CN109520504A - A kind of unmanned plane inspection method for optimizing route based on grid discretization - Google Patents

A kind of unmanned plane inspection method for optimizing route based on grid discretization Download PDF

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CN109520504A
CN109520504A CN201811422746.6A CN201811422746A CN109520504A CN 109520504 A CN109520504 A CN 109520504A CN 201811422746 A CN201811422746 A CN 201811422746A CN 109520504 A CN109520504 A CN 109520504A
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CN109520504B (en
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李晓欢
曹先彬
刘锋
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Beihang University
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    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract

The unmanned plane that the invention discloses a kind of based on grid discretization is patrolled method for optimizing route, and unmanned plane inspection technical field is belonged to.The method first obtains the coordinate parameters in inspection region, and carries out visualization processing；Then it regard the fixed circle domain of unmanned plane scan area as grid, with grid dividing inspection region, and discretization net center of a lattice is demarcated as dispersive target point；Multiple no-manned plane scheduling modeling is carried out again, establishes optimization aim and constraint condition, seeks the optimization inspection route of unmanned plane target point after discrete；Finally, carrying out path planning to dispersive target point using improved A* algorithm, path planning optimum target node is found, the optimization flight path of unmanned plane is obtained after iteration optimization.The present invention can be improved inspection areal coverage, reduce unmanned plane sortie and position to target in inspection region.

Description

A kind of unmanned plane inspection method for optimizing route based on grid discretization
Technical field
The present invention relates to unmanned plane inspection technical field, specially a kind of unmanned plane inspection path based on grid discretization Optimization method.
Background technique
Unmanned air vehicle technique is recent military, civil aircraft a hot research field, in battlefield prospection, monitoring, mesh Demarcate the civilian uses such as military uses and environment detection, photography of taking photo by plane, resource exploration, disaster inspection, logistics transportation such as position all It has a wide range of applications.
Traditional inspection task is usually to rely on related personnel manually to be patrolled, and it is certain to require staff to have Vocational skills, this legacy mode operation amount is big, operating cost is high, inspection low efficiency.Unmanned plane is as a kind of dynamic, controllable System, the push-button aircraft that can be carried multiple-task equipment, execute multitask and can reuse possess easy to carry, behaviour Make it is simple, be swift in response, load is abundant, task purposes is wide, take off landing low to environmental requirement, autonomous flight the features such as, can be with Applied to multiple fields, inspection operation is completed.
But in practical flight operation, constraint of the unmanned plane by objective condition such as itself cruising ability, orographic factors is right In the excessive region of inspection range, the inspection operation to all monitoring regions often cannot achieve.Therefore, how according to it is many about Beam condition come optimize unmanned plane inspection path, reduce the sortie of unmanned plane, and improve unmanned plane inspection coverage rate become nobody The hot spot of machine practical application scene research.
Summary of the invention
The unmanned plane that it is an object of the present invention to provide a kind of based on grid discretization is patrolled method for optimizing route, and inspection area is improved Domain coverage ratio reduces unmanned plane sortie and positions to target in inspection region, and the optimization of unmanned plane is obtained after iteration optimization Flight path.
A kind of unmanned plane inspection method for optimizing route based on grid discretization, includes the following steps:
The present invention is premised on following assumed condition:
(1) drone flying height is h1~h2, flying speed v, every frame unmanned plane energy content of battery is E；
(2) distance is fixed value h between unmanned plane and level ground, and unmanned plane position three-dimensional coordinate is known at any time；
(3) unmanned plane range of effectively patrolling is that area planar and the unmanned plane elevation angle are greater than θ and are not obstructed by barrier, all Unmanned plane landing point is identical；
(4) assume the autonomous path flight after all unmanned planes press optimization, it is automatic after completion task without manual control Return to landing point；
(5) assume that ignoring unmanned plane takes off, lands and turn influence of the process to cruise duration.
Step S1, the coordinate parameters in inspection region are obtained from map software；
Step S2, visualization processing is carried out according to coordinate parameters；
Step S3, sliding-model control carried out to the gross area in inspection region, grid dividing is patrolled region, takes the grid element center to be Inspection regioinvertions are multiple dispersive target points by target point；
Step S4, multiple no-manned plane scheduling model is established, n frame unmanned plane will be sought and turned in the optimization inspection route in inspection region Turn to the optimization inspection route for seeking unmanned plane by dispersive target point；
Step S5, using improved A* algorithm, path planning is carried out to dispersive target point, finds path planning optimum target Node；
Step S6, according to optimum target node, the optimal flight paths of unmanned plane is obtained after iteration optimization, export optimal rule Draw path.
In step S2, the visualization processing is to read coordinate data by importing function, is constructed using stuffing function Three-dimensional space model demarcates inspection region, if the distance between unmanned plane and level ground are fixed, according to Terrain Elevation Variation, using unmanned aerial vehicle control system dynamic adjust unmanned plane flying height, make unmanned plane scan circle domain area keep It is constant, in order to carry out sliding-model control to inspection region, and target in the region that can be patrolled according to no-manned plane three-dimensional coordinate pair It is positioned.
In step S3, the sliding-model control is the circle domain discretization inspection region fixed with unmanned plane scan area And covered with circle domain, it is multiple roundness mess by inspection discrete region, and discretization grid element center was demarcated as with generation The dispersive target point of table, a wide range of target area is discrete for multiple dispersive target points, reduction calculation amount, in order to step S4 Ask unmanned plane by dispersive target point optimization inspection route.
Step S4, specifically progress multiple no-manned plane scheduling modeling, obtain the coverage rate of unmanned plane and the optimization solution of sortie.It seeks Ask optimization inspection route of the n frame unmanned plane in inspection region that can be converted into the optimization inspection road for seeking unmanned plane by dispersive target point Line finds the optimization solution of unmanned plane sortie by the inspection coverage rate under the different sorties of comparison again while guaranteeing coverage rate, Establish following target equation:
In formula (1), Cr indicates coverage rate,Indicate NkIt is pointed out from landing and is dealt into MiStart to patrol, until MjKnot Beam returns to landing point, and m indicates inspection dispersive target point total number, NkIndicate the kth frame unmanned plane in n sortie unmanned plane, i table Show that the i-th row, j indicate jth column, MiIndicate MiA target point, MjIndicate MjA target point；
0 < ∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
0 < A (t)+B (t)+C (t)≤E (3)
In formula (3), A (t) indicates the computing module energy consumption of every frame unmanned plane, and B (t) indicates the flight control of every frame unmanned plane Molding block energy consumption, C (t) indicate the communication module energy consumption of every frame unmanned plane, and t indicates the time；
The target point number that the constraint condition of formula (2) indicates that multi rack time unmanned plane is flown over is less than target point total number, The constraint condition of formula (3) indicates that every frame unmanned plane energy consumption will be in the constraint of energy content of battery E.
Step S5 specifically uses improved A*Algorithm carries out path optimization, A to dispersive target point*Algorithm is by estimating It calculates function to realize, unmanned plane, which often makes a move, requires to calculate the value of evaluation function, which is The position that unmanned plane needs to reach in next step, the expression formula of evaluation function are as follows:
F (n)=g (n)+h (n) (4)
In formula (4), g (n) refers to unmanned plane from the practical road that the starting point in path is passed through to path planning interior joint n Journey, the value of function g (n) are actual numerical value；H (n) is the estimation distance that the terminal from node n to path planning is spent, function h It (n) is initial evaluation function.Function f (n) is the algebraical sum of the value and function h (n) of function g (n), indicates unmanned plane on entire road The total distance passed through in diameter planning.
Two-dimentional Euclidean distance is extended to three-dimensional Euclidean distance in initial evaluation function h (n) by improved A* algorithm, and is drawn Enter power dissipation constraints cost parameter, formula is as follows:
H (n)=α di,j+β·E(t) (5)
In formula (5), α is indicated apart from the factor, di,jEuclidean distance between representation space two o'clock i and j, β indicate energy consumption because Son, E (t) indicate dump energy, di,jExpression formula are as follows:
In formula (6), xiThe X axis coordinate of representation space point i, xjThe X axis coordinate of representation space point j；yiRepresentation space point i Y axis coordinate, yjThe Y axis coordinate of representation space point j；ziThe Z axis coordinate of representation space point i, zjThe Z axis of representation space point j is sat Mark；
The specific implementation steps are as follows by step S6:
Step1: initialization generates OPEN list and CLOSED list, and OPEN list saves all generated and do not investigate Node, record the node accessed in CLOSED list.
Step2: load initial value is loaded in the information of starting point in the OPEN list of step1 generation, in generation Only include start node in OPEN list, and is denoted as f (n)=h (n).
Step3: searching OPEN list, if obtaining not having numerical value in OPEN list after searching, starting point planning in path is lost It loses, turns to and execute the start node that step2 continually looks for path planning；If the target of load path planning in OPEN list Node then executes step4.
Step4: searching the value of function f (n), calculates the numerical value in OPEN list using evaluation function, searches institute in list There is the smallest point of node f (n) value to be denoted as optimal node BESTNODE, this node will be loaded into CLOSED list, and handle This node carries out next step operation as present node.
Step5: judging target point, according to evaluation function judge optimal node whether be path target point.
If it is then algorithm terminates, and export the path node cooked up；If not target specified in path Point is just loaded into the neighbor node of present node in OPEN list, carries out step3.It circuits sequentially until finding defined target Node.
Step6: outgoing route saves optimal node, and saves it in CLOSED list, according in CLOSED list Node export planning path.
The path optimization the invention has the benefit that a kind of unmanned plane based on grid discretization proposed by the present invention is patrolled Method establishes multiple no-manned plane scheduling model by design optimization target and constraint condition, is improving the same of inspection areal coverage When reduce unmanned plane sortie, and target in inspection region can be positioned, patrol for a wide range of unmanned plane and provide one Kind optimization planning scheme.
Detailed description of the invention
Fig. 1 is a kind of process signal of unmanned plane inspection method for optimizing route based on grid discretization provided by the invention Figure；
Fig. 2 is a kind of target inspection of unmanned plane inspection method for optimizing route based on grid discretization provided by the invention Regional distribution chart；
Fig. 3 is a kind of area grid of unmanned plane inspection method for optimizing route based on grid discretization provided by the invention Discretization schematic diagram；
Fig. 4 is a kind of target positioning of unmanned plane inspection method for optimizing route based on grid discretization provided by the invention Figure.
Specific embodiment
The present invention is further elaborated with reference to the accompanying drawings and examples, but is not limitation of the invention.
Embodiment:
The present invention provides a kind of unmanned plane inspection method for optimizing route based on grid discretization, is with following assumed condition Premise:
(1) drone flying height is h1~h2, flying speed v, every frame unmanned plane energy content of battery is E；
(2) distance is fixed value h between unmanned plane and level ground, and unmanned plane position three-dimensional coordinate is known at any time；
(3) unmanned plane range of effectively patrolling is that area planar and the unmanned plane elevation angle are greater than θ and are not obstructed by barrier, all Unmanned plane landing point is identical；
(4) assume that the autonomous path flight after all unmanned planes press optimization is moved after completion task and returned without manual control Return landing point；
(5) assume that ignoring unmanned plane takes off, lands and turn influence of the process to cruise duration.
The method for optimizing route as shown in Figure 1, a kind of unmanned plane based on grid discretization is patrolled, includes the following steps:
Step S1, the coordinate parameters in inspection region are obtained from map software；
Step S2, visualization processing is carried out according to coordinate parameters；
Step S3, sliding-model control carried out to the gross area in inspection region, grid dividing is patrolled region, takes the grid element center to be Inspection regioinvertions are multiple dispersive target points by target point；
Step S4, multiple no-manned plane scheduling model is established, n frame unmanned plane will be sought and turned in the optimization inspection route in inspection region Turn to the optimization inspection route for seeking unmanned plane target point after discrete；
Step S5, using improved A* algorithm, path planning is carried out to dispersive target point, finds path planning optimum target Node；
Step S6, according to optimum target node, the optimal flight paths of unmanned plane is obtained after iteration optimization, export optimal rule Draw path.
As shown in Fig. 2, carrying out visualization processing according to coordinate parameters etc. using MATLAB in step S2.Pass through xlsread Function reads coordinate data, and constructs three-dimensional space model using mesh function, reuses rectangle function spotting area Domain；In view of terrain elevation data difference, the height above sea level distance between unmanned plane and inspection region can constantly change, and lead to scanning surface Product constantly changes and its factors such as sight is obscured by an obstacle, and the distance between unmanned plane and level ground is fixed, base area The variation dynamic of shape height adjusts the flying height of unmanned plane, it is ensured that and unmanned plane scans circle domain area and remains unchanged, so as to In to inspection region progress sliding-model control, and in the region that can be patrolled according to no-manned plane three-dimensional coordinate pair, target be positioned.
As shown in figure 3, carrying out sliding-model control to inspection region in step S3.It is with unmanned plane scanning constant circle domain pair Inspection region is covered, and is multiple grids by inspection discrete region, and discretization grid element center is demarcated as representative Dispersive target point, by a wide range of target area it is discrete be multiple dispersive target points, reduce calculation amount；Although having blank area Domain, which seems, not to be arrived by inspection, but considers its actual conditions, and unmanned plane flies to next target point Shi Huicheng from a target point Reveal one " band-like " inspection path, these white spaces are also bound to be scanned.Secondly because inspection zone boundary is deposited Uneven, cause some regions in boundary that may not be scanned, but its area comes compared to total inspection region Say very little, thus these scannings less than region it is negligible.
In step S4, carry out multiple no-manned plane scheduling modeling, seek n frame unmanned plane inspection region optimization inspection route can It is converted into the optimization inspection route for seeking unmanned plane target point after discrete, by comparing the inspection coverage rate under different sorties, Find the optimization solution of unmanned plane sortie again while guaranteeing coverage rate:
Establish target equation:
In formula (1), Cr indicates coverage rate,Indicate NkIt is pointed out from landing and is dealt into MiStart to patrol, until MjKnot Beam returns to landing point, and m indicates inspection dispersive target point total number, NkIndicate the kth frame unmanned plane in n sortie unmanned plane, i table Show that the i-th row, j indicate jth column, MiIndicate MiA target point, MjIndicate MjA target point；
0 < ∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
0 < A (t)+B (t)+C (t)≤E (3)
In formula (3), A (t) indicates the computing module energy consumption of every frame unmanned plane, and B (t) indicates the flight control of every frame unmanned plane Molding block energy consumption, C (t) indicate the communication module energy consumption of every frame unmanned plane, and t indicates the time；
The target point number that the constraint condition of formula (2) indicates that multi rack time unmanned plane is flown over is less than target point total number, The constraint condition of formula (3) indicates that every frame unmanned plane energy consumption will be in the constraint of gross energy E.
By introduce energy constraint condition, the accuracy in unmanned plane cruise duration can be improved, effectively avoid midway nobody The case where function amount surprisingly exhausts promotes the success rate of inspection task.
Will inspection discrete region be target point after, in step S5, using improved A*The dispersive target point path of algorithm Optimization, A*Algorithm is realized by evaluation function, and unmanned plane, which often makes a move, requires to calculate the value of evaluation function, the estimation The smallest node of functional value is exactly the position that unmanned plane needs to reach in next step, and the general expression of evaluation function is as follows:
F (n)=g (n)+h (n) (4)
In formula (4), g (n) refers to unmanned plane from the practical road that the starting point in path is passed through to path planning interior joint n Journey, the value of function g (n) are actual numerical value；H (n) is the estimation distance that the terminal from node n to path planning is spent, function h It (n) is initial evaluation function.Function f (n) is the algebraical sum of the value and function h (n) of function g (n), indicates unmanned plane on entire road The total distance passed through in diameter planning.
Two-dimentional Euclidean distance is extended to three-dimensional Euclidean distance in initial evaluation function h (n) by improved A* algorithm, and is drawn Enter power dissipation constraints cost parameter, formula is as follows:
H (n)=α di,j+β·E(t) (5)
In formula (5), α is indicated apart from the factor, di,jEuclidean distance between representation space two o'clock i and j, β indicate energy consumption because Son, E (t) indicate dump energy, di,jExpression formula are as follows:
In formula (6), xiThe X axis coordinate of representation space point i, xjThe X axis coordinate of representation space point j；yiRepresentation space point i Y axis coordinate, yjThe Y axis coordinate of representation space point j；ziThe Z axis coordinate of representation space point i, zjThe Z axis of representation space point j is sat Mark；
In step S6, the specific implementation steps are as follows:
Step1: initialization generates OPEN and CLOSED list, and OPEN table saves all generated and the node do not investigated, The node accessed is recorded in CLOSED table.
Step2: load initial value is loaded in the information of starting point in the OPEN list of step1 generation, in generation Only include start node in OPEN list, and is denoted as f (n)=h (n).
Step3: searching OPEN list, if obtaining not having numerical value in OPEN list after searching, starting point planning in path is lost It loses, turns to and execute the start node that step2 continually looks for path planning；If the target of load path planning in OPEN list Node then executes step4.
Step4: searching the value of function f (n), calculates the numerical value in OPEN list using evaluation function, searches institute in list There is the smallest point of node f (n) value to be denoted as optimal node BESTNODE, this node will be loaded into CLOSED list, and handle This node carries out next step operation as present node.
Step5: judging target point, according to evaluation function judge optimal node whether be path target point.If so, that Algorithm terminates, and exports the path node cooked up；If not target point specified in path, just facing present node Nearly node is loaded into OPEN list, carries out step3.It circuits sequentially until finding defined destination node.
Step6: outgoing route saves optimal node, and saves it in CLOSED list, according in CLOSED list Node export planning path.
In conjunction with Fig. 4, since the distance between drone flying height and level ground are fixed, unmanned plane position three-dimensional coordinate At any time it is found that it is high to subtract flight by the height value of no-manned plane three-dimensional coordinate when target is located at unmanned plane and scans the circle domain center of circle Angle value, the position coordinates for target that you can get it, is accurately positioned target.

Claims (6)

  1. The method for optimizing route 1. a kind of unmanned plane based on grid discretization is patrolled, it is characterised in that:
    Premised on following assumed condition:
    (1) drone flying height is h1~h2, flying speed v, every frame unmanned plane energy content of battery is E；
    (2) distance is fixed value h between unmanned plane and level ground, and unmanned plane position three-dimensional coordinate is known at any time；
    (3) unmanned plane range of effectively patrolling is area planar and the unmanned plane elevation angle greater than θ and is not obstructed by barrier, it is all nobody Machine landing point is identical；
    (4) assume the autonomous path flight after all unmanned planes press optimization, without manual control, auto-returned after completion task Landing point；
    (5) assume that ignoring unmanned plane takes off, lands and turn influence of the process to cruise duration；
    Described method includes following steps:
    Step S1, the coordinate parameters in inspection region are obtained from map software；
    Step S2, visualization processing is carried out according to coordinate parameters；
    Step S3, sliding-model control is carried out to the gross area in inspection region, grid dividing inspection region, taking grid element center is target Inspection regioinvertions are multiple dispersive target points by point；
    Step S4, multiple no-manned plane scheduling model is established, n frame unmanned plane will be sought and be converted into the optimization inspection route in inspection region Ask unmanned plane by the optimization inspection route of dispersive target point；
    Step S5, using improved A* algorithm, path planning is carried out to dispersive target point, finds path planning optimum target section Point；
    Step S6, according to optimum target node, the optimal flight paths of unmanned plane are obtained after iteration optimization, export optimum programming road Diameter.
  2. The method for optimizing route 2. a kind of unmanned plane based on grid discretization according to claim 1 is patrolled, feature exist In: in step S2, the visualization processing is to read coordinate data by importing function, is constructed using stuffing function three-dimensional Spatial model demarcates inspection region, if the distance between unmanned plane and level ground are fixed, according to the change of Terrain Elevation To change, the flying height of unmanned plane is adjusted using unmanned aerial vehicle control system dynamic, the circle domain area for scanning unmanned plane remains unchanged, In order to carry out sliding-model control to inspection region, and positioned according to target in no-manned plane three-dimensional coordinate pair inspection region.
  3. The method for optimizing route 3. a kind of unmanned plane based on grid discretization according to claim 1 is patrolled, feature exist In: in step S3, the sliding-model control is the circle domain discretization inspection region fixed with unmanned plane scan area and with justifying Domain is covered, and is multiple roundness mess by inspection discrete region, and discretization grid element center is demarcated as representative Dispersive target point.
  4. The method for optimizing route 4. a kind of unmanned plane based on grid discretization according to claim 1 is patrolled, feature exist In:
    Step S4 specifically establishes following target equation:
    In formula (1), Cr indicates coverage rate,Indicate NkIt is pointed out from landing and is dealt into MiStart to patrol, until MjEnd is returned Landing point is returned to, m indicates inspection dispersive target point total number, NkIndicate that the kth frame unmanned plane in n sortie unmanned plane, i indicate the I row, j indicate jth column, MiIndicate MiA target point, MjIndicate MjA target point；
    0 < ∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
    0 < A (t)+B (t)+C (t)≤E (3)
    In formula (3), A (t) indicates the computing module energy consumption of every frame unmanned plane, and B (t) indicates that the flight of every frame unmanned plane controls mould Block energy consumption, C (t) indicate the communication module energy consumption of every frame unmanned plane, and t indicates the time.
  5. The method for optimizing route 5. a kind of unmanned plane based on grid discretization according to claim 1 is patrolled, feature exist In:
    Step S5, the improved A*Two-dimentional Euclidean distance is extended to three-dimensional Euclidean in initial evaluation function h (n) by algorithm Distance, and power dissipation constraints cost parameter is introduced, formula is as follows:
    H (n)=α di,j+β·E(t) (5)
    In formula (5), h (n) is the estimation distance that the terminal from node n to path planning is spent, and function h (n) is initially to estimate Calculate function；α is indicated apart from the factor, di,jEuclidean distance between representation space two o'clock i and j, β indicate that Energy consumption factor, E (t) indicate Dump energy, di,jExpression formula are as follows:
    In formula (6), xiThe X axis coordinate of representation space point i, xjThe X axis coordinate of representation space point j；yiThe Y-axis of representation space point i Coordinate, yjThe Y axis coordinate of representation space point j；ziThe Z axis coordinate of representation space point i, zjThe Z axis coordinate of representation space point j.
  6. The method for optimizing route 6. a kind of unmanned plane based on grid discretization according to claim 1 is patrolled, feature exist In: the specific implementation steps are as follows by step S6:
    Step1: initialization generates OPEN list and CLOSED list, and OPEN list saves all generated and the section do not investigated Point records the node accessed in CLOSED list；
    Step2: load initial value is loaded in the information of starting point in the OPEN list of step1 generation, arranges in the OPEN of generation Only include start node in table, and is denoted as f (n)=h (n)；
    Step3: searching OPEN list, if obtaining not having numerical value in OPEN list after searching, path starting point planning failure turns The start node of path planning is continually looked for execution step2；If the destination node of load path planning in OPEN list, Then execute step4；
    Step4: searching the value of function f (n), calculates the numerical value in OPEN list using evaluation function, searches all sections in list The smallest point of point f (n) value is denoted as optimal node BESTNODE, this node will be loaded into CLOSED list, and this Node carries out next step operation as present node；
    Step5: judging target point, according to evaluation function judge optimal node whether be path target point；
    If it is then algorithm terminates, and export the path node cooked up；If not target point specified in path, just The neighbor node of present node is loaded into OPEN list, step3 is carried out；It circuits sequentially until finding defined destination node；
    Step6: outgoing route saves optimal node, and saves it in CLOSED list, according to the section in CLOSED list Point output planning path.
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CN113203417A (en) * 2021-04-06 2021-08-03 广东安恒电力科技有限公司 Unmanned aerial vehicle inspection path planning method based on brain storm optimization algorithm
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