CN104965518B - Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map - Google Patents
Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map Download PDFInfo
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Abstract
The invention discloses a kind of electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map in polling transmission line flying robot's flight course planning technique study technical field.Including gathering the three-dimensional data information of power line Terrain atural object by laser scanner technique, and build corresponding Three-dimensional Numeric Map；According to Three-dimensional Numeric Map dyspoiesis thing map, the safe flight path of inspection flying robot is established；If prominent meet the weather condition for being unfavorable for flying robot's inspection, emergency landing is taken immediately, makes flying robot's safe landing nearby, it is again etc. to be recycled after emergency situations terminate；If without special weather situation, judge whether patrol task is completed after crusing robot inspection setting time；If patrol task is completed, maked a return voyage by former safe flight route；If patrol task is not completed, autonomous hedging is carried out according to the safe flight path of foundation.
Description
Technical field
It is more particularly to a kind of the invention belongs to polling transmission line flying robot's flight course planning technique study technical field
Electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map.
Background technology
With the development of science and technology the tour of transmission line of electricity is via initial artificial inspection slowly by flying robot's inspection
Substituted.So, the work risk of staff just greatly reduces.But at the same time, due to power line Terrain,
The adverse circumstances such as atural object, meteorology factor and flying robot itself run into the influence of emergency situations, cause inspection flying machine
People needs to carry out path planning and urgent danger prevention.
Current overhead transmission line inspection flying robot preferably can carry out inspection to transmission line of electricity, but patrol
The safe avoidance flight path established depending on during seldom considers prominent chance adverse circumstances during flying robot's operation in the air
(storm etc.) and dynamic barrier (such as occurring some birds suddenly), also come without a set of perfect flight course planning method
Solve these emergency situations.
The content of the invention
For in place of above-mentioned the deficiencies in the prior art, the present invention proposes that a kind of electric inspection process based on Three-dimensional Numeric Map flies
Row robot flight course planning method, it is characterised in that this method includes：
Step 1：The three-dimensional data information of power line Terrain atural object is gathered by laser scanner technique, and is built corresponding
Three-dimensional Numeric Map；
Step 2：According to Three-dimensional Numeric Map dyspoiesis thing map, and utilize the high frequency probability 3D mappings based on Octree
Framework generates global context map, then establishes the certain semantic model by mission requirements automatically by airborne comprehensive sensor,
So as to the paths planning method that is combined using local multiresolution avoidance with layering structure, the peace of inspection flying robot is established
Full flight path；
Step 3：If prominent meet the weather condition for being unfavorable for flying robot's inspection, emergency landing is taken immediately, makes to fly
The safe landing nearby of row robot, it is again etc. to be recycled after emergency situations terminate；
Step 4：If without special weather situation, judge whether patrol task is complete after crusing robot inspection setting time
Into；If patrol task is completed, maked a return voyage by former safe flight route；If patrol task is not completed, according in step 2
Safe flight path carry out autonomous hedging.
Local multiresolution avoidance in the step 2 includes following son with the paths planning method that layering structure is combined
Step：
Sub-step 101：It is the task incipient stage to set top, and surrounding is accessible, provides a series of destinations, inspection in advance
Flying robot flies in flight course according to these destinations；Because global context map will not change in these destinations, because
This uses Global motion planning when beginning a task with；
In global context map caused by the high frequency probability 3D mapping frameworks based on Octree, each node of Octree
Represent a cubic volume for being referred to as voxel；Wherein, leaf node n is z in sensor measurement distance1:tWhen probability tables
It is shown asThe formula is by current distance zt, P (n) Prior Probabilities
It is z with sensor measurement distance1:t-1When probability P (n | z1:t-1) determine；This method can not only take known spatial, and
Unknown space can also arbitrarily be taken；
Finally, Global motion planning enters next layer, local multiresolution path planning layer as input layer；
It is described it is top be mission planning layer and global path planning layer, respectively by certain semantic model and global context
Figure is drawn；
Sub-step 102：In local multiresolution path planning layer, using the path planning based on grid, use it is multiple with
The three-dimensional grid that size centered on inspection flying robot is M × M × M, it is by recursive method, these grids are embedding each other
Enter, obtain the uniform grid that size is N × N × N, (log is included in each M × M × M grid2(N/M)+1)M3Individual unit；
The local multiresolution path planning layer is obtained by airborne sensor, global context map and partial barriers map
Arrive；
Sub-step 103：For the planning in grid, it is necessary to which an embedded non-directed graph, grid inside connection surrounding are all
Grid；In the grid centered on inspection flying robot, path planning is carried out using a kind of graph search algorithm, is finally given
Most short maximally effective path in grid；
Sub-step 104：In lowermost layer, break the barriers map and motion model is established one and repelled based on adaptive obstacle
The local avoidance layer in domain is as safe floor；All barriers are considered as the place for having highest repulsion degree, and robot is always from height
The place of repulsion degree moves to the place of low repulsion degree.
The global context map is generated using the high frequency probability 3D mapping frameworks based on Octree.
The certain semantic model is the automatic foundation by mission requirements by airborne comprehensive sensor.
The partial barriers map is generated by Three-dimensional Numeric Map.
Sub-step 105：The design in the safe flight path will consider that the safe distance of livewire work, flying robot exist
The interference of electromagnetic field is avoided during flight while ensures the sampled point nearest from fixed obstacle enough far to allow flying robot
One minimum adaptive radius of turn ρmin, wherein,Wherein, V is the constant row of flying robot
Sail speed, ΦmaxBe the maximum deflection angle convolution of flying robot can avoiding obstacles, without any collision.
Brief description of the drawings
Fig. 1 is inspection flying robot's flight course planning flow chart of the invention based on Three-dimensional Numeric Map.
Fig. 2 is inspection flying robot part multiresolution avoidance and layering structure combination path planning process figure.
Embodiment
Method proposed by the present invention is further described with reference to the accompanying drawings and examples.
It is as shown in Figure 1 inspection flying robot's flight course planning flow chart of the invention based on Three-dimensional Numeric Map；The party
Method concretely comprises the following steps：
1) the three-dimensional data information of power line Terrain atural object is gathered by laser scanner technique, and builds corresponding three
Dimension word map；
2) according to Three-dimensional Numeric Map dyspoiesis thing map, and the high frequency probability 3D mapping frameworks based on Octree are utilized
Global context map is generated, establishes the certain semantic model by mission requirements automatically by airborne comprehensive sensor in addition, from
And the paths planning method being combined using local multiresolution avoidance with layering structure, establish the safety of inspection flying robot
Flight path.
It is illustrated in figure 2 inspection flying robot part multiresolution avoidance and layering structure combination path planning process
Figure, the paths planning method concretely comprise the following steps：
First, it is mission planning layer and global path planning layer to design top, respectively by semantic model and global context
Map is drawn.This two layers is the task incipient stage, and surrounding is accessible, therefore can be flown taking human as a series of destinations are provided in advance
Robot must fly in flight course according to these destinations.Because environmental map will not change in these destinations, therefore
Global motion planning is used when beginning a task with.In global context map caused by the high frequency probability 3D mapping frameworks based on Octree,
One cubic volume for being referred to as voxel of each node on behalf of Octree.Wherein, leaf node n is in sensor measurement distance
For z1:tWhen probability be expressed asThe formula is by current distance zt、
P (n) Prior Probabilities and sensor measurement distance are z1:t-1When probability P (n | z1:t-1) determine.This method can not only take
Known spatial, but also can arbitrarily take unknown space.Finally, Global motion planning enters next layer as input layer, local more
Resolution ratio path planning layer.
Secondly, local multiresolution path planning layer is designed, the layer is by airborne sensor, global context map and local barrier
Thing map is hindered to obtain.In local multiresolution path planning layer, using the path planning based on grid, and work as distance increase
When, the resolution ratio of grid is reduced, when distance reduces, improves resolution accuracy.For the path planning layer, use multiple with flight
The three-dimensional grid that size centered on robot is M × M × M, by recursive method, these grids is embedded in each other, obtained
Size is N × N × N uniform grid, and (log is included in each M × M × M grid2(N/M)+1)M3Individual unit.For net
Planning in lattice is, it is necessary to an embedded non-directed graph, all grids of grid inside connection surrounding.In the net centered on robot
In lattice, path planning is carried out using a kind of graph search algorithm.In the searching algorithm, to connect the section of eight most inside cell grids
Point as search for start node, the cost for traveling through border be by between the obstacle cost of its institute's join domain and central area by
What boundary length two parts that Euclidean distance is drawn were tried to achieve, i.e. f (n)=g (n)+d (n).The barrier cost g (n) is side
Fate number is multiplied by the length of each unit, and the Euclidean distance d (n) to object element is obtained by heuristic search, wherein,Finally give most short maximally effective path in grid.
Finally, break the barriers map and motion model one local avoidance layer based on adaptive obstacle region of rejection of design
As safe floor, it is the bottom of the structure.All barriers are regarded as the place of highest repulsion degree, flying machine
These barriers can be repelled during people's inspection automatically, target point is considered as the place of minimum repulsion degree, therefore flying robot
Movement locus the place of low repulsion degree is always moved to from the place of high repulsion degree.If the positional information of flying robot is
qRobot=[x,y,z]T, target position information qGoal=[xgoal,ygoal,zgoal]T, the position of i-th of barrier is qobstacle
(i)=[xobstacle,yobstacle,zobstacle]T, then the repulsion degree function of robot to i-th of barrier beThe repulsion degree function of robot to target point can be expressed asWherein, krep(i)Represent robot to i-th of barrier repulsion degree proportionality coefficient,
qrobot-qobstacleRepresent position qrobotTo the distance of i-th of barrier.By searching for the descent direction of synthesis repulsion degree, realize
The collision-free Trajectory Planning of Welding of flying robot.When flying robot is prominent meets dynamic barrier (such as the birds of flight), the layer can
By the frequency of the small barrier of sensor senses on robot platform and avoid the collision of these barriers.If flying robot
Too near from barrier, the safe floor can also reduce the speed of flying robot or make its complete stop motion.Reduce inspection
The speed of robot can act on speed control layer by the local avoidance obstacle instruction that airborne microcontroller is sent and be controlled
System, and airborne Hovering control device then can force it to stop at a certain position.
3) believed by the device measuring such as anemobiagraph, humidity sensor and meteorological satellite wind speed, humidity, Ground Meteorological etc.
Breath.If prominent meet the meteorological change (such as storm) for being unfavorable for flying robot's inspection, emergency landing is taken immediately, quickly makes to fly
The safe landing nearby of row robot, it is again etc. to be recycled after emergency situations terminate；
If 4) change without special weather, flying robot's inspection judges whether patrol task is completed afterwards for a period of time, if complete
Cheng Ze makes a return voyage by former safe flight route, if not completing, is carried out always according to the best safety flight path in step 2)
Autonomous hedging.
The foregoing is only a preferred embodiment of the present invention, but protection scope of the present invention be not limited thereto,
Any one skilled in the art the invention discloses technical scope in, the change or replacement that can readily occur in,
It should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims
It is defined.
Claims (1)
1. a kind of electric inspection process flying robot's flight course planning method based on Three-dimensional Numeric Map, methods described include：
Step 1：The three-dimensional data information of power line Terrain atural object is gathered by laser scanner technique, and builds corresponding three
Dimension word map；
Step 2：According to Three-dimensional Numeric Map dyspoiesis thing map, and utilize the high frequency probability 3D mapping frameworks based on Octree
Global context map is generated, then establishes the certain semantic model by mission requirements automatically by airborne comprehensive sensor, so as to
The paths planning method being combined using local multiresolution avoidance with layering structure, the safety for establishing inspection flying robot are flown
Walking along the street footpath；
Step 3：If prominent meet the weather condition for being unfavorable for flying robot's inspection, emergency landing is taken immediately, makes flying machine
Device people safe landing nearby, it is again etc. to be recycled after emergency situations terminate；
Step 4：If without special weather situation, judge whether patrol task is completed after crusing robot inspection setting time；
If patrol task is completed, maked a return voyage by former safe flight route；If patrol task is not completed, according in step 2
Safe flight path carries out autonomous hedging；
It is characterized in that the local multiresolution avoidance in the step 2 includes such as with the paths planning method that layering structure is combined
Lower sub-step：
Sub-step 101：It is the task incipient stage to set top, and surrounding is accessible, provides a series of destinations, inspection flight in advance
Robot flies in flight course according to these destinations；Because global context map will not change in these destinations, therefore
Global motion planning is used when beginning a task with；
In global context map caused by the high frequency probability 3D mapping frameworks based on Octree, each node on behalf of Octree
One cubic volume for being referred to as voxel；Wherein, leaf node n is z in sensor measurement distance1:tWhen probability be expressed asThe formula is by current distance zt, Prior Probability P (n) and pass
Sensor measurement distance is z1:t-1When probability P (n | z1:t-1) determine；This method can not only take known spatial, and can also
Enough any unknown spaces of occupancy；
Finally, Global motion planning enters next layer, local multiresolution path planning layer as input layer；
It is described it is top be mission planning layer and global path planning layer, obtained respectively by certain semantic model and global context map
Go out；
Sub-step 102：In local multiresolution path planning layer, using the path planning based on grid, use multiple with inspection
The three-dimensional grid that size centered on flying robot is M × M × M, by recursive method, these grids are embedded in each other,
The uniform grid that size is N × N × N is obtained, (log is included in each M × M × M grid2(N/M)+1)M3Individual unit；
The local multiresolution path planning layer is obtained by airborne sensor, global context map and partial barriers map；
Sub-step 103：For the planning in grid, it is necessary to an embedded non-directed graph, all grids of grid inside connection surrounding；
In the grid centered on inspection flying robot, path planning is carried out using a kind of graph search algorithm, in searching algorithm, with
Start node of the node of eight most inside cell grids as search is connected, the cost for traveling through border is by its institute bonding pad
What the boundary length two parts drawn between the obstacle cost in domain and central area by Euclidean distance were tried to achieve, i.e. f (n)=g (n)
+d(n)；The barrier cost g (n) is the length that edge score is multiplied by each unit, and arrives the Euclidean distance d of object element
(n) obtained by heuristic search, finally give most short maximally effective path in grid；
Sub-step 104：In lowermost layer, break the barriers map and motion model establishes one based on adaptive obstacle region of rejection
Local avoidance layer is as safe floor；All barriers are considered as the place for having highest repulsion degree, and robot always repels from height
The place of degree moves to the place of low repulsion degree；
Sub-step 105：The design in the safe flight path will consider that the safe distance of livewire work, flying robot are flying
When avoid the interference of electromagnetic field while ensure the sampled point nearest from fixed obstacle enough far to allow flying robot one
Minimum adaptive radius of turn ρmin, wherein,Wherein, V is the constant traveling speed of flying robot
Degree, Φ max are the maximum deflection angles of flying robot, flying robot's convolution can avoiding obstacles, without any collision.
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| CN105843253A (en) * | 2016-04-08 | 2016-08-10 | 北京博瑞空间科技发展有限公司 | Design method and system for unmanned aerial vehicle's travel path |
| CN105892489B (en) * | 2016-05-24 | 2019-09-10 | 国网山东省电力公司电力科学研究院 | A kind of automatic obstacle avoiding UAV system and control method based on Multi-sensor Fusion |
| CN106647804B (en) * | 2016-12-01 | 2019-09-20 | 深圳创动科技有限公司 | A kind of automatic detecting method and system |
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| CN107228673B (en) * | 2017-05-19 | 2020-02-18 | 北京旋极伏羲大数据技术有限公司 | Route planning method and device |
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| CN108318040A (en) * | 2018-02-06 | 2018-07-24 | 贵州电网有限责任公司 | A kind of more rotor trajectory planning system and method for transmission line-oriented inspection |
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| CN108733755B (en) * | 2018-04-11 | 2021-01-26 | 国网智能科技股份有限公司 | Intelligent inspection method and system based on three-dimensional information of power transmission line |
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| CN112748733B (en) * | 2020-12-16 | 2024-05-07 | 广东电网有限责任公司 | Cable barrow path planning method, device, equipment and storage medium |
| CN112747736B (en) * | 2020-12-22 | 2022-07-08 | 西北工业大学 | Indoor unmanned aerial vehicle path planning method based on vision |
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| CN113504791B (en) * | 2021-07-08 | 2022-06-14 | 中国南方电网有限责任公司超高压输电公司大理局 | Method and device for determining flight route of unmanned aerial vehicle |
| CN113988482B (en) * | 2021-12-23 | 2022-03-15 | 深圳市领志光机电自动化系统有限公司 | Three-dimensional space track path automatic matching generation system based on internet data chain |
| CN115048479B (en) * | 2022-08-16 | 2022-11-11 | 赛维航电科技有限公司 | Method, device, system and medium for processing aerograph of airborne mobile terminal |
| CN115097868B (en) * | 2022-08-24 | 2022-11-22 | 深圳市瓴鹰智能科技有限公司 | Flight control method and related device |
| CN116952249B (en) * | 2023-09-18 | 2023-12-08 | 中煤科工机器人科技有限公司 | Autonomous navigation method for wheel type inspection robot |
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| JP3522256B2 (en) * | 2002-02-13 | 2004-04-26 | 川崎重工業株式会社 | Surface condition detection method, surface condition detection system, and surface condition detection device |
| US7797095B2 (en) * | 2005-02-23 | 2010-09-14 | Aviation Safety Technologies, Llc | Method and device of calculating aircraft braking friction and other relating landing performance parameters based on the data received from aircraft's on board flight data management system |
| CN102622653B (en) * | 2012-02-27 | 2014-10-01 | 北京航空航天大学 | Multi-resolution path planning method for micro unmanned aerial vehicle under influence of wind field |
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