is longer. Meanwhile, the cloud server optimizes the obstacle avoidance scheme according to actual obstacle avoidance requirements, and the phenomenon that the unmanned aerial vehicle collides with the aircraft can be effectively avoided. The problem of poor obstacle avoidance effect in the actual flight process of the existing unmanned aerial vehicle can be effectively solved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of an unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing in an embodiment of the present application;
fig. 2 is a schematic flow chart of an automatic obstacle avoidance method for an unmanned aerial vehicle based on cloud computing according to an embodiment of the present application;
fig. 3 is a schematic flow chart of step S40 in an embodiment of an automatic obstacle avoidance method for an unmanned aerial vehicle based on cloud computing according to the present application;
fig. 4 is a schematic block diagram of an unmanned aerial vehicle automatic obstacle avoidance system based on cloud computing according to an embodiment of the present application.
Description of element reference numerals
100. A space coordinate module; 200. a data acquisition module; 300. a data receiving module; 400. a data processing module; 500. and a data transmitting module.
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application.
Please refer to fig. 1 to 4. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the application to the extent that it can be practiced, since modifications, changes in the proportions, or adjustments of the sizes, which are otherwise, used in the practice of the application, are included in the spirit and scope of the application which is otherwise, without departing from the spirit or scope thereof.
The application discloses an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing, which can be used for solving the problem of poor obstacle avoidance effect in the actual flight process of the existing unmanned aerial vehicle.
Referring to fig. 1, fig. 1 is a schematic diagram of an architecture of an automatic obstacle avoidance method and an obstacle avoidance system of an unmanned aerial vehicle based on cloud computing according to an embodiment of the present application.
As shown in fig. 1, the architecture includes a cloud computing server, a manual server, and an execution end, and it can be understood that the execution end includes an unmanned aerial vehicle, and communication connection is provided among the cloud computing server, the manual server, and the execution end. In particular, the communication connection may allow for the use of high-speed wireless communication technologies, such as high-performance Wi-Fi (e.g., wi-Fi 6), 4G/5G, etc., to ensure that the speed and bandwidth of the data transmission meets the requirements. Meanwhile, the communication connection also adopts a data optimization protocol, such as TCP (transmission control protocol) or UDP (user datagram protocol), and the most suitable protocol is selected according to actual requirements so as to reduce transmission delay and improve transmission speed. It should be noted that, in order to further improve the data transfer rate among the cloud computing server, the manual server and the executing end, the cloud computing server, the manual server and the executing end support perform data transfer through multiple channels. And the parallel transmission of data is realized by utilizing a multichannel transmission technology, so that the transmission speed is improved. For example, multiple wireless modules or multiple network interfaces may be allowed to be used in the drone for simultaneous data transmission.
It should be noted that, for the unmanned aerial vehicle, a plurality of lidars are disposed on the body. And acquiring surrounding environment data of the unmanned aerial vehicle through the laser radar. The laser radar is respectively positioned at six directions of the unmanned aerial vehicle, so that radar data of the six directions of the unmanned aerial vehicle are acquired through the laser radar. At the same time, at least one group of laser radars are rotationally connected to the unmanned aerial vehicle, and the laser radars can allow biaxial rotation of the unmanned aerial vehicle. Therefore, the radar data of the forward direction of the unmanned aerial vehicle in the fattening process is acquired through the laser radar.
Referring to fig. 2, in an embodiment, the application discloses an automatic obstacle avoidance method of an unmanned aerial vehicle based on cloud computing, by which the stability of the unmanned aerial vehicle in the flight process can be effectively improved.
Specifically, the automatic obstacle avoidance method at least comprises the following steps in the actual use process.
First, step S10 is executed to establish a space coordinate system based on the flight environment of the unmanned aerial vehicle.
The unmanned aerial vehicle is in the flight process, through this space coordinate in order to confirm position and gesture. In one embodiment, the spatial coordinate system may allow for a Cartesian coordinate system. Wherein the cartesian coordinate system represents the position using a rectangular coordinate system. It divides the space into X, Y and Z three axes, X axis represents east-west direction, Y axis represents north-south direction, Z axis represents direction perpendicular to ground. The position and the gesture of the unmanned aerial vehicle in the air can be effectively described through a Cartesian coordinate system.
Next, step S20 is executed to obtain initial data preset by the unmanned aerial vehicle during flight, where the initial data includes flight trajectory information, flight speed information, and flight safety threshold information of the unmanned aerial vehicle.
Wherein, unmanned aerial vehicle presets its flight orbit information in advance in the flight process. The unmanned aerial vehicle can be allowed to fly according to preset flight track information in the take-off process. It should be noted that the flight trajectory may include an initial point location, a final point location, and a plurality of intermediate points. In the actual flight process, the unmanned aerial vehicle starts from the initial point position, sequentially passes through a plurality of intermediate point positions and then reaches the final point position. It can be appreciated that the unmanned aerial vehicle flies according to a preset flying speed in the flying process.
During the flight of the unmanned aerial vehicle, when an obstacle appears on the flight path of the unmanned aerial vehicle. When the unmanned plane flies according to a set route, a collision event is easy to occur.
In particular, for unmanned aerial vehicle flight speeds, adjustments may be allowed to be made according to different circumstances. To improve unmanned aerial vehicle's flight effect.
The unmanned aerial vehicle flight safety threshold information characterizes a preset space range, and can safely fly when no obstacle exists in the range. It will be appreciated that in an embodiment, the flight safety threshold information may include a first threshold range and a second threshold range. The first threshold range and the second threshold range are spherical areas centered on the center position of the drone, and the radius of the first threshold range is smaller than the radius of the second threshold range.
It can be appreciated that a plurality of lidars are provided on the unmanned aerial vehicle, and the unmanned aerial vehicle itself carries the positioning device. The method can allow the actual position information of the unmanned aerial vehicle to be acquired through the positioning device, and the surrounding environment data information of the unmanned aerial vehicle to be acquired through the laser radar. In order to improve the data transmission effect between the unmanned aerial vehicle and the cloud server, data transmission between the unmanned aerial vehicle and the cloud server is carried out through multiple channels.
Meanwhile, the environment data information of the unmanned aerial vehicle at least comprises front/back/left/right/up/down six azimuth data information of the unmanned aerial vehicle in the flight process and circumferential data information of the unmanned aerial vehicle in the flight direction. Therefore, the cloud server side can allow the subsequent data of the unmanned aerial vehicle to be processed according to preset flight track information, flight speed information and flight safety threshold information, and generate corresponding control instructions.
Next, step S30 is executed to acquire the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time. Communication connection is established between the unmanned aerial vehicle and the cloud server, and the cloud server acquires the position information and the surrounding environment data information of the unmanned aerial vehicle in real time.
Meanwhile, the cloud server side can allow position information and surrounding environment data information of the unmanned aerial vehicle to be obtained in real time according to preset flight track information, flight speed information and flight safety threshold information, and a decision is made.
Further, step S40 is executed to determine whether or not an obstacle exists on the flight path of the unmanned aerial vehicle based on the surrounding environment data information, and if the obstacle exists, an avoidance command is generated, and if the obstacle does not exist, a continuation flight command is generated.
Referring to fig. 3, in step S40, further includes:
step S401, acquiring the laser radar data, and determining whether the distance data perceived by the laser radar is located outside the flight safety threshold information.
In step S402, if the circumferential data information is located outside the safety threshold, the unmanned aerial vehicle travels according to a preset trajectory.
In step S403, if the circumferential data information is within the safety threshold, it is determined that the circumferential data is within the first threshold range or the second threshold range.
In step S404, when the circumferential data is within the first threshold range, an emergency braking procedure is executed, and a first warning signal is generated.
And step S405, when the circumferential data is within the second threshold range, executing a deceleration braking procedure and generating a second early warning signal. And when the deceleration braking program is executed, acquiring acceleration required by unmanned aerial vehicle braking by inquiring an initial speed-acceleration comparison table based on the flight speed data of the unmanned aerial vehicle. Through selecting suitable acceleration for unmanned aerial vehicle can in time slow down in actual flight in-process, and be in the state of hovering, in order to avoid unmanned aerial vehicle to take place to hit the machine realization.
It should be noted that the emergency level of the first warning signal is higher than that of the second warning signal. Meanwhile, when the unmanned aerial vehicle generates a first early warning signal or a second early warning signal, the first early warning signal and the second early warning signal are synchronously transmitted to the manual server.
After the first early warning signal or the second early warning signal is acquired, the artificial service end can allow feedback decision-making according to the early warning signal and generate a feedback signal. At the same time, feedback decisions may be allowed to include automatic takeover as well as manual control. It can be understood that when the feedback signal is an automatic takeover, the unmanned aerial vehicle executes the avoidance program according to the actual situation. And if the feedback signal is manual control, the unmanned aerial vehicle ends the automatic control program and adopts manual control.
In an embodiment, when the unmanned aerial vehicle executes the avoidance procedure, the method at least includes the following steps:
first, optimized trajectory data is acquired. The optimized track data is an optimized route based on multiple acquisitions of the laser radar. When no one flies along the optimized track route, the avoidance of the ground obstacle can be realized.
It should be noted that, for the number of lidars of the unmanned aerial vehicle, the priority is up, down, left, right, front and back in sequence. For example, the drone may allow for the use of RRT path planning algorithms when performing path optimization. The method searches the path between the unmanned aerial vehicle and the target point by continuously expanding branches of a tree. When encountering an obstacle, branches of the tree are automatically avoided, and unexplored areas are selected.
Secondly, after the optimized route is set, the unmanned aerial vehicle can fly along the optimized track data according to the preset flying speed.
It should be noted that the optimized trajectory data includes an end position, and when the unmanned aerial vehicle is located at the end position, whether the distance data information perceived by the laser radar of the unmanned aerial vehicle is located outside the safety threshold value.
Specifically, the cloud server side may acquire the spatial coordinate data of the destination location, and based on the spatial coordinate data of the destination location and the vector value of the nearest point location of the flight trajectory information. And finally, executing a reset program based on the vector value, so that the unmanned aerial vehicle returns to a preset flight track.
Finally, step S50 is executed, and the avoidance instruction or the continuous flight instruction is sent to the unmanned aerial vehicle.
Specifically, cloud computing resources can be fully utilized through the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing, efficient data processing and path planning are achieved, and the obstacle avoidance capability and flight safety of the unmanned aerial vehicle are greatly improved. Meanwhile, as data processing and decision making are completed at the cloud, the calculation resource and load requirements of the unmanned aerial vehicle can be greatly reduced, so that the weight and energy consumption of the unmanned aerial vehicle are lower, and the flight time is longer.
Referring to fig. 4, in an embodiment, the present application further provides an unmanned aerial vehicle automatic obstacle avoidance system based on cloud computing, where the unmanned aerial vehicle automatic obstacle avoidance system based on cloud computing corresponds to the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing in the above embodiment. The unmanned aerial vehicle automatic obstacle avoidance system based on cloud computing can comprise a space coordinate module 100, a data acquisition module 200, a data receiving module 300, a data processing module 400 and a data sending module 500. The module referred to in the present application refers to a series of computer program segments capable of being executed by a processor and performing a fixed function, and stored in a memory.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the space coordinate module 100 is configured to establish a space coordinate system based on a flight environment of the unmanned aerial vehicle.
The data acquisition module 200 is configured to acquire initial data preset by the unmanned aerial vehicle during flight, where the initial data includes flight trajectory information, flight speed information, and flight safety threshold information of the unmanned aerial vehicle.
And the data receiving module 300 is used for acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time.
And the data processing module 400 is configured to determine, based on the surrounding environment data information, whether an obstacle exists on the flight path of the unmanned aerial vehicle, generate an avoidance command if the obstacle exists, and generate a continuing flight command if the obstacle does not exist.
And the data sending module 500 is used for sending the avoidance instruction or the continuous flight instruction to the unmanned aerial vehicle.
In detail, each module in the unmanned aerial vehicle automatic obstacle avoidance system based on cloud computing in the embodiment of the present application adopts the same technical means as the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing in the above embodiment, and can produce the same technical effects, and is not described here again.
In summary, the application discloses an unmanned aerial vehicle automatic obstacle avoidance method and an obstacle avoidance system based on cloud computing, cloud computing resources can be fully utilized by the unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing, efficient data processing and path planning are realized, and the obstacle avoidance capability and flight safety of the unmanned aerial vehicle are greatly improved. Meanwhile, as data processing and decision making are completed at the cloud, the calculation resource and load requirements of the unmanned aerial vehicle can be greatly reduced, so that the weight and energy consumption of the unmanned aerial vehicle are lower, and the flight time is longer. Meanwhile, the cloud server optimizes the obstacle avoidance scheme according to actual obstacle avoidance requirements, and the phenomenon that the unmanned aerial vehicle collides with the aircraft can be effectively avoided.
Therefore, the problem of poor obstacle avoidance effect in the actual flight process of the existing unmanned aerial vehicle can be effectively solved. Therefore, the application effectively overcomes some practical problems in the prior art, thereby having high utilization value and use significance.
The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the above embodiments may be implemented by hardware associated with a program stored in a computer-readable storage medium, including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (CD-ROM), or other optical disk Memory, magnetic disk Memory, tape Memory, or any other medium capable of being used for carrying or storing data that can be Read by a computer.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
Claims (9)
1. An unmanned aerial vehicle automatic obstacle avoidance method based on cloud computing is characterized by comprising the following steps:
establishing a space coordinate system based on the flight environment of the unmanned aerial vehicle;
acquiring initial data preset by the unmanned aerial vehicle during flight, wherein the initial data comprise flight track information, flight speed information and flight safety threshold information of the unmanned aerial vehicle;
acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time;
judging whether an obstacle exists on the flight route of the unmanned aerial vehicle based on the surrounding environment data information, if so, generating an avoidance command, and if not, generating a continuous flight command;
and sending the avoidance instruction or the continuous flight instruction to the unmanned aerial vehicle.
2. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method of claim 1, wherein the flight trajectory information comprises coordinate data of an initial point location, a final point location, and a plurality of intermediate points;
the unmanned aerial vehicle flight safety threshold information comprises a first threshold range and a second threshold range, the first threshold range and the second threshold range are spherical areas taking the central position of the unmanned aerial vehicle as a sphere center, and the radius of the first threshold range is smaller than that of the second threshold range.
3. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method according to claim 1, wherein the step of acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time comprises:
the position information of the unmanned aerial vehicle and the surrounding environment data information are transmitted through multiple channels; and
the surrounding environment data information is at least six azimuth data information of the unmanned aerial vehicle in the flying process, namely front/back/left/right/up/down, and circumferential data information of the unmanned aerial vehicle in the flying direction;
the azimuth data information and the circumferential data information are acquired through a laser radar.
4. The method for automatically avoiding an obstacle of an unmanned aerial vehicle based on cloud computing according to claim 2, wherein the step of determining whether an obstacle exists on a flight path of the unmanned aerial vehicle based on the surrounding environment data information, if the obstacle exists, generating an avoidance instruction, and if the obstacle does not exist, generating a continuing flight instruction comprises:
acquiring perception data of the laser radar, and judging whether the distance data perceived by the laser radar is positioned outside the flight safety threshold information;
if the circumferential data information is located outside the safety threshold, the unmanned aerial vehicle runs according to a preset track route;
if the circumferential data information is located within the safety threshold, judging whether the circumferential data is located within the first threshold range or the second threshold range;
when the circumferential data is within the first threshold range, executing an emergency braking program and generating a first early warning signal;
and executing a deceleration braking program and generating a second early warning signal when the circumferential data are within the second threshold range.
5. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method according to claim 4, wherein the deceleration braking program is executed based on flight speed data of the unmanned aerial vehicle, and acceleration required for unmanned aerial vehicle braking is obtained by querying an initial speed-acceleration comparison table.
6. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method of claim 5, further comprising the steps of:
synchronously transmitting the generated first early warning signal or second early warning signal to a manual server;
the artificial server feedback signal is obtained, and the feedback signal comprises: automatic connection and manual control;
if the feedback signal is manual control, the unmanned aerial vehicle ends an automatic control program and adopts manual control;
and if the feedback signal is an automatic takeover, executing an avoidance program.
7. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method of claim 6, wherein the performing an avoidance procedure comprises:
obtaining optimized track data;
according to the preset flight speed, flying along the optimized track data;
the optimized track data comprise an end position, and when the unmanned aerial vehicle is located at the end position, whether the distance data information perceived by the laser radar of the unmanned aerial vehicle is located outside the safety threshold value or not is judged.
8. The cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method of claim 7, further comprising:
acquiring space coordinate data of the end point position;
based on the space coordinate data of the end point position and the vector value of the nearest point position of the flight track information;
and executing a reset program based on the vector value, so that the unmanned aerial vehicle returns to a preset flight track.
9. An obstacle avoidance system applying the cloud computing-based unmanned aerial vehicle automatic obstacle avoidance method according to any one of claims 1 to 8, comprising a cloud computing server, a manual server and an execution server, wherein the cloud computing server, the manual server and the execution server are in communication connection, and the obstacle avoidance system comprises:
the space coordinate module is used for establishing a space coordinate system based on the flight environment of the unmanned aerial vehicle;
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring initial data preset when the unmanned aerial vehicle flies, and the initial data comprise flight track information, flight speed information and flight safety threshold information of the unmanned aerial vehicle;
the data receiving module is used for acquiring the position information of the unmanned aerial vehicle and the surrounding environment data information of the unmanned aerial vehicle in real time;
the data processing module is used for judging whether an obstacle exists on the flight route of the unmanned aerial vehicle or not based on the surrounding environment data information, generating an avoidance instruction if the obstacle exists, and generating a continuous flight instruction if the obstacle does not exist;
and the data sending module is used for sending the avoidance instruction or the continuous flight instruction to the unmanned aerial vehicle.
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