CN110006428A - A kind of overlay path method and device for planning based on unmanned plane energy - Google Patents
A kind of overlay path method and device for planning based on unmanned plane energy Download PDFInfo
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Abstract
The present invention provides a kind of overlay path method and device for planning based on unmanned plane energy, method therein is the following steps are included: the first step, it establishes landform digitlization and shows model, utilize the DEM information of landform, in conjunction with rectangular Bézier surface, landform digital surface model DSM is established, so that it is determined that normal vector and unmanned plane that landform shows perceive sampling point position and establish Concourse Division path；Second step establishes unmanned plane during flying power module, calculates energy consumption of the unmanned plane in space path section；Third step finds the overlay path of energetic optimum using GA algorithm traverse path space.The present invention may be implemented to improve observation resolution ratio consistency, guarantee unmanned plane minimum power consumption in segmented paths；Using genetic algorithm, traversal search flight path order makes path under conditions of meeting observation coverage and observation resolution ratio, the technical effect of the feature with energy optimization.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a method and a device for planning a coverage path based on unmanned aerial vehicle energy.
Background
With the development of intelligent control and Unmanned technology, Unmanned Aerial Vehicles (UAVs) play an increasingly important role in civil and military fields, such as ground observation, crop monitoring, disaster relief, target detection, and the like. The coverage path planning is the basis for the unmanned aerial vehicle to execute tasks quickly and efficiently. The purpose of the coverage path planning is to provide a flight trajectory which covers all observation areas and meets the optimal indexes of flight time, path length or energy consumption.
In the prior art, coverage path planning is generally divided into the following several major categories according to different application scenarios: the method comprises the steps of planning a coverage path based on coverage information maximization, planning a coverage path based on shortest coverage time, planning a coverage path based on largest coverage area, planning a coverage path based on lowest coverage repetition rate, planning a coverage path based on shortest path length and planning a coverage path based on optimal energy.
Typical coverage path planning algorithms are: ladder decomposition method, Morse decomposition method, TSP (TravengSalesman Problem), MTSP (multiple traversing Salesman Problem), SOM (self organizing mapping), and the like.
The inventor of the present application finds that the method of the prior art has at least the following technical problems in the process of implementing the present invention:
a terrain decomposition algorithm represented by a trapezoidal decomposition method and a moss function decomposition method adopts the idea of breaking up the whole into parts, decomposes a complex terrain with an observation space into simple and non-overlapping units, and enables an unmanned aerial vehicle to realize complete coverage of irregular terrain based on the terrain including polygonal obstacles in a simple movement mode (a cattle-farming mode, a grass-mowing mode, and the like). However, the problem of observation resolution difference caused by terrain is not solved by the method, deep research on energy consumption of the unmanned aerial vehicle is lacked, and energy cannot be effectively utilized.
Therefore, the method in the prior art has the technical problem that the energy utilization rate of the unmanned aerial vehicle is low.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for planning a coverage path based on energy of an unmanned aerial vehicle, so as to solve or at least partially solve the technical problem of low energy utilization of the unmanned aerial vehicle in the prior art.
The invention provides a method for planning a coverage path based on unmanned aerial vehicle energy, which comprises the following steps:
step S1: initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information;
step S2: determining an observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, connecting every two sampling points to establish a segmented path, establishing a power speed model of the unmanned aerial vehicle according to a kinetic energy theorem, calculating the energy consumption of the unmanned aerial vehicle on the segmented path based on the power speed model and a digital surface model, and establishing an energy consumption graph of the whole area to be observed according to the energy consumption on the segmented path;
step S3: and traversing the energy consumption graph by adopting a genetic algorithm to obtain a coverage path with the minimum total energy consumption, wherein the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
In one embodiment, step S1 specifically includes:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of a sampling area, and recording and storing the position and elevation information of all grid primitives;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
In one embodiment, step S2 specifically includes:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; carrying out simplification analysis on a formula of a kinetic energy theorem to obtain a power speed model of the unmanned aerial vehicle:
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
In one embodiment, step S3 specifically includes:
step S3.1: initializing population parameters, wherein the population parameters specifically comprise population number Cp, chromosome gene number Cg, iteration number C and cross probability PcProbability of variation Pm；
Step S3.2: calculating fitness F between chromosomesi＝1/∑EpqAnd selecting the optimized individuals according to a preset probability, selecting the probability Pi＝k·Fi；
Step S3.3: in the crossing process, a partial matching crossing algorithm is adopted, two individuals are randomly selected, a plurality of gene segments are exchanged at corresponding positions to prevent local convergence, and then two genes of the individuals are randomly selected to be exchanged to realize mutation operation;
step S3.4: and (3) repeatedly executing the steps S3.1-S3.3, and outputting the individual with the maximum fitness as an optimal solution when the preset iteration times are reached, wherein the optimal solution corresponds to the coverage path with the minimum total energy consumption.
In one embodiment, the predetermined probability is set according to the convergence effect.
Based on the same inventive concept, the second aspect of the present invention provides an unmanned aerial vehicle energy-based coverage path planning apparatus, including:
the digital surface model establishing module is used for initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information;
the power speed model building module is used for determining the observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, each two sampling points are connected to build a subsection path, a power speed model of the unmanned aerial vehicle is built according to a kinetic energy theorem, the energy consumption of the unmanned aerial vehicle on the subsection path is calculated based on the power speed model and the digital surface model, and an energy consumption graph of the whole area to be observed is built according to the energy consumption on the subsection path;
and the energy consumption graph traversal module is used for traversing the energy consumption graph by adopting a genetic algorithm to obtain a coverage path with the minimum total energy consumption, wherein the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
In one embodiment, the digital surface modeling module is specifically configured to perform the following steps:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of the sampling region, recording and storing the positions of all grid primitives andelevation information;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
In one embodiment, the power speed model building module is specifically configured to perform the following steps:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; carrying out simplification analysis on a formula of a kinetic energy theorem to obtain a power speed model of the unmanned aerial vehicle:
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
Based on the same inventive concept, a third aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed, performs the method of the first aspect.
Based on the same inventive concept, a fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the invention provides a method for planning a coverage path based on energy of an unmanned aerial vehicle, which comprises the steps of firstly establishing a terrain digitized Surface Model, namely establishing a terrain digitized Surface Model DSM (Digital Elevation Model, DSM) by utilizing DEM (Digital Elevation Model) information of a terrain and combining tensor product Bezier curved surfaces, thereby determining positions of normal vectors indicated by the terrain and unmanned aerial vehicle sensing sampling points and establishing an interval subsection path; then establishing an unmanned aerial vehicle flight power model, and calculating the energy consumption of the unmanned aerial vehicle on the space subsection path section; then, a GA (Genetic Algorithm, GA) Algorithm is utilized to traverse a path space, and a coverage path with optimal energy is searched; by utilizing a genetic algorithm, the sequence of the flight paths is traversed and searched, so that the paths have the characteristic of energy optimization under the condition of meeting the observation coverage and the observation resolution, the energy utilization rate of the unmanned aerial vehicle is improved, and the technical problem of low energy utilization rate of the unmanned aerial vehicle in the method in the prior art is solved.
Furthermore, the invention provides an energy optimal complete coverage path planning method for improving the consistency of the observation resolution, and the tensor product Bezier curved surface is adopted to ensure the coverage of the area, so that the consistency of the observation resolution is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for planning a coverage path based on energy of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is a flow chart of an implementation of the method of FIG. 1;
FIG. 3 is a schematic diagram of a terrain DSM model according to an embodiment of the invention;
FIG. 4 is a schematic diagram of calculating the fly height of an unmanned aerial vehicle according to an embodiment of the invention;
FIG. 5 is a flow chart of a DSM modeling method according to an embodiment of the invention;
FIG. 6 is a schematic diagram of path energy consumption according to an embodiment of the present invention;
FIG. 7 is a flow chart of a genetic algorithm according to an embodiment of the present invention;
fig. 8 is a schematic diagram of path planning in a specific example according to an embodiment of the present invention;
fig. 9 is a block diagram of a coverage path planning apparatus based on unmanned aerial vehicle energy according to an embodiment of the present invention;
FIG. 10 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention;
fig. 11 is a block diagram of a computer device in an embodiment of the present invention.
Detailed Description
The invention aims to provide a method and a device for planning a coverage path based on unmanned aerial vehicle energy, which are used for solving the technical problem that the unmanned aerial vehicle energy utilization rate is low in the method in the prior art.
The main inventive concept and technical effect of the invention are as follows:
the coverage path planning method for improving the energy utilization rate of the unmanned aerial vehicle and increasing the consistency of the observation resolution is provided, the consistency of the observation resolution can be increased by establishing a digital surface model (DSM model) based on DEM information by analyzing the influence of topographic features and sensor characteristics on the observation effect; the energy utilization rate of the unmanned aerial vehicle in the flight process is improved by establishing an energy consumption model (power speed model) in the stable flight process of the rotor unmanned aerial vehicle and an energy priority path solving algorithm (genetic algorithm).
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment provides a method for planning a coverage path based on energy of an unmanned aerial vehicle, please refer to fig. 1, and the method includes:
step S1 is first executed: initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information.
Specifically, the existing tool can be used to label the boundary of the region to be observed, and in the process of observing the ground by using the camera, the effective resolution is determined by two aspects: the first is the absolute height of the sensor from the ground, and the second is the included angle between the main optical axis of the camera and the normal vector of the area to be sensed. The effective resolution of the camera can be calculated using existing formulas. In order to ensure that the spatial resolution of the perception data of the whole observation area is consistent, the unmanned aerial vehicle needs to keep a fixed absolute height with the indication of the terrain to be measured. In the process of establishing the digitized surface model of the sampling region, the existing method can be adopted, and preferably, a tensor product Bezier surface method is adopted.
In one embodiment, step S1 may be implemented by:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of a sampling area, and recording and storing the position and elevation information of all grid primitives;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
Specifically, please refer to fig. 5, which is a flowchart of a DSM modeling method according to an embodiment of the present invention, in step S1.1, a schematic diagram of calculating the flying height of an unmanned aerial vehicle is shown in fig. 4, and the height H isi,i+1The calculation formula of (a) is as follows:
can be calculated by using the correlation theorem of triangles in which H isi,i+1Representing the flight altitude of the drone, S represents the distance between the spatial sampling points i and i +1, hi+1Is ABiLength of (h)iIs CBi+1Length of (h)i+1And hiα being intermediate variables, introduced for ease of calculationi+ΔiIs Bi+1BiAnd BiE, the angle between them.
The elevation information, namely DEM information in the step S1.2 is used for realizing the digital expression of the topography of the terrain surface through limited terrain elevation data, and the elevation information is a group of ordered numerical value array forms to express the ground elevation. The preset software may be existing software, such as Global Mapper. The digitized surface model (DSM model) constructed in step S1.3 is shown in fig. 3.
Then, step S2 is executed: determining an observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, connecting every two sampling points to establish a subsection path, establishing a power speed model of the unmanned aerial vehicle according to a kinetic energy theorem, calculating the energy consumption of the unmanned aerial vehicle on the subsection path based on the power speed model and a digital surface model, and establishing an energy consumption graph of the whole area to be observed according to the energy consumption on the subsection path.
Specifically, the observation sampling position may include a plurality of different sampling points, and a spatial flight path, i.e., a segmented path, may be established by connecting any two sampling points. The flight height of the unmanned aerial vehicle determines the projection area of the camera on the ground, the area to be observed is divided into grids with the size of the projection area, and the upper part of each grid is a sampling point.
In one embodiment, step S2 may be implemented by:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; carrying out simplification analysis on a formula of a kinetic energy theorem to obtain a power speed model of the unmanned aerial vehicle:
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
Specifically, the power speed model in step S2.1 is the flight speed of the unmanned aerial vehicle in the steady state, and the energy consumption of the unmanned aerial vehicle on a certain path in the space can be quantitatively estimated through the power speed model.
In a specific implementation, the energy calculation formula is as follows:
wherein,
Φ(Bi+1) And Φ (B)i) Elevation information representing spatial sampling points i +1 and i, f representing the lift of the drone, G representing the total weight of the drone, Vz representing the flight speed of the drone when it is in steady state, ψ representing angle αi+Δi，αiRepresenting pitch angle, i.e. gradient information in step S2.3, Si,i+1Representing the distance of two point paths in space. FIG. 6 is a schematic diagram of path energy consumption according to an embodiment of the present invention;
step S3 is executed again: and traversing the energy consumption graph by adopting a genetic algorithm to obtain a coverage path with the minimum total energy consumption, wherein the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
In one embodiment, step S3 may be implemented by:
step S3.1: initializing population parameters, wherein the population parameters specifically comprise population number Cp, chromosome gene number Cg, iteration number C and cross probability PcProbability of variation Pm；
Step S3.2: calculating fitness between chromosomesResponse degree Fi＝1/∑EpqAnd selecting the optimized individuals according to a preset probability, selecting the probability Pi＝k·Fi；
Step S3.3: in the crossing process, a partial matching crossing algorithm is adopted, two individuals are randomly selected, a plurality of gene segments are exchanged at corresponding positions to prevent local convergence, and then two genes of the individuals are randomly selected to be exchanged to realize mutation operation;
step S3.4: and (3) repeatedly executing the steps S3.1-S3.3, and outputting the individual with the maximum fitness as an optimal solution when the preset iteration times are reached, wherein the optimal solution corresponds to the coverage path with the minimum total energy consumption.
Specifically, please refer to fig. 7, which is a flowchart for solving the optimal path by using the genetic algorithm in the present invention, where the preset iteration number is the total iteration number in fig. 7, and the solution with the highest fitness is the optimal solution. The preset probability in step S3.2 is set according to the convergence effect. Through genetic algorithm GA, the efficiency of covering path planning can be effectively improved, especially for a scene of a large-area covered and observed by multiple unmanned aerial vehicles, the calculation efficiency of the algorithm and the effectiveness of the solution can be well met.
Referring to fig. 2, a general implementation flowchart of the path planning method of the present invention is shown, and the method for planning an optimal energy coverage path of an unmanned aerial vehicle disclosed in the present invention includes the following steps: the method comprises the following steps of firstly, establishing a terrain digital surface model, specifically comprising: establishing a terrain analytic Model, namely a terrain Digital Surface Model (DSM) by utilizing DEM (Digital Elevation Model) information of terrain and combining tensor product Bezier curved surfaces, thereby determining positions of normal vectors indicated by the terrain and unmanned aerial vehicle perception sampling points and establishing an interval segmented path; secondly, establishing a flight power model of the unmanned aerial vehicle, and calculating the energy consumption of the unmanned aerial vehicle on a spatial path section; and thirdly, traversing the path space by using a GA (Genetic Algorithm, GA) Algorithm to find an energy-optimal coverage path.
In order to more clearly illustrate the path planning method of the present invention, a specific example is described below, please refer to fig. 8, where the load of the unmanned aerial vehicle of the present embodiment is 1.3kg, and the flying speed is 3 m/s. The size of an observation area to be covered is as follows:
112 degrees 02 '56.3' W-112 degrees 01 '22' W, 46 degrees 44 '12.6' N-46 degrees 43 '23.86' N, area 3.006km2. In the test process, the projection of the sensor effectively covers the whole observation area, and the unmanned aerial vehicle covers the path to meet the requirement of minimum energy consumption; meanwhile, the designed flight path of the unmanned aerial vehicle effectively increases the consistency of the observation resolution of the sensor. The traditional Morse function method, the trapezoidal decomposition method and the mowing type coverage path planning algorithm can not meet the characteristics at the same time.
Generally speaking, compared with the prior art, the method for planning the energy optimal complete coverage path for improving the consistency of the observation resolution is provided, and tensor product Bezier curved surfaces are adopted to ensure the coverage of the area and improve the consistency of the observation resolution; the unmanned aerial vehicle is ensured to have the least energy consumption on the segmented path through the unmanned aerial vehicle power estimation algorithm; and traversing and searching the sequence of the flight path by using a genetic algorithm, so that the path has the characteristic of energy optimization under the condition of meeting the observation coverage and the observation resolution.
Based on the same inventive concept, the application also provides a device of the unmanned aerial vehicle energy-based coverage path planning method in the first embodiment, which is detailed in the second embodiment.
Example two
The embodiment provides a coverage path planning device based on unmanned aerial vehicle energy, please refer to fig. 9, the device includes:
the digital surface model establishing module 201 is used for initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information;
the power speed model building module 202 is used for determining an observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, each two sampling points are connected to build a subsection path, a power speed model of the unmanned aerial vehicle is built according to a kinetic energy theorem, the energy consumption of the unmanned aerial vehicle on the subsection path is calculated based on the power speed model and the digital surface model, and an energy consumption graph of the whole area to be observed is built according to the energy consumption on the subsection path;
and the energy consumption map traversing module 203 is configured to traverse the energy consumption map by using a genetic algorithm to obtain a coverage path with the minimum total energy consumption, where the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
In one embodiment, the digitized surface model building module 201 is specifically configured to perform the following steps:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of a sampling area, and recording and storing the position and elevation information of all grid primitives;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
In one embodiment, the power speed model building module 202 is specifically configured to perform the following steps:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; carrying out simplification analysis on a formula of a kinetic energy theorem to obtain a power speed model of the unmanned aerial vehicle:
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
In one embodiment, the energy consumption map traversing module 203 is specifically configured to perform the following steps:
step S3.1: initializing population parameters, wherein the population parameters specifically comprise population number Cp, chromosome gene number Cg, iteration number C and cross probability PcProbability of variation Pm；
Step S3.2: calculating fitness F between chromosomesi＝1/∑EpqAnd selecting the optimized individuals according to a preset probability, selecting the probability Pi＝k·Fi；
Step S3.3: in the crossing process, a partial matching crossing algorithm is adopted, two individuals are randomly selected, a plurality of gene segments are exchanged at corresponding positions to prevent local convergence, and then two genes of the individuals are randomly selected to be exchanged to realize mutation operation;
step S3.4: and (3) repeatedly executing the steps S3.1-S3.3, and outputting the individual with the maximum fitness as an optimal solution when the preset iteration times are reached, wherein the optimal solution corresponds to the coverage path with the minimum total energy consumption.
Since the device described in the second embodiment of the present invention is a device used for implementing the method for planning a coverage path based on energy of an unmanned aerial vehicle in the first embodiment of the present invention, a person skilled in the art can understand the specific structure and deformation of the device based on the method described in the first embodiment of the present invention, and thus, details thereof are not described herein. All the devices adopted in the method of the first embodiment of the present invention belong to the protection scope of the present invention.
EXAMPLE III
Referring to fig. 3, based on the same inventive concept, the present application further provides a computer-readable storage medium 300, on which a computer program 311 is stored, which when executed implements the method according to the first embodiment.
Since the computer-readable storage medium described in the third embodiment of the present invention is a computer device used for implementing the unmanned aerial vehicle energy-based coverage path planning method in the first embodiment of the present invention, based on the method described in the first embodiment of the present invention, those skilled in the art can understand the specific structure and deformation of the computer-readable storage medium, and thus, details are not described here. Any computer readable storage medium used in the method of the first embodiment of the present invention is within the scope of the present invention.
Example four
Based on the same inventive concept, the present application further provides a computer device, please refer to fig. 4, which includes a storage 401, a processor 402, and a computer program 403 stored in the storage and running on the processor, and when the processor 402 executes the above program, the method in the first embodiment is implemented.
Since the computer device introduced in the fourth embodiment of the present invention is a computer device used for implementing the unmanned aerial vehicle energy-based coverage path planning method in the first embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, a person skilled in the art can know the specific structure and deformation of the computer device, and thus details are not described herein. All the computer devices used in the method in the first embodiment of the present invention are within the scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.
Claims (10)
1. A method for planning a coverage path based on unmanned aerial vehicle energy is characterized by comprising the following steps:
step S1: initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information;
step S2: determining an observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, connecting every two sampling points to establish a segmented path, establishing a power speed model of the unmanned aerial vehicle according to a kinetic energy theorem, calculating the energy consumption of the unmanned aerial vehicle on the segmented path based on the power speed model and a digital surface model, and establishing an energy consumption graph of the whole area to be observed according to the energy consumption on the segmented path;
step S3: and traversing the energy consumption graph by adopting a genetic algorithm to obtain a coverage path with the minimum total energy consumption, wherein the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
2. The method according to claim 1, wherein step S1 specifically comprises:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of a sampling area, and recording and storing the position and elevation information of all grid primitives;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
3. The method according to claim 1, wherein step S2 specifically comprises:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; carrying out simplification analysis on a formula of a kinetic energy theorem to obtain a power speed model of the unmanned aerial vehicle:
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
4. The method according to claim 1, wherein step S3 specifically comprises:
step S3.1: initializing population parameters, wherein the population parameters specifically comprise population number Cp, chromosome gene number Cg, iteration number C and cross probability PcProbability of variation Pm；
Step S3.2: calculating fitness F between chromosomesi＝1/∑EpqAnd selecting the optimized individuals according to a preset probability, selecting the probability Pi＝k·Fi；
Step S3.3: in the crossing process, a partial matching crossing algorithm is adopted, two individuals are randomly selected, a plurality of gene segments are exchanged at corresponding positions to prevent local convergence, and then two genes of the individuals are randomly selected to be exchanged to realize mutation operation;
step S3.4: and (3) repeatedly executing the steps S3.1-S3.3, and outputting the individual with the maximum fitness as an optimal solution when the preset iteration times are reached, wherein the optimal solution corresponds to the coverage path with the minimum total energy consumption.
5. The method of claim 4, wherein the predetermined probability is set according to a convergence effect.
6. The utility model provides a coverage route planning device based on unmanned aerial vehicle energy which characterized in that includes:
the digital surface model establishing module is used for initializing an unmanned aerial vehicle task map, marking the boundary of a region to be observed, calculating the area of a sampling region according to the observation resolution, and establishing a digital surface model of the sampling region by utilizing preset grid primitive information;
the power speed model building module is used for determining the observation sampling position of the unmanned aerial vehicle on each grid surface, wherein the observation sampling position comprises at least two sampling points, each two sampling points are connected to build a subsection path, a power speed model of the unmanned aerial vehicle is built according to a kinetic energy theorem, the energy consumption of the unmanned aerial vehicle on the subsection path is calculated based on the power speed model and the digital surface model, and an energy consumption graph of the whole area to be observed is built according to the energy consumption on the subsection path;
and the energy consumption graph traversal module is used for traversing the energy consumption graph by adopting a genetic algorithm to obtain a coverage path with the minimum total energy consumption, wherein the total energy consumption is the sum of the energy consumption of the unmanned aerial vehicle on each segmented path.
7. The apparatus of claim 6, wherein the digitized surface modeling module is specifically configured to perform the steps of:
step S1.1: estimating the total area S of the region to be observed0And according to a calculation formula of the observation resolution, calculating the flight height of the unmanned aerial vehicle, and then according to the camera field angleAnd the flying height H of the unmanned aerial vehicle, determining the area of a sampling region:
step S1.2: generating gridding primitive information by using preset software, wherein the quantity N of the primitives is S through a formula No/SsCalculation of SoDenotes the area of the observation region, SsRepresenting the area of a sampling area, and recording and storing the position and elevation information of all grid primitives;
step S1.3: and determining the order of a tensor product Bezier curved surface according to the number N of the primitives, generating the tensor product Bezier curved surface based on the order of the curved surface and the recorded position and elevation information of the grid primitives, and taking the tensor product Bezier curved surface as a digital surface model of the sampling area.
8. The apparatus of claim 6, wherein the power velocity model building module is specifically configured to perform the steps of:
step S2.1: the process from starting to stable flight state of the unmanned aerial vehicle is described by adopting a kinetic energy theorem, wherein the formula of the kinetic energy theorem is as follows:
wherein P represents the output power in the steady state, fr＝fs+fa(fs＝1/2V2CDAρ,fa＝kG)，frRepresenting static friction in flight, where Aρ represents the Reynolds coefficient of air, and CDExpressing lift coefficient, k static air friction coefficient, V air flow velocity, fsRepresenting air friction in flight; simplifying and analyzing the formula of the kinetic energy theorem to obtain a power-speed model of the unmanned aerial vehicleType (2):
wherein v is0And P0Respectively representing the flying speed and the corresponding power output in a stable state, m representing the takeoff mass of the unmanned aerial vehicle, and C being a constant related to the air friction;
step S2.2: calculating longitude and latitude and height information of the unmanned aerial vehicle at a space sampling point based on a DSM model, and further obtaining length and gradient information of a segmented path through an earth arc model;
step S2.3: calculating the energy consumption of the unmanned aerial vehicle on the segmented path according to the power speed model and the distance and gradient information of the two-point space path;
step S2.4: and establishing an energy consumption graph of the whole region to be observed according to the energy consumption on the segmented path.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed, implements the method of any one of claims 1 to 5.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 5 when executing the program.
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