CN115167524A - Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium - Google Patents
Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN115167524A CN115167524A CN202210991219.7A CN202210991219A CN115167524A CN 115167524 A CN115167524 A CN 115167524A CN 202210991219 A CN202210991219 A CN 202210991219A CN 115167524 A CN115167524 A CN 115167524A
- Authority
- CN
- China
- Prior art keywords
- route
- target
- unmanned aerial
- aerial vehicle
- planning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000002159 abnormal effect Effects 0.000 claims description 28
- 238000004590 computer program Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 4
- 238000004891 communication Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 241001061260 Emmelichthys struhsakeri Species 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000010485 coping Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010006 flight Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for planning a patrol route of an unmanned aerial vehicle. The method comprises the following steps: acquiring a three-dimensional live-action model and a route planning parameter of a target area; and planning the route in the three-dimensional real scene model according to the route planning parameters to obtain a target route. The problem of the navigation line that current unmanned aerial vehicle tours the navigation line planning method and plans has unmanned aerial vehicle to patrol the effect relatively poor is solved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method, a device, equipment and a storage medium for planning a patrol route of an unmanned aerial vehicle.
Background
At present, unmanned aerial vehicles are widely applied to the fields of automatic patrol apparent power transmission and power distribution, and the unmanned aerial vehicles are mainly manually patrolled in the aspects of patrolling at the starting stage in the field of power transformation. The manual inspection is difficult to keep high special attention and has the inspection dead angle, and unmanned aerial vehicle replaces the operating personnel of transformer substation to carry out the work of inspecting, can solve above problem.
At present, a method for planning an automatic driving route of an unmanned aerial vehicle based on a point cloud model is popularized. Due to the fact that equipment in the transformer substation is distributed densely, the point cloud model of the transformer substation is difficult to present the exact position and the orientation of the meter, noise exists in the point cloud model of the transformer substation, safety distance inspection of a route is not facilitated, and perfect denoising is difficult to achieve when the point cloud model of the transformer substation is used. Based on the prior art scheme, the problem that the unmanned aerial vehicle patrols the poor effect of the navigation of the unmanned aerial vehicle exists in the navigation route planned by the existing unmanned aerial vehicle patrolling navigation route planning method.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for planning a patrol route of an unmanned aerial vehicle, which are used for solving the problem of poor patrol effect of the unmanned aerial vehicle in the route planned by the conventional method for planning the patrol route of the unmanned aerial vehicle.
According to one aspect of the invention, an unmanned aerial vehicle patrol route planning method is provided, which comprises the following steps:
acquiring a three-dimensional live-action model and a route planning parameter of a target area;
and planning the route in the three-dimensional real scene model according to the route planning parameters to obtain a target route.
According to another aspect of the present invention, there is provided an unmanned aerial vehicle patrol route planning apparatus, comprising:
the acquisition module is used for acquiring a three-dimensional real scene model and a route planning parameter of a target area;
and the route planning module is used for planning routes in the three-dimensional real scene model according to the route planning parameters to obtain the target route.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method for planning a patrol route of an unmanned aerial vehicle according to any of the embodiments of the present invention.
According to another aspect of the invention, there is provided a computer readable storage medium having stored thereon computer instructions for causing a processor to execute a method of planning a cruising route of an unmanned aerial vehicle according to any one of the embodiments of the invention.
According to the technical scheme of the embodiment of the invention, the three-dimensional real-scene model and the route planning parameters of the target area are obtained, and the route planning is carried out in the three-dimensional real-scene model according to the route planning parameters, so that when the unmanned aerial vehicle patrols along the planned route, the unmanned aerial vehicle can shoot the expected image of the target object and effectively avoid surrounding obstacles, and the safety and the effectiveness of the unmanned aerial vehicle patrol can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for planning a patrol route of an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a block diagram of a structure of a cruising route planning apparatus for an unmanned aerial vehicle according to a second embodiment of the present invention;
fig. 3 is a block diagram of another unmanned aerial vehicle cruising route planning apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing the method for planning the patrol route of the unmanned aerial vehicle according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for planning an cruising route of an unmanned aerial vehicle according to an embodiment of the present invention, where this embodiment is applicable to determining a cruising route for a cruising operation of an unmanned aerial vehicle in a transformer substation, and the method may be executed by an unmanned aerial vehicle cruising route planning apparatus, where the unmanned aerial vehicle cruising route planning apparatus may be implemented in a form of hardware and/or software, and the unmanned aerial vehicle cruising route planning apparatus may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, obtaining a three-dimensional real scene model and a flight path planning parameter of the target area.
Wherein, the target area is the region that unmanned aerial vehicle carried out the operation of patrolling, for example transformer substation.
The route planning parameters are parameters required to be acquired for planning a target route of the unmanned aerial vehicle, and the route planning parameters can include course angle, holder angle, shooting distance and the like.
The target routes include an overpass route and/or a underpassing route. The upper-crossing type air route refers to an air route without equipment above the whole air route, the lower-crossing type air route refers to an air route without equipment below the whole air route, and charged equipment is not allowed to exist above and below any point of the air route at the same time.
The three-dimensional live-action model is created based on RTK (real-time kinematic). The composition of an RTK includes a base station and a rover station. The RTK principle is to install a reference station at a known position on the ground, where the reference station is a station that is no longer moving after being established in a certain place, and continuously observes satellite navigation signals for a long time, and transmits the observation data to a fixed observation station on the ground in real time or at regular time through a communication facility. The reference station can start to compare the position signal transmitted by the satellite with the actual position information of the reference station after receiving the satellite signal, find the positioning error in the transmission process, then send the error to the rover station which continuously moves along with the point to be measured, and the rover station corrects the error according to the satellite signal received in real time, so that the accurate position of the rover station is obtained. The unmanned aerial vehicle carrying the RTK module is used for carrying out oblique photography of the transformer substation, so that images with high-precision longitude and latitude and other geographic information can be shot.
The oblique photography technology is to acquire images from five different angles such as vertical angle, four oblique angles and the like through an unmanned aerial vehicle, and acquire the surface of an object and the high-resolution texture of side view. The traditional vertical photography can only acquire object top information, and cannot acquire object side information, so that a target area can be observed from multiple angles, and the actual situation of a transformer substation is reflected more truly. Measurements including height, length, area, angle, slope, etc. attributes are obtained.
Furthermore, the unmanned aerial vehicle carrying the RTK module finishes oblique photography of the transformer substation to obtain a large number of images with high-precision geographic information in multiple angles, and then uses professional software, such as EFS software and Change Finder software, the professional software integrates the functions of multi-angle oblique image browsing, measuring, inquiring and the like, data are fused and reconstructed, the measurement of height, length, area, angle, gradient and the like can be carried out, and a real and high-precision transformer substation three-dimensional live-action model can be obtained. The three-dimensional real-scene model comprises a three-dimensional real-scene model, a three-dimensional real-scene model and a three-dimensional real-scene model, wherein each meter of the equipment in the three-dimensional real-scene model, such as each oil level meter, each barometer of a transformer substation, a meter of action times and the like, is complete in appearance and distinguishable in orientation, and characters on a nameplate of the equipment are clearly displayed. The three-dimensional live-action model of the transformer substation is used as a high-precision three-dimensional map of an unmanned aerial vehicle automatic driving route and is a basic condition for planning a patrol route of the unmanned aerial vehicle.
The air route planning parameters are parameters for planning an unmanned aerial vehicle patrol air route and comprise first air route planning parameters or second air route planning parameters. The first air route planning parameter comprises an aerial shooting point, and the second air route planning parameter comprises a coordinate, a course angle and a holder angle.
In one embodiment, in order to obtain the target route, first route planning parameters which are determined based on the three-dimensional real-scene model and contain aerial shooting points are acquired, and then the aerial shooting points are connected point by point to form the patrol route. The method for drawing the aerial photography point can comprise the following steps:
a1, three-point positioning drawing method: when selecting an aerial photography point, particularly an aerial photography point for a shooting meter, the screen viewing angle needs to be adjusted to enable the aerial photography point to be located in the middle of a display interface as far as possible. In actual use, the selection of the aerial photography point can be performed in the preview mode.
a2, firstly setting points and then photographing: the method comprises the steps of firstly vertically taking points to the ground to determine the position of the unmanned aerial vehicle, then modifying/increasing the photographing action, wherein the photographing distance is the ground clearance of the unmanned aerial vehicle. The method is suitable for planning the aerial shooting points for daily tour in the channel.
and a3, when an obstacle is detected between the two aerial photography points, increasing the set number of auxiliary aerial photography points between the two aerial photography points. The set number can be determined based on the distance from one aerial shooting point to another aerial shooting point under the condition of avoiding the obstacles, the auxiliary aerial shooting point is only used for planning a patrol route, and the unmanned aerial vehicle does not perform photographing operation when flying to the auxiliary aerial shooting point.
And S120, planning the route in the three-dimensional real scene model according to the route planning parameters to obtain a target route.
The target route is preferably horizontal and vertical, so that the situation that the unmanned aerial vehicle flies obliquely is avoided. Compared with parallel flight, the probability of faults occurring in oblique flying of the unmanned aerial vehicle is higher.
Further, after the route planning is carried out in the three-dimensional real scene model according to the route planning parameters to obtain a target route, the method also comprises the following steps:
step b1, acquiring a point cloud model of the target area and the spatial distribution of the target route in the point cloud model.
Therein, a point cloud model is determined based on radar scan data of a target area, which may clearly show linear objects such as wires, frames, etc.
The three-dimensional live-action model of the transformer substation often has the condition that a wire part is lost, so that the position of the wire is difficult to judge when the route is planned based on the three-dimensional live-action model.
And b2, in the point cloud model, determining whether a first type of target object exists in a first set neighborhood range of the target route, wherein the first set neighborhood range is greater than or equal to a safety distance corresponding to the first type of target object, and the first type of target object is a linear obstacle.
The first set neighborhood range is a region where the first type of target object is forbidden to appear. The first type of target object may be a wire-type obstacle such as a wire and a frame around the device. The safety distance corresponding to the first type of target object is a safety distance created for the first type of target object in advance. When the distance between the first type of target object and the target air route is greater than or equal to the safe distance, the unmanned aerial vehicle can normally fly in a normal state, and when the distance between the first type of target object and the target air route is smaller than the safe distance, the unmanned aerial vehicle can fly abnormally.
And b3, if the first type of target object exists, optimizing the target route according to the position relation between the first type of target object and the target route so as to update the target route.
If a first type of target object exists in a first neighborhood range of the target air route, the target air route needs to be optimized to update the target air route according to the position relation between the first type of target object and the target air route in the point cloud model. Wherein the positional relationship includes, but is not limited to, parallel, intersecting, overlapping, etc.
Further, before step b1, the following steps are also included:
step c1, in the three-dimensional real-scene model, determining whether a second type of target object exists in a second set neighborhood range of the target air route, wherein the second set neighborhood range is larger than or equal to a safety distance corresponding to the second type of target object, and the second type of target object is a nonlinear obstacle.
And the second set neighborhood range is a region in which the second type target object is forbidden to appear. The second type of target object is a non-linear obstacle, which may be a device, a building, etc. The safety distance corresponding to the second type of target object is a safety distance created for the second type of target object in advance. When the distance between the second type of target object and the target air route is greater than or equal to the safe distance, the unmanned aerial vehicle can normally fly in a normal state, and when the distance between the second type of target object and the target air route is smaller than the safe distance, the unmanned aerial vehicle can fly abnormally.
And c2, if the second type of target object exists, optimizing the target air route again according to the position relation between the second type of target object and the target air route so as to update the target air route again.
If a second type of target object exists in a second neighborhood range of the target route, the target route needs to be optimized to update the target route according to the position relation between the second type of target object and the target route in the point cloud model. Wherein the positional relationship includes, but is not limited to, crossing, overlapping, etc.
Further, after the step c2, the following steps are also included:
and d1, acquiring real-time flight data of the unmanned aerial vehicle test flight along a target air route, wherein the real-time flight data comprises an RTK signal and real-time air route data.
The real-time flight data is real-time data generated in the process that the unmanned aerial vehicle flies along a target route, and the real-time flight data comprises but is not limited to RTK signals and real-time route data.
When the unmanned aerial vehicle takes a test flight, all parts of hardware of the unmanned aerial vehicle need to be normal and intact, for example, the appearance of the unmanned aerial vehicle is intact and has no damage, and the lens surface has no scratch and damage; the protective cover of the holder is detached, and the holder is intact; the propeller is firmly installed; the electric quantity of the battery and the remote controller battery is full, the battery has no abnormal heating or bulge, and the battery is installed in place; the sensitivity of the mode switching key, the roller and the shortcut key is normal; adequate capacity and correct installation of the SD card, etc.; unmanned aerial vehicle remote control parameter has correctly set up, if the height of navigating backwards, ensures that unmanned aerial vehicle navigates backwards highly to be higher than the highest equipment height of waiting to tour the transformer substation. The flight mode is set to the RTK mode. Regarding the RTK mode, because in an environment where the number of substation devices is large and the distribution is dense, the unmanned aerial vehicle must be capable of performing automatic inspection work under the condition that the RTK signal is good and the RTK signal is in a fixed solution. When the unmanned aerial vehicle tries to fly, abnormal information such as compass calibration abnormality and RTK signal difference does not need to be set. The 'permission to switch flight modes' is opened, and the visual obstacle avoidance function is closed. And closing the vision obstacle avoidance function, because the substation equipment is densely distributed, the unmanned aerial vehicle is often required to finish shooting in a short distance, if the vision obstacle avoidance function is opened, the unmanned aerial vehicle can automatically hover once being 2 meters away from the equipment, and the air route task cannot be smoothly finished.
And d2, when abnormal data exist in the real-time flight data, stopping the trial flight operation of the unmanned aerial vehicle according to the abnormal data, and simultaneously optimizing the target air route again according to the occurrence area of the abnormal data so as to update the target air route again, wherein the abnormal data comprise RTK signal abnormal data or deviation data of the real-time air route corresponding to the real-time air route data from the target air route.
Further, the following steps are also included after step d 1:
and e1, if the real-time flight data has no abnormal data, acquiring the real-time flight data of the set number of test flights of the unmanned aerial vehicle along the target air route again.
The unmanned aerial vehicle flight data is abnormal, and the unmanned aerial vehicle loses position signals and the like due to disconnection of the unmanned aerial vehicle and a remote controller and network disconnection.
It can be understood that if there is no abnormal data in the real-time flight data, it indicates that the current trial flight of the unmanned aerial vehicle is smooth. In order to ensure the shooting effect of the unmanned aerial vehicle flying along the target air route, the present embodiment obtains the real-time flight data of the set number of times of the unmanned aerial vehicle flying along the target air route again, for example, obtains the real-time flight data of the unmanned aerial vehicle flying along the target air route twice again.
And e2, if the real-time flight data with the set times have no abnormal data, outputting a successful lane planning identifier.
If the real-time flight data generated by the unmanned aerial vehicle after the unmanned aerial vehicle is continuously set for times (such as three times) of trial flight has no abnormal data, the target air route is judged to meet the set requirement, and an air route planning success identifier is output.
Whether the real-time flight data generated by the trial flight times of the unmanned aerial vehicle have abnormal data or not can be verified in the actual flight environment to determine whether the target air line can be used for cruising of the unmanned aerial vehicle or not, and the method is simple, direct and effective.
In order to deal with the abnormal flight data of the unmanned aerial vehicle, an out-of-control coping strategy can be set in advance, and the unmanned aerial vehicle is prevented from colliding station equipment. For example, when the airline is an overpass type, the out-of-control coping strategy should be set to return flight (including a return flight height, a return flight point and the like) in advance, and when the unmanned aerial vehicle detects that an emergency is encountered, the unmanned aerial vehicle can fly vertically upwards until the set return flight height, fly horizontally to the return flight point, and finally descend vertically to the return flight point. When the air route is of an upward-crossing type, no equipment is arranged right above the whole unmanned aerial vehicle path, but equipment can be arranged below the whole unmanned aerial vehicle path, so that the runaway coping strategy cannot adopt landing or hovering. Similarly, when the airline is the underpass type, there is not equipment under the whole unmanned aerial vehicle route of underpass type airline, but the top may have metal framework, the high pressure overline of taking the point etc. consequently the reply strategy of out of control can not adopt and return to the journey, and the reply strategy of out of control should set up in advance to descend, when unmanned aerial vehicle detected to meet with emergency, unmanned aerial vehicle vertical landing.
According to the technical scheme of the embodiment of the invention, the three-dimensional real-scene model and the route planning parameters of the target area are obtained, and route planning is carried out in the three-dimensional real-scene model according to the route planning parameters, so that when the unmanned aerial vehicle patrols along the planned route, the unmanned aerial vehicle not only can shoot the expected image of the target object, but also can effectively avoid surrounding obstacles, and therefore, the safety and the effectiveness of the unmanned aerial vehicle patrolling can be improved.
Example two
Fig. 2 is a block diagram of a structure of a cruising route planning apparatus for an unmanned aerial vehicle according to a second embodiment of the present invention. The embodiment can be applied to the condition that the target route is obtained by acquiring the route planning parameters and planning the route in the process of the unmanned aerial vehicle patrol.
As shown in fig. 2, the specific structure of the unmanned aerial vehicle patrol route planning device disclosed in this embodiment is as follows:
the obtaining module 201 is configured to obtain a three-dimensional real-scene model and a route planning parameter of a target area.
And the route planning module 202 is used for planning routes in the three-dimensional real scene model according to the route planning parameters to obtain target routes.
Optionally, as shown in fig. 3, the apparatus further includes an optimization module 203, configured to obtain a point cloud model of the target area and a spatial distribution of the target route in the point cloud model;
in the point cloud model, determining whether a first type of target object exists in a first set neighborhood range of a target route, wherein the first set neighborhood range is greater than or equal to a safety distance corresponding to the first type of target object, and the first type of target object is a linear obstacle;
and if the first type of target objects exist, optimizing the target route according to the position relation between the first type of target objects and the target route so as to update the target route.
Optionally, the optimization module 203 is further configured to determine, in the three-dimensional real-world model, whether a second type of target object exists in a second set neighborhood range of the target route, where the second set neighborhood range is greater than or equal to a safety distance corresponding to the second type of target object, and the second type of target object is a non-linear obstacle.
And if the second type of target object exists, optimizing the target air route again according to the position relation between the second type of target object and the target air route so as to update the target air route again.
Optionally, the optimization module 203 is further configured to acquire real-time flight data of the drone flying along the target flight path, where the real-time flight data includes an RTK signal and real-time flight path data. When the real-time flight data are detected to have abnormal data, the test flight operation of the unmanned aerial vehicle is stopped according to the abnormal data, meanwhile, the target air route is optimized again according to the occurrence area of the abnormal data so as to update the target air route again, and the abnormal data comprise RTK signal abnormal data or deviation data of the real-time air route corresponding to the real-time air route data, wherein the deviation data of the real-time air route deviates from the target air route.
Optionally, the optimization module 203 may be further configured to, after obtaining the real-time flight data of the unmanned aerial vehicle pilot flying along the target route, obtain the real-time flight data of the unmanned aerial vehicle pilot flying along the target route for the set number of times again if there is no abnormal data in the real-time flight data; and if no abnormal data exist in the real-time flight data of the set times, outputting a successful air route planning identifier.
According to the technical scheme, the three-dimensional real-scene model and the air route planning parameters of the target area are obtained through mutual matching of the modules, and air route planning operation is carried out in the three-dimensional real-scene model according to the air route planning parameters, so that when the unmanned aerial vehicle patrols along the planned air route, the unmanned aerial vehicle can shoot the expected image of the target object and effectively avoid surrounding obstacles, and therefore the safety and effectiveness of unmanned aerial vehicle patrolling can be improved.
The unmanned aerial vehicle patrol route planning device provided by the embodiment of the invention can execute the unmanned aerial vehicle patrol route planning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
FIG. 4 shows a schematic block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, a method of unmanned aerial vehicle cruise route planning may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a drone cruise route planning method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An unmanned aerial vehicle patrol route planning method is characterized by comprising the following steps:
acquiring a three-dimensional live-action model and a route planning parameter of a target area;
and planning the route in the three-dimensional real scene model according to the route planning parameters to obtain a target route.
2. The method of claim 1, wherein after performing the route planning in the three-dimensional real scene model according to the route planning parameters to obtain the target route, further comprising:
acquiring a point cloud model of a target area and the spatial distribution of the target route in the point cloud model;
determining whether a first type of target object exists in a first set neighborhood range of the target route in the point cloud model, wherein the first set neighborhood range is larger than or equal to a safety distance corresponding to the first type of target object, and the first type of target object is a linear obstacle;
and if the first type of target objects exist, optimizing the target air route according to the position relation between the first type of target objects and the target air route so as to update the target air route.
3. The method of claim 2, wherein the obtaining a point cloud model of a target area and the spatial distribution of the target course in the point cloud model further comprises:
in the three-dimensional real-scene model, determining whether a second type of target object exists in a second set neighborhood range of the target route, wherein the second set neighborhood range is greater than or equal to a safety distance corresponding to the second type of target object, and the second type of target object is a nonlinear obstacle;
and if the second type of target object exists, optimizing the target air route again according to the position relation between the second type of target object and the target air route so as to update the target air route again.
4. The method of claim 3, further comprising, after said optimizing said target route again according to the positional relationship between said second type of target object and said target route to update said target route again, if said second type of target object exists:
acquiring real-time flight data of the unmanned aerial vehicle trying to fly along the target air route, wherein the real-time flight data comprises an RTK signal and real-time air route data;
when the real-time flight data are detected to have abnormal data, stopping the trial flight operation of the unmanned aerial vehicle according to the abnormal data, and simultaneously optimizing the target air route again according to the occurrence area of the abnormal data so as to update the target air route again, wherein the abnormal data comprise RTK signal abnormal data or deviation data of the real-time air route corresponding to the real-time air route data deviating from the target air route.
5. The method of claim 4, wherein after obtaining the real-time flight data of the drone for test flight along the target route, further comprising:
if the real-time flight data have no abnormal data, acquiring the real-time flight data of the unmanned aerial vehicle which is tried to fly along the target air route for a set number of times again;
and if no abnormal data exist in the real-time flight data of the set times, outputting a successful air route planning identifier.
6. The method of any one of claims 1-5, wherein the route planning parameters include a first route planning parameter or a second route planning parameter, wherein the first route planning parameter includes an aerial point and the second route planning parameter includes coordinates, a heading angle, and a pan-tilt angle.
7. The method of claim 6, wherein the target pattern comprises an overpass pattern and/or an underpassing pattern.
8. The utility model provides an unmanned aerial vehicle tours airline planning device which characterized in that includes:
the acquisition module is used for acquiring a three-dimensional real scene model and a route planning parameter of a target area;
and the route planning module is used for planning routes in the three-dimensional real scene model according to the route planning parameters to obtain target routes.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of unmanned aerial vehicle cruise route planning of any of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to, when executed, implement the method of unmanned aerial vehicle cruise route planning of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210991219.7A CN115167524A (en) | 2022-08-18 | 2022-08-18 | Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210991219.7A CN115167524A (en) | 2022-08-18 | 2022-08-18 | Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115167524A true CN115167524A (en) | 2022-10-11 |
Family
ID=83480781
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210991219.7A Pending CN115167524A (en) | 2022-08-18 | 2022-08-18 | Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115167524A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117111641A (en) * | 2023-10-25 | 2023-11-24 | 天津云圣智能科技有限责任公司 | Unmanned aerial vehicle route deviation rectifying method, device, equipment and storage medium |
| CN117288207A (en) * | 2023-11-24 | 2023-12-26 | 天津云圣智能科技有限责任公司 | Route planning method and device for three-dimensional modeling, electronic equipment and storage medium |
-
2022
- 2022-08-18 CN CN202210991219.7A patent/CN115167524A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117111641A (en) * | 2023-10-25 | 2023-11-24 | 天津云圣智能科技有限责任公司 | Unmanned aerial vehicle route deviation rectifying method, device, equipment and storage medium |
| CN117111641B (en) * | 2023-10-25 | 2024-01-19 | 天津云圣智能科技有限责任公司 | Unmanned aerial vehicle route deviation rectifying method, device, equipment and storage medium |
| CN117288207A (en) * | 2023-11-24 | 2023-12-26 | 天津云圣智能科技有限责任公司 | Route planning method and device for three-dimensional modeling, electronic equipment and storage medium |
| CN117288207B (en) * | 2023-11-24 | 2024-02-20 | 天津云圣智能科技有限责任公司 | Route planning method and device for three-dimensional modeling, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20240201240A1 (en) | Unmanned Aerial Vehicle Electromagnetic Avoidance And Utilization System | |
| CN115167524A (en) | Unmanned aerial vehicle patrol route planning method, device, equipment and storage medium | |
| US10509417B2 (en) | Flight planning for unmanned aerial tower inspection with long baseline positioning | |
| US20200004272A1 (en) | System and method for intelligent aerial inspection | |
| CN111537515A (en) | Iron tower bolt defect display method and system based on three-dimensional live-action model | |
| WO2023279695A1 (en) | Power transmission and transformation project acceptance method based on bim and point cloud measurement | |
| CN111415409B (en) | Modeling method, system, equipment and storage medium based on oblique photography | |
| CN111958592A (en) | Image semantic analysis system and method for transformer substation inspection robot | |
| CN109946729B (en) | Aerial target tracking method and device | |
| CN113075938A (en) | Remote intelligent inspection system and method for power transmission line | |
| CN213302860U (en) | Three-dimensional visual obstacle avoidance system of unmanned aerial vehicle | |
| CN113077561A (en) | Intelligent inspection system for unmanned aerial vehicle | |
| CN111244822B (en) | Fixed-wing unmanned aerial vehicle line patrol method, system and device in complex geographic environment | |
| CN107424440B (en) | Aircraft approach landing monitoring system | |
| CN112954599A (en) | Post-disaster unmanned aerial vehicle communication system | |
| CN114140759A (en) | High-precision map lane line position determining method and device and automatic driving vehicle | |
| CN111578861A (en) | Power distribution network tree obstacle detection method and system | |
| CN115719436A (en) | Model training method, target detection method, device, equipment and storage medium | |
| CN114820800A (en) | Real-time inspection method and equipment for power transmission line | |
| CN116594428A (en) | Method and device for generating patrol route, electronic equipment and storage medium | |
| CN116659503A (en) | Routing inspection route planning method and device, electronic equipment and storage medium | |
| CN109358640B (en) | Real-time visualization method and system for laser detection of unmanned aerial vehicle and storage medium | |
| CN113111714A (en) | Risk prevention and control system applied to power transmission field operation | |
| CN112698662A (en) | Unmanned aerial vehicle substation equipment fine routing inspection remote control system and method | |
| Huang et al. | Research and design of space-sky-ground integrated transmission line inspection platform based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |