CN109520504B - Grid discretization-based unmanned aerial vehicle patrol route optimization method - Google Patents
Grid discretization-based unmanned aerial vehicle patrol route optimization method Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle inspection path optimization method based on grid discretization, and belongs to the technical field of unmanned aerial vehicle inspection. The method comprises the steps of firstly, obtaining coordinate parameters of a patrol area and carrying out visualization processing; then, taking a circular area with a fixed scanning area of the unmanned aerial vehicle as a grid, dividing a patrol area by using the grid, and calibrating the center of a discrete grid as a discrete target point; then, carrying out scheduling modeling on the multiple unmanned aerial vehicles, establishing an optimized target and constraint conditions, and solving an optimized patrol route of a target point after the unmanned aerial vehicles are dispersed; and finally, performing path planning on the discrete target points by adopting an improved A-star algorithm, searching optimal target nodes for path planning, and obtaining the optimized flight path of the unmanned aerial vehicle after iterative optimization. The invention can improve the coverage rate of the inspection area, reduce the number of unmanned aerial vehicles and position the target in the inspection area.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle inspection path optimization method based on grid discretization.
Background
The unmanned aerial vehicle technology is a hot research field of military and civil aircrafts recently, and has wide application in military applications such as battlefield reconnaissance, monitoring and target positioning, and civil applications such as environment detection, aerial photography, resource exploration, disaster patrol and logistics transportation.
The traditional patrol task usually depends on related personnel to carry out manual patrol, and requires certain professional skills of the personnel, and the traditional mode has the disadvantages of large workload, high operation cost and low patrol efficiency. The unmanned aerial vehicle is taken as an unmanned aerial vehicle which has power, can be controlled, can carry various task equipment, can execute multiple tasks and can be repeatedly used, has the characteristics of convenience for carrying, simplicity in operation, rapidness in reaction, rich load, wide task application, low requirement on environment during taking off and landing, autonomous flight and the like, can be applied to various fields, and can complete patrol operation.
However, in actual flight operation, the unmanned aerial vehicle is constrained by objective conditions such as self cruising ability and terrain factors, and for areas with an excessively large patrol range, patrol operation of all monitoring areas cannot be achieved. Therefore, how to optimize the unmanned aerial vehicle patrol route according to a plurality of constraint conditions, reduce the number of the unmanned aerial vehicle, and improve the patrol coverage rate of the unmanned aerial vehicle becomes a hotspot of the research on the practical application scene of the unmanned aerial vehicle.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle inspection path optimization method based on grid discretization, which is used for improving the coverage rate of an inspection area, reducing the number of unmanned aerial vehicle stands, positioning targets in the inspection area and obtaining the optimized flight path of the unmanned aerial vehicle after iterative optimization.
An unmanned aerial vehicle patrol route optimization method based on grid discretization comprises the following steps:
the invention is premised on the following assumptions:
(1) the flying height of the unmanned aerial vehicle is h1～h2The flying speed is v, and the battery energy of each unmanned aerial vehicle is E;
(2) the distance between the unmanned aerial vehicle and the horizontal ground is a fixed value h, and the three-dimensional coordinate of the position of the unmanned aerial vehicle can be known at any time;
(3) the effective patrol range of the unmanned aerial vehicle is that the elevation angle of the area plane and the unmanned aerial vehicle is larger than theta and is not blocked by a barrier, and the take-off and landing points of all the unmanned aerial vehicles are the same;
(4) assuming that all unmanned aerial vehicles fly autonomously according to the optimized path without manual control, and automatically returning to a take-off and landing point after completing a task;
(5) the influence of the take-off, landing and turning processes of the unmanned aerial vehicle on the endurance time is assumed to be ignored.
Step S1, acquiring coordinate parameters of the patrol area from map software;
step S2, performing visualization processing according to the coordinate parameters;
step S3, discretizing the total area of the patrol area, dividing the patrol area into grids, taking the grid center as a target point, and converting the patrol area into a plurality of discrete target points;
step S4, establishing a multi-unmanned aerial vehicle dispatching model, and converting the optimized patrol route of seeking n unmanned aerial vehicles in a patrol area into the optimized patrol route of seeking the unmanned aerial vehicles passing through discrete target points;
step S5, performing path planning on the discrete target points by adopting an improved A-star algorithm, and searching for the optimal target node of the path planning;
and S6, obtaining the optimal flight path of the unmanned aerial vehicle after iterative optimization according to the optimal target node, and outputting the optimal planning path.
In step S2, the visualization process includes reading coordinate data through an import function, constructing a three-dimensional space model by using a fill function, calibrating a patrol area, setting a fixed distance between the drone and the horizontal ground, and dynamically adjusting the flying height of the drone by using a drone control system according to a change in terrain height, so that the circular area scanned by the drone remains unchanged, thereby performing discretization on the patrol area, and positioning an object in the patrol area according to the three-dimensional coordinates of the drone.
In step S3, the discretization process is to discretize the patrol area with a circle having a fixed scanning area of the unmanned aerial vehicle and cover the patrol area with the circle, discretize the patrol area into a plurality of circular grids, calibrate the centers of the discretization grids as representative discretization target points, and discretize a large-scale target area into a plurality of discretization target points, so as to reduce the calculation amount, thereby facilitating the determination of the optimized patrol route of the unmanned aerial vehicle passing through the discretization target points in step S4.
And step S4, specifically, carrying out scheduling modeling on multiple unmanned aerial vehicles to obtain an optimized solution of the coverage rate and the number of the racks of the unmanned aerial vehicles. Seeking the optimized patrol route of n unmanned aerial vehicles in a patrol area can be converted into seeking the optimized patrol route of the unmanned aerial vehicles passing through a discrete target point, and by comparing patrol coverage rates of different frames, the optimized solution of the unmanned aerial vehicle frame number is found while the coverage rate is ensured, and the following target equation is established:
in the formula (1), Cr represents a coverage,
represents NkStarting from the point of take-off and landing to MiStarting patrol until MjFinish returning to the point of take-off and landing, m denotes patrolLooking up the total number of discrete target points, NkDenotes the kth drone among n drones, i denotes the ith row, j denotes the jth column, MiDenotes the M < th > elementiAn object point, MjDenotes the M < th > elementjA target point;
0＜∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
0＜A(t)+B(t)+C(t)≤E (3)
in the formula (3), a (t) represents the energy consumption of the operation module of each unmanned aerial vehicle, b (t) represents the energy consumption of the flight control module of each unmanned aerial vehicle, c (t) represents the energy consumption of the communication module of each unmanned aerial vehicle, and t represents time;
the constraint condition of the formula (2) indicates that the number of target points flown by multiple unmanned aerial vehicles is smaller than the total number of the target points, and the constraint condition of the formula (3) indicates that the energy consumption of each unmanned aerial vehicle is within the constraint of the battery energy E.
Step S5, using modified A*Algorithm, path optimization for discrete target points, A*The algorithm is realized by an estimation function, the value of the estimation function needs to be calculated when the unmanned aerial vehicle walks each step, the node with the minimum estimation function value is the position where the unmanned aerial vehicle needs to arrive next step, and the expression of the estimation function is as follows:
f(n)＝g(n)+h(n) (4)
in the formula (4), g (n) refers to the actual distance from the starting point of the path to the node n in the path planning, and the value of the function g (n) is an actual numerical value; h (n) is the estimated distance taken from node n to the end of the path plan, and function h (n) is the initial estimation function. The function f (n) is the algebraic sum of the value of the function g (n) and the function h (n), and represents the total route that the unmanned aerial vehicle passes through in the whole route planning.
The improved A algorithm expands the two-dimensional Euclidean distance to the three-dimensional Euclidean distance in an initial estimation function h (n), and introduces an energy loss constraint cost parameter, and the formula is as follows:
h(n)＝α·di,j+β·E(t) (5)
in the formula (5), α represents a distance factor, di,jRepresenting the euclidean distance between two points i and j in space,beta represents the energy consumption factor, E (t) represents the residual energy, di,jThe expression of (c) is:
in the formula (6), xiX-axis coordinate, X, representing a spatial point ijAn X-axis coordinate representing a spatial point j; y isiY-axis coordinate, Y, representing spatial point ijA Y-axis coordinate representing a spatial point j; z is a radical of formulaiZ-axis coordinate, Z, representing a spatial point ijA Z-axis coordinate representing a spatial point j;
the specific implementation steps of step S6 are as follows:
step 1: initializing, and generating an OPEN list and a CLOSED list, wherein the OPEN list stores all generated but unexplored nodes, and the CLOSED list records accessed nodes.
Step 2: the initial value is loaded, and the information of the start point is loaded into the OPEN list generated by step1, and the generated OPEN list includes only the start node and is denoted by f (n) h (n).
Step 3: searching an OPEN list, if no numerical value exists in the OPEN list after searching, failing to plan the path starting point, and turning to execute step2 to continuously search the starting point of the path planning; if the target node of the path plan has already been loaded in the OPEN list, step4 is executed.
Step 4: and (3) searching the value of a function f (n), calculating the value in the OPEN list by using an estimation function, marking the minimum point of the values of all nodes f (n) in the search list as the best node BESTNODE, loading the node into the CLOSED list, taking the node as the current node, and carrying out the next operation.
Step 5: and judging the target point, and judging whether the optimal node is the target point of the path according to the estimation function.
If yes, finishing the algorithm and outputting the planned path nodes; if not, the neighbor node of the current node is loaded into the OPEN list for step 3. And sequentially circulating until a specified target node is found.
Step 6: and outputting the path, storing the optimal node in a CLOSED list, and outputting the planned path according to the nodes in the CLOSED list.
The beneficial effects of the invention are as follows: according to the unmanned aerial vehicle inspection path optimization method based on grid discretization, disclosed by the invention, a multi-unmanned aerial vehicle scheduling model is established by designing an optimization target and constraint conditions, the coverage rate of an inspection area is improved, the number of unmanned aerial vehicle shelves is reduced, the target in the inspection area can be positioned, and an optimization planning scheme is provided for large-range unmanned aerial vehicle inspection.
Drawings
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle inspection path optimization method based on grid discretization provided by the invention;
fig. 2 is a target patrol area distribution diagram of the unmanned aerial vehicle patrol route optimization method based on grid discretization provided by the invention;
fig. 3 is a schematic diagram of area grid discretization of an unmanned aerial vehicle patrolling path optimization method based on grid discretization provided by the invention;
fig. 4 is a target positioning diagram of the unmanned aerial vehicle patrol route optimization method based on grid discretization provided by the invention.
Detailed Description
The invention is further illustrated but not limited by the following figures and examples.
Example (b):
the invention provides an unmanned aerial vehicle inspection path optimization method based on grid discretization, which is based on the following assumption conditions:
(1) the flying height of the unmanned aerial vehicle is h1～h2The flying speed is v, and the battery energy of each unmanned aerial vehicle is E;
(2) the distance between the unmanned aerial vehicle and the horizontal ground is a fixed value h, and the three-dimensional coordinate of the position of the unmanned aerial vehicle can be known at any time;
(3) the effective patrol range of the unmanned aerial vehicle is that the elevation angle of the area plane and the unmanned aerial vehicle is larger than theta and is not blocked by a barrier, and the take-off and landing points of all the unmanned aerial vehicles are the same;
(4) assuming that all unmanned aerial vehicles fly autonomously according to the optimized path without manual control, and moving back to the take-off and landing point after completing the task;
(5) the influence of the take-off, landing and turning processes of the unmanned aerial vehicle on the endurance time is assumed to be ignored.
As shown in fig. 1, an unmanned aerial vehicle patrol route optimization method based on grid discretization includes the following steps:
step S1, acquiring coordinate parameters of the patrol area from map software;
step S2, performing visualization processing according to the coordinate parameters;
step S3, discretizing the total area of the inspection area, dividing the inspection area into grids, taking the grid center as a target point, and converting the inspection area into a plurality of discrete target points;
step S4, establishing a multi-unmanned aerial vehicle dispatching model, and converting the optimized patrol route of seeking n unmanned aerial vehicles in the patrol area into the optimized patrol route of seeking the target point of the unmanned aerial vehicle after dispersion;
step S5, adopting improved A-algorithm to plan the path of the discrete target point and find the optimal target node of the path plan;
and S6, obtaining the optimal flight path of the unmanned aerial vehicle after iterative optimization according to the optimal target node, and outputting the optimal planning path.
As shown in fig. 2, in step S2, visualization processing is performed on the basis of coordinate parameters and the like using MATLAB. Reading coordinate data through an xlsread function, constructing a three-dimensional space model by using a mesh function, and calibrating a target area by using a rectangle function; the difference of terrain elevation data is considered, the altitude distance between the unmanned aerial vehicle and the patrol area can be changed constantly, the scanning area is changed constantly, the sight line of the unmanned aerial vehicle is blocked by a barrier, and other factors are caused, the distance between the unmanned aerial vehicle and the horizontal ground is fixed, the flight height of the unmanned aerial vehicle is adjusted dynamically according to the change of the terrain height, the scanning circle area of the unmanned aerial vehicle can be kept unchanged, so that the patrol area can be subjected to discretization, and targets in the patrol area can be positioned according to the three-dimensional coordinates of the unmanned aerial vehicle.
As shown in fig. 3, in step S3, discretization processing is performed on the patrol area. The method comprises the steps that an unmanned aerial vehicle is used for fixedly scanning a circular area to cover a patrol area, the patrol area is dispersed into a plurality of grids, the center of the dispersed grid is calibrated to be a representative dispersed target point, a large-range target area is dispersed into a plurality of dispersed target points, and the calculation amount is reduced; although there are blank areas that appear not to be surveyed, in consideration of the actual situation, the drone will present a "belt-like" patrol path as it flies from one target point to the next, and these blank areas will be scanned. Secondly, because there is unevenness at the border of the patrol area, some areas at the border may not be scanned, but the area is small compared with the total patrol area, so the areas which are not scanned are negligible.
In the step S4, scheduling and modeling of multiple unmanned aerial vehicles are carried out, the seeking of the optimized patrol route of n unmanned aerial vehicles in the patrol area can be converted into the seeking of the optimized patrol route of the unmanned aerial vehicles passing through the discrete target points, and by comparing patrol coverage rates under different shelves, the coverage rate is ensured, and meanwhile, an optimized solution of the unmanned aerial vehicle shelf number is found:
establishing a target equation:
in the formula (1), Cr represents a coverage,
represents NkStarting from the point of take-off and landing to MiStarting patrol until MjEnding to return to the starting and landing point, wherein m represents the total number of the discrete target points for patrol, NkDenotes the kth drone among n drones, i denotes the ith row, j denotes the jth column, MiDenotes the M thiAn object point, MjDenotes the M < th > elementjA target point;
0＜∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
0＜A(t)+B(t)+C(t)≤E (3)
in the formula (3), a (t) represents the energy consumption of the operation module of each unmanned aerial vehicle, b (t) represents the energy consumption of the flight control module of each unmanned aerial vehicle, c (t) represents the energy consumption of the communication module of each unmanned aerial vehicle, and t represents time;
the constraint condition of the formula (2) indicates that the number of target points flown by multiple unmanned aerial vehicles is smaller than the total number of the target points, and the constraint condition of the formula (3) indicates that the energy consumption of each unmanned aerial vehicle is within the constraint of the total energy E.
By introducing the energy constraint condition, the endurance time accuracy of the unmanned aerial vehicle can be improved, the condition that the energy of the unmanned aerial vehicle is exhausted unexpectedly midway is effectively avoided, and the success rate of the inspection task is improved.
After discretizing the patrol area into target points, in step S5, modified a is used*Discrete target point path optimization of the algorithm, A*The algorithm is realized through an estimation function, the value of the estimation function needs to be calculated when the unmanned aerial vehicle walks in each step, the node with the minimum estimation function value is the position to which the unmanned aerial vehicle needs to arrive in the next step, and the general expression of the estimation function is as follows:
f(n)＝g(n)+h(n) (4)
in the formula (4), g (n) refers to the actual distance traveled by the unmanned aerial vehicle from the starting point of the path to the node n in the path planning, and the value of the function g (n) is an actual numerical value; h (n) is the estimated distance from node n to the end of the path plan, and the function h (n) is the initial estimation function. The function f (n) is the algebraic sum of the value of the function g (n) and the function h (n), and represents the total route that the unmanned plane passes through in the whole route planning.
The improved A-algorithm expands the two-dimensional Euclidean distance to the three-dimensional Euclidean distance in the initial estimation function h (n), and introduces an energy loss constraint cost parameter, and the formula is as follows:
h(n)＝α·di,j+β·E(t) (5)
in the formula (5), α represents a distance factor, di,jRepresenting the Euclidean distance between two points i and j in space, beta representing an energy consumption factor, E (t) representing residual energy, di,jThe expression of (a) is:
in the formula (6), xiX-axis coordinate, X, representing spatial point ijAn X-axis coordinate representing a spatial point j; y isiY-axis coordinate, Y, representing spatial point ijA Y-axis coordinate representing a spatial point j; z is a radical ofiZ-axis coordinate, Z, representing a spatial point ijA Z-axis coordinate representing a spatial point j;
in step S6, the specific implementation steps are as follows:
step 1: initializing, generating an OPEN list and a CLOSED list, wherein the OPEN list stores all generated nodes which are not investigated, and the CLOSED list records accessed nodes.
Step 2: the initial value is loaded, and the information of the start point is loaded into the OPEN list generated by step1, and the generated OPEN list only contains the start node, and is denoted by f (n) ═ h (n).
Step 3: searching an OPEN list, if no numerical value exists in the OPEN list after the search, failing to plan the path starting point, and turning to execute step2 to continuously search the starting node of the path plan; if the target node of the path plan is already loaded in the OPEN list, step4 is executed.
Step 4: and (3) searching the value of a function f (n), calculating the value in the OPEN list by using an estimation function, marking the minimum point of the values of all nodes f (n) in the search list as the best node BESTNODE, loading the node into the CLOSED list, taking the node as the current node, and carrying out the next operation.
Step 5: and judging the target point, and judging whether the optimal node is the target point of the path or not according to the estimation function. If yes, finishing the algorithm and outputting the planned path nodes; if not, the neighbor node of the current node is loaded into the OPEN list for step 3. And sequentially circulating until a specified target node is found.
Step 6: and outputting the path, storing the optimal node in a CLOSED list, and outputting the planned path according to the nodes in the CLOSED list.
With reference to fig. 4, because the distance between the flying height of the unmanned aerial vehicle and the horizontal ground is fixed, the three-dimensional coordinate of the position of the unmanned aerial vehicle can be known at any time, when the target is positioned at the circle center of the scanning circle of the unmanned aerial vehicle, the position coordinate of the target can be obtained by subtracting the flying height value from the height value of the three-dimensional coordinate of the unmanned aerial vehicle, and the target is precisely positioned.
Claims (1)
1. An unmanned aerial vehicle inspection path optimization method based on grid discretization,
the following assumptions are premised:
(1) the flying height of the unmanned aerial vehicle is h1～h2The flying speed is v, and the battery energy of each unmanned aerial vehicle is E;
(2) the distance between the unmanned aerial vehicle and the horizontal ground is a fixed value h, and the three-dimensional coordinate of the position of the unmanned aerial vehicle can be known at any time;
(3) the effective patrol range of the unmanned aerial vehicle is that the elevation angle of the area plane and the unmanned aerial vehicle is larger than theta and is not blocked by a barrier, and the take-off and landing points of all the unmanned aerial vehicles are the same;
(4) assuming that all unmanned aerial vehicles fly autonomously according to the optimized path without manual control, and automatically returning to a take-off and landing point after completing a task;
(5) the influence of the take-off, landing and turning processes of the unmanned aerial vehicle on the endurance time is assumed to be ignored;
the unmanned aerial vehicle patrol route optimization method based on grid discretization comprises the following steps:
step S1, acquiring coordinate parameters of the patrol area from map software;
step S2, performing visualization processing according to the coordinate parameters;
in step S2, the visualization process includes reading coordinate data through an import function, constructing a three-dimensional space model by using a fill function, calibrating a patrol area, setting a fixed distance between the drone and the horizontal ground, and dynamically adjusting the flying height of the drone by using a drone control system according to a change in terrain height so that the circular area scanned by the drone remains unchanged, thereby facilitating discretization of the patrol area and positioning an object in the patrol area according to the three-dimensional coordinates of the drone;
step S3, discretizing the total area of the patrol area, dividing the patrol area into grids, taking the grid center as a target point, and converting the patrol area into a plurality of discrete target points;
in step S3, the discretization process is to discretize the patrol area by a circle with a fixed unmanned aerial vehicle scanning area and cover the patrol area by the circle, to discretize the patrol area into a plurality of circular meshes, and to calibrate the center of the discretized mesh as a representative discretized target point;
step S4, establishing a multi-unmanned aerial vehicle dispatching model, and converting the optimized patrol route of seeking n unmanned aerial vehicles in a patrol area into the optimized patrol route of seeking the unmanned aerial vehicles passing through discrete target points;
step S5, performing path planning on the discrete target points by adopting an improved A-star algorithm, and searching for the optimal target node of the path planning;
step S6, obtaining the optimal flight path of the unmanned aerial vehicle after iterative optimization according to the optimal target node, and outputting the optimal planning path;
the specific implementation steps of step S6 are as follows: step 1: initializing, generating an OPEN list and a CLOSED list, wherein the OPEN list stores all generated nodes which are not investigated, and the CLOSED list records accessed nodes;
step 2: loading an initial value, loading information of a starting point into an OPEN list generated by step1, wherein the generated OPEN list only comprises the starting node and is recorded as f (n) ═ h (n);
step 3: searching an OPEN list, if no numerical value exists in the OPEN list after the search, failing to plan the path starting point, and turning to execute step2 to continuously search the starting node of the path plan; if the target node of the path plan is loaded in the OPEN list, step4 is executed;
step 4: searching the value of a function f (n), calculating the value in an OPEN list by using an estimation function, marking the minimum point of the values of all nodes f (n) in the search list as an optimal node BESTNODE, loading the node into a CLOSED list, and taking the node as the current node for the next operation;
step 5: judging a target point, and judging whether the optimal node is the target point of the path according to the estimation function; if yes, finishing the algorithm and outputting the planned path nodes; if the current node is not the target point specified in the path, loading the adjacent nodes of the current node into an OPEN list, and performing step 3; sequentially circulating until a specified target node is found;
step 6: the output path stores the optimal node, stores the optimal node in a CLOSED list and outputs a planning path according to the node in the CLOSED list;
the method is characterized in that:
in step S4, the following target equation is specifically established:
in the formula (1), Cr represents a coverage,
represents NkStarting from the take-off and landing point to MiStarting patrol until MjEnding to return to the starting and landing point, wherein m represents the total number of the discrete target points for patrol, NkRepresents the k-th unmanned plane in the n unmanned planes, i represents the ith row, j represents the jth column, MiDenotes the M thiAn object point, MjDenotes the M thjA target point;
0＜∑i∈[1,m]∑j∈[1,m](j-i+1)≤m (2)
0＜A(t)+B(t)+C(t)≤E (3)
in the formula (3), a (t) represents the energy consumption of the operation module of each unmanned aerial vehicle, b (t) represents the energy consumption of the flight control module of each unmanned aerial vehicle, c (t) represents the energy consumption of the communication module of each unmanned aerial vehicle, and t represents time;
after dispersing the patrol area into target points, optimizing a discrete target point path by adopting an improved A-algorithm, wherein the A-algorithm is realized by an estimation function, the value of the estimation function needs to be calculated when the unmanned aerial vehicle walks each step, the node with the minimum estimation function value is the position where the unmanned aerial vehicle needs to arrive next step, and the general expression of the estimation function is as follows:
f(n)＝g(n)+h(n) (4)
in the formula (4), g (n) refers to the actual distance traveled by the unmanned aerial vehicle from the starting point of the path to the node n in the path planning, and the value of the function g (n) is an actual numerical value; h (n) is the estimated distance from node n to the end of the path plan, and the function h (n) is the initial estimation function; the function f (n) is the algebraic sum of the value of the function g (n) and the function h (n), and represents the total route passed by the unmanned aerial vehicle in the whole route planning;
in step S5, the modification A*The algorithm expands the two-dimensional Euclidean distance to the three-dimensional Euclidean distance in an initial estimation function h (n), and introduces an energy loss constraint cost parameter, and the formula is as follows:
h(n)＝α·di,j+β·E(t) (5)
in the formula (5), h (n) is an estimated distance spent from the node n to the end point of the path plan, and a function h (n) is an initial estimation function; α represents a distance factor, di,jRepresents the Euclidean distance between two points i and j in space, beta represents an energy consumption factor, E (t) represents residual energy, di,jThe expression of (a) is:
in the formula (6), xiX-axis coordinate, X, representing spatial point ijX-axis coordinates representing spatial point j; y isiY-axis coordinate, Y, representing spatial point ijY-axis coordinates representing spatial point j; z is a radical of formulaiZ-axis coordinate, Z, representing a spatial point ijRepresenting the Z-axis coordinate of spatial point j.
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| CN110941284A (en) * | 2019-11-20 | 2020-03-31 | 黄锦山 | Relay network deployment method based on unmanned aerial vehicle and ground station equipment |
| CN111452988A (en) * | 2020-01-09 | 2020-07-28 | 国网内蒙古东部电力有限公司呼伦贝尔供电公司 | Unmanned aerial vehicle multi-machine cooperative intelligent inspection system and method based on ubiquitous power Internet of things |
| CN112506216B (en) * | 2020-11-18 | 2022-12-30 | 天津(滨海)人工智能军民融合创新中心 | Flight path planning method and device for unmanned aerial vehicle |
| CN113055850B (en) * | 2021-03-04 | 2022-05-03 | 重庆邮电大学 | Energy minimization method for unmanned aerial vehicle cruise communication |
| CN113010834B (en) * | 2021-03-26 | 2022-08-19 | 上海科建工程管理股份有限公司 | Ground inspection robot inspection coverage verification method and device based on GIS |
| CN113203417B (en) * | 2021-04-06 | 2022-08-19 | 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 |
| CN113378318B (en) * | 2021-06-24 | 2022-07-15 | 成都飞机工业（集团）有限责任公司 | Shape design method of special wrench for complex space |
| CN113625771B (en) * | 2021-09-10 | 2023-02-21 | 中国人民解放军国防科技大学 | Shadow following single unmanned aerial vehicle area coverage path planning method |
| CN113625772B (en) * | 2021-09-10 | 2023-02-24 | 中国人民解放军国防科技大学 | Shadow following multi-unmanned aerial vehicle area coverage path planning method |
| CN114143872B (en) * | 2021-11-25 | 2023-03-28 | 同济大学 | Multi-mobile-device positioning method based on unmanned aerial vehicle-mounted WiFi probe |
| CN116824414B (en) * | 2023-08-29 | 2023-11-14 | 深圳市硕腾科技有限公司 | Method for rapidly deploying RTK (real time kinematic) by unmanned aerial vehicle |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018103242A1 (en) * | 2016-12-09 | 2018-06-14 | 武汉科技大学 | Electric tower inspection method of using four-rotor unmanned aerial vehicle based on motor learning |
| 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 |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880186B (en) * | 2012-08-03 | 2014-10-15 | 北京理工大学 | flight path planning method based on sparse A* algorithm and genetic algorithm |
| GB2505464B (en) * | 2012-08-31 | 2019-12-18 | Bae Systems Plc | Route planning |
| CN103528586B (en) * | 2013-10-31 | 2016-06-01 | 中国航天时代电子公司 | Path Planning based on fault grid designs |
| CN105222779B (en) * | 2015-08-26 | 2018-03-09 | 北京农业智能装备技术研究中心 | The path planning method and device of plant protection unmanned plane |
| CN105737819B (en) * | 2016-02-25 | 2018-10-23 | 西北工业大学 | No-manned plane three-dimensional Route planner based on space compression and computation of table lookup |
| CN106295141B (en) * | 2016-08-01 | 2018-12-14 | 清华大学深圳研究生院 | A plurality of unmanned plane determining method of path and device for reconstructing three-dimensional model |
| CN107450576B (en) * | 2017-07-24 | 2020-06-16 | 哈尔滨工程大学 | Method for planning paths of bridge detection unmanned aerial vehicle |
| CN108253987B (en) * | 2017-12-29 | 2021-08-27 | 中国地质大学（武汉） | Unmanned aerial vehicle trajectory planning method, device and storage device based on A-x algorithm |
| CN108196575B (en) * | 2018-01-05 | 2020-11-03 | 湖北工业大学 | Unmanned aerial vehicle task allocation and route planning method |
| CN108681321B (en) * | 2018-04-10 | 2021-05-14 | 华南理工大学 | Underwater detection method for unmanned ship cooperative formation |
-
2018
- 2018-11-27 CN CN201811422746.6A patent/CN109520504B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018103242A1 (en) * | 2016-12-09 | 2018-06-14 | 武汉科技大学 | Electric tower inspection method of using four-rotor unmanned aerial vehicle based on motor learning |
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment;Chao Yin，等;《IEEE INTERNET OF THINGS JOURNAL》;20170630;第5卷(第2期);全文 * |
| 基于 Ａ＊算法的突防路径规划方法;张 弛，等;《信息化研究》;20150228;第41卷(第01期);全文 * |
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