CN105045274A - Intelligent tower connected graph construction method for unmanned aerial vehicle inspection track planning - Google Patents

Abstract

The invention provides a method for constructing an intelligent tower connectivity graph for planning an unmanned aerial vehicle inspection track, which comprises the following steps of: A. predicting the distribution of the power transmission line towers by adopting a unitary nonlinear regression prediction method to generate a plurality of tower lines, wherein each tower line covers a plurality of towers; B. connecting the plurality of tower lines to form a line connection diagram by using the critical condition of tower distribution; wherein the critical conditions include: the method comprises the following steps of (1) crossing conditions, parallel distribution conditions of a plurality of lines at close distances, tower steering conditions and tower branch conditions; C. and constructing a storage structure of the line connection graph. The invention can utilize the algorithm to carry out intelligent planning on the power transmission line, thereby improving the planning efficiency; the invention provides a whole-course inspection scheme, which not only inspects the route, but also inspects the route, thereby greatly improving the efficiency of line inspection; according to the invention, through an intelligent traversal algorithm, the optimal patrol route at the planned position in a line network is met, and traversal of all towers in the area under the condition of the shortest patrol distance is met.

Description

A kind of intelligent shaft tower connected graph construction method of patrolling and examining trajectory planning for unmanned plane

Technical field

The present invention relates to a kind of intelligent shaft tower connected graph construction method of patrolling and examining trajectory planning for unmanned plane.

Background technology

Overhead line inspection is the groundwork of grid company to transmission line of electricity daily servicing, and routine inspection mode can be divided into manual inspection, have people's helicopter routing inspection and unmanned plane to patrol and examine.Although manual inspection is the most frequently used routine inspection mode, but there is the deficiencies such as efficiency is comparatively slow, the restriction of climate geographical environment always, machine patrols mode and is just being widely studied and application, especially the unmanned plane safe and efficient characteristic of patrolling and examining, overhead power line is patrolled and examined flying robot (FlyingRobotForOverheadPower-lineInspection, FROPI) and is had increasing using value.

UAS includes unmanned plane body platform, mission payload and data wireless and transmits three parts.The unmanned aerial vehicle platform being applied to polling transmission line at present mainly comprises miniature many rotor wing unmanned aerial vehicles machine, unmanned plane helicopter etc.; Mission payload mainly comprises to be responsible for patrolling and examining imaging device, as visible light camera, thermal infrared imager etc.; Wireless transmission mainly comprises the transmission (number passes) to telecommand, and the image data transmission of high bandwidth (figure passes), is referred to as measuring and control data chain.

UAS is the main body of polling transmission line work, first the autonomous line walking process of unmanned plane will be determined to make an inspection tour region, on the basis getting accurate circuit geographic coordinate, according to shooting angle and flight safety distance planning unmanned plane during flying course line, unmanned plane according to the line of flight uploaded in advance by/falling a little sets out patrols and examines flight to circuit.Unmanned plane should get as much as possible in tour process desirable patrols and examines data, guarantee that being perfectly safe of flight is reliable again, unmanned plane during flying region and the line of flight directly affect the safety of patrolling and examining quality, patrolling and examining efficiency or even unmanned plane during flying and overhead transmission line, and therefore the mission planning of power transmission line unmanned machine plays vital effect.Simultaneously, overhead transmission line is distributed in mountain area more, there is scissors crossing region, region in a big way in transmission line of electricity intricate, grid therefore in large regions select best patrol and examine circuit to raising unmanned plane operating efficiency, improve efficiency when safe flight play key effect.

The mission planning of patrolling and examining for power transmission line unmanned machine at present adopts planning mode manually mostly, although this kind of mode ensure that the flight safety of unmanned plane, but efficiency is lower, cannot meet the needs that extensive unmanned plane is patrolled and examined, manual planning mode is difficult to realize optimal programming in large regions simultaneously.

First the mission planning of UAV Intelligent line walking needs to build a kind of data structure, to carry out intelligent algorithm planning to all shaft tower positions in whole overhead transmission line region.

Chinese patent application 201410283175.8 discloses the high-precision three-dimensional method for reconstructing in a kind of transmission line of electricity and corridor.The three dimensional point cloud in transmission line of electricity and corridor is gathered by airborne laser radar, three dimensional point cloud is tested, eliminate the point of mistake and the point of height anomaly, and three dimensional point cloud is carried out automatic classification based on voxel classification method by utilization, algorithm finally carries out automatic three-dimensional reconstruction to transmission line of electricity and corridor.

Chinese patent application 201310676886.7 discloses a kind of unmanned aerial vehicle flight path planning algorithm searched for based on Dubins path and sparse A*.Dubins path is combined with sparse A* searching algorithm, adopts Dubins path as the heuristic function of sparse A* searching algorithm, and utilize the node in this heuristic function search volume, realize the trajectory planning of unmanned plane.

The defect of technique scheme is: 1, the autonomous mission planning means of existing overhead transmission line mainly lean on planning mode manually, and efficiency is lower, cannot meet the demand of the inspection of the complicated circuit of large regions; 2, trajectory planning generally only does one way mission planning, namely only patrols and examines going on one way circuit, there is no and carry out task inspection, therefore cause energy consumption waste in process of making a return voyage; 3, in the grid having scissors crossing, planing method is difficult to consider to find out best tour circuit from the overall situation manually, makes flying distance under the prerequisite completing all line inspections the shortest.

Summary of the invention

For the shortcoming of prior art, the object of this invention is to provide a kind of intelligent shaft tower connected graph construction method of patrolling and examining trajectory planning for unmanned plane.

To achieve these goals, the invention provides a kind of intelligent shaft tower connected graph construction method of patrolling and examining trajectory planning for unmanned plane, this connected graph construction method comprises the steps:

A, employing unitary non-linear regression Forecasting Methodology, predict electric power line pole tower distribution, generate some shaft tower circuits, every bar shaft tower circuit covers some shaft towers;

In this step, first three-dimensional space model is simplified to two-dimensional spatial model, adopts unitary non-linear regression Forecasting Methodology, electric power line pole tower distribution is predicted; Prediction mode adopts fiducial interval mode, predicts according to history shaft tower data (response variable) and judges newly inputting data (explanatory variable).Due to the singularity of shaft tower distribution, be summarized as special circumstances kind, and to determining the existence condition of often kind of special circumstances, to classify in algorithm process process.

B, the critical condition utilizing shaft tower to distribute, make some shaft tower circuits be communicated with and form circuit connected graph; Wherein said critical condition comprises: scissors crossing situation, many parallel distribution situations of circuit close together, shaft towers turn to situation, shaft tower branch situation;

In this step, there are special circumstances if reach shaft tower distribution and reach critical condition, carrying out critical types judgement, row relax of going forward side by side; The critical kind that shaft tower distributes is divided into following several:

, beyond setting shaft tower number, and there is point of crossing in scissors crossing situation: occur multiple point in forecast interval section;

Many the parallel distribution situations of circuit close together: occur multiple point in forecast interval section, beyond setting shaft tower number, but without point of crossing;

Shaft tower turns to situation: compared with predictive equation, occurs unique flex point in forecast interval;

, in forecast interval, there is multiple flex point in shaft tower branch situation: compared with predictive equation.

The storage organization of C, structure circuit connected graph.

The present invention builds shaft tower connected graph, to carry out intelligent task planning by linear regression prediction algorithm intelligence when only there being shaft tower geographic coordinate.

According to another embodiment of the present invention, steps A specifically comprises the steps:

A1, dimension-reduction treatment: dimension-reduction treatment is carried out to three-dimensional shaft tower geographic coordinate and is converted to two-dimensional coordinate; Set former A shaft tower three-dimensional coordinate as (x _t, y _t, z _t), x _trepresent shaft tower three dimensions longitude coordinate, y _trepresent shaft tower latitude coordinate, z _trepresent height above sea level residing for shaft tower, then after dimensionality reduction, A shaft tower coordinate is (x _t, y _t);

The establishment of A2, regression equation: the one-variable linear regression forecast model formula being applied to transmission line of electricity mission planning is as follows:

${\hat{Y}}_t=a+b{x}_t---\left(1\right)$

X in formula _trepresent t shaft tower longitude coordinate, represent t estimated latitude coordinate;

A3, fetch and return prediction step to be N, obtain the solving of parameter a, b in regression equation as follows:

$\left\{\begin{array}{c}a=\frac{\mathrm{\Σ}{Y}_i}{N}-b\frac{\mathrm{\Σ}{X}_i}{N}\\ b=\frac{\mathrm{N\Σ}{Y}_i{X}_i-\mathrm{\Σ}{Y}_i\mathrm{\Σ}{X}_i}{\mathrm{N\Σ}{X_i}^2-{\left(\mathrm{\Σ}{X}_i\right)}^2}\end{array}\right.---\left(2\right)$

Wherein, n is prediction moving step length; Due to the distance between two base shaft towers at tens meters of rice even up to a hundred not etc., majority of case by continuous many bases are formed with shaft tower can near linear section.

According to another embodiment of the present invention, in steps A 3, step-length N is 5 meters-10 meters.

According to another embodiment of the present invention, steps A comprises steps A 4 further: optimum prediction matching, by the determined curve of optimum fit curve determination shaft tower coordinate.Due to except special circumstances, this curve can be approximately straight line in certain distance, therefore have employed Linear regression in the present invention.Curve adopts " least square method ", namely " residual sum of squares (RSS) is minimum " determines linear position, from formula (1), the difference e i that the method for estimation of " least square method-OLS " should meet actual observed value Yi and predicted value is as far as possible the smaller the better, and ei representation formula is as follows:

$min(\Σ{e_i}^2)=min{(Y_i-\widehat{{\mathrm{\β}}_1}-\widehat{{\mathrm{\β}}_2}{x}_i)}^2---\left(3\right)$

Utilize Cramer's rule to solve the OLS estimator of observed reading form is:

$\left\{\begin{array}{c}\widehat{{\mathrm{\β}}_1}=\frac{\Σ{X_i}^2\ΣY_i-\ΣX_i\ΣY_i{X}_i}{N\Σ{X_i}^2-{\left(\ΣX_i\right)}^2}\\ \widehat{{\mathrm{\β}}_2}=\frac{N\ΣX_i{Y}_i-\ΣX_i\ΣY_i}{N\Σ{X_i}^2-{\left(\ΣX_i\right)}^2}\end{array}\right.---\left(4\right)$

According to the final straight-line equation of above-mentioned result matching be:

$\stackrel{\‾}{Y}=\widehat{{\mathrm{\β}}_1}+\widehat{{\mathrm{\β}}_2}\stackrel{\‾}{X}---\left(5\right)$

According to another embodiment of the present invention, steps A comprises steps A 5 further: build forecast interval.There is deviation to a certain degree in the determined curvilinear equation of the shaft tower due to reality and predictive equation, has therefore carried out interval prediction to Y value, namely builds the forecast interval of mean value.According to shaft tower distribution situation, setting level of significance a, the degree of confidence calculating Y mean value is the forecast interval of 1-a.

According to another embodiment of the present invention, step C specifically comprises the steps:

C1, set up shaft tower matrix, wherein ranks coordinate represents shaft tower number, and matrix data is shaft tower geographical location information; According to predicting the outcome shaft tower is divided into two types: node shaft tower and not a node shaft tower; Its interior joint comprises circuit start-stop shaft tower and intersection shaft tower; Not a node shaft tower is the inside shaft tower only belonging to uniline;

C2, for node shaft tower, build node shaft tower adjacency list;

C3, for not a node shaft tower, carry out matrix structure storage, row matrix coordinate represents affiliated circuit, and row coordinate represents shaft tower number.

In step C2, for node shaft tower, because shaft tower data volume is comparatively large, consider algorithm storage space and efficiency of algorithm, adopt Linked Storage Structure-adjacency list.

Building process for node shaft tower adjacency list is as follows:

C21, to sort by reference direction according to shaft tower geographical position coordinates;

C22, for each point of pole data structure just like properties:

A, subscript: for shaft tower line marker;

B, front abutment points: when node shaft tower is for intersection shaft tower, a upper adjacent line of front abutment points place circuit;

C, rear abutment points: when node shaft tower is for intersection shaft tower, next adjacent line of rear abutment points place circuit.

In step C3, carry out matrix structure storage for not a node shaft tower, row matrix coordinate represents affiliated circuit, and row coordinate represents shaft tower number.Note following situation:

I, whether be a node: head node represents one of end points of a circuit;

II, whether be crossover node: the point value that intersects is " 1 ", and ordinary node is " 0 ";

III, affiliated circuit: ordinary node has and only has an affiliated circuit, and crossover node has many affiliated circuits.

Compared with prior art, the present invention possesses following beneficial effect:

1, the present invention can utilize algorithm to carry out intelligent planning to transmission line of electricity, improves planning efficiency;

2, the present invention proposes omnidistance inspection scheme, not only patrol going in way, come and go in way and also carry out line inspection, thus greatly improve line walking efficiency;

3, for the mission planning of complicated grid, the present invention is by intelligent ergodic algorithm, and the patrol route of planning department's the best in line network, meets the traversal to shaft towers all in region in the shortest tour distance situation.

Below in conjunction with accompanying drawing, the present invention is described in further detail.

Accompanying drawing explanation

Fig. 1 is the overview flow chart of the intelligent shaft tower connected graph construction method of embodiment 1;

Fig. 2 is in embodiment 1, the process flow diagram of steps A.

Embodiment

Embodiment 1

Present embodiments provide a kind of intelligent shaft tower connected graph construction method of patrolling and examining trajectory planning for unmanned plane, as shown in Figure 1, it comprises the steps:

A, employing unitary non-linear regression Forecasting Methodology, predict electric power line pole tower distribution, generate some shaft tower circuits, every bar shaft tower circuit covers some shaft towers.As shown in Figure 2, steps A specifically comprises the steps:

A1, dimension-reduction treatment: dimension-reduction treatment is carried out to three-dimensional shaft tower geographic coordinate and is converted to two-dimensional coordinate; Set former A shaft tower three-dimensional coordinate as (x _t, y _t, z _t), x _trepresent shaft tower three dimensions longitude coordinate, y _trepresent shaft tower latitude coordinate, z _trepresent height above sea level residing for shaft tower, then after dimensionality reduction, A shaft tower coordinate is (x _t, y _t); But storage organization is retention bar tower elevation information still, for trajectory planning.

The establishment of A2, regression equation: the one-variable linear regression forecast model formula being applied to transmission line of electricity mission planning is as follows:

${\hat{Y}}_t=a+b{x}_t---\left(1\right)$

X in formula _trepresent t shaft tower longitude coordinate, represent t estimated latitude coordinate;

A3, fetch and return prediction step to be N, obtain the solving of parameter a, b in regression equation as follows:

$\left\{\begin{array}{c}a=\frac{\mathrm{\Σ}{Y}_i}{N}-b\frac{\mathrm{\Σ}{X}_i}{N}\\ b=\frac{\mathrm{N\Σ}{Y}_i{X}_i-\mathrm{\Σ}{Y}_i\mathrm{\Σ}{X}_i}{\mathrm{N\Σ}{X_i}^2-{\left(\mathrm{\Σ}{X}_i\right)}^2}\end{array}\right.---\left(2\right)$

Wherein, n is prediction moving step length; Due to the distance between two base shaft towers at tens meters of rice even up to a hundred not etc., majority of case by continuous many bases are formed with shaft tower can near linear section.According to the shaft tower distribution situation in concrete area, and ensure accuracy and the rapidity of prediction, step-length N can be set to 5 meters-10 meters.

A4, optimum prediction matching, by the determined curve of optimum fit curve determination shaft tower coordinate.Due to except special circumstances, this curve can be approximately straight line in certain distance, therefore have employed Linear regression in the present invention.Curve adopts " least square method ", namely " residual sum of squares (RSS) is minimum " determines linear position, from formula (1), the difference e i that the method for estimation of " least square method-OLS " should meet actual observed value Yi and predicted value is as far as possible the smaller the better, and ei representation formula is as follows:

$min\left({{\mathrm{\Σe}}_i}^2\right)=min{(Y_i-\widehat{{\mathrm{\β}}_1}-\widehat{{\mathrm{\β}}_2}{x}_i)}^2---\left(3\right)$

Utilize Cramer's rule to solve the OLS estimator of observed reading form is:

$\left\{\begin{array}{c}\widehat{{\mathrm{\β}}_1}=\frac{\Σ{X_i}^2\ΣY_i-\ΣX_i\ΣY_i{X}_i}{N\Σ{X_i}^2-{\left(\ΣX_i\right)}^2}\\ \widehat{{\mathrm{\β}}_2}=\frac{N\ΣX_i{Y}_i-\ΣX_i\ΣY_i}{N\Σ{X_i}^2-{\left(\ΣX_i\right)}^2}\end{array}\right.---\left(4\right)$

According to the final straight-line equation of above-mentioned result matching be:

$\stackrel{\‾}{Y}=\widehat{{\mathrm{\β}}_1}+\widehat{{\mathrm{\β}}_2}\stackrel{\‾}{X}---\left(5\right)$

A5, structure forecast interval.There is deviation to a certain degree in the determined curvilinear equation of the shaft tower due to reality and predictive equation, has therefore carried out interval prediction to Y value, namely builds the forecast interval of mean value.According to shaft tower distribution situation, setting level of significance a, the degree of confidence calculating Y mean value is the forecast interval of 1-a.

B, the critical condition utilizing shaft tower to distribute, make some shaft tower circuits be communicated with and form circuit connected graph; Wherein critical condition comprises: scissors crossing situation, many parallel distribution situations of circuit close together, shaft towers turn to situation, shaft tower branch situation;

In this step, there are special circumstances if reach shaft tower distribution and reach critical condition, carrying out critical types judgement, row relax of going forward side by side; The critical kind that shaft tower distributes is divided into following several:

, beyond setting shaft tower number, and there is point of crossing in scissors crossing situation: occur multiple point in forecast interval section;

Many the parallel distribution situations of circuit close together: occur multiple point in forecast interval section, beyond setting shaft tower number, but without point of crossing;

Shaft tower turns to situation: compared with predictive equation, occurs unique flex point in forecast interval;

, in forecast interval, there is multiple flex point in shaft tower branch situation: compared with predictive equation.

The storage organization of C, structure circuit connected graph.Step C specifically comprises the steps:

C1, set up shaft tower matrix, wherein ranks coordinate represents shaft tower number, and matrix data is shaft tower geographical location information; According to predicting the outcome shaft tower is divided into two types: node shaft tower and not a node shaft tower; Its interior joint comprises circuit start-stop shaft tower and intersection shaft tower; Not a node shaft tower is the inside shaft tower only belonging to uniline;

C2, for node shaft tower, build node shaft tower adjacency list;

C3, for not a node shaft tower, carry out matrix structure storage, row matrix coordinate represents affiliated circuit, and row coordinate represents shaft tower number.

In step C2, for node shaft tower, because shaft tower data volume is comparatively large, consider algorithm storage space and efficiency of algorithm, adopt Linked Storage Structure-adjacency list.

Building process for node shaft tower adjacency list is as follows:

C21, to sort by reference direction according to shaft tower geographical position coordinates;

C22, for each point of pole data structure just like properties:

A, subscript: for shaft tower line marker;

B, front abutment points: when node shaft tower is for intersection shaft tower, a upper adjacent line of front abutment points place circuit;

C, rear abutment points: when node shaft tower is for intersection shaft tower, next adjacent line of rear abutment points place circuit.

In step C3, carry out matrix structure storage for not a node shaft tower, row matrix coordinate represents affiliated circuit, and row coordinate represents shaft tower number.Note following situation:

I, whether be a node: head node represents one of end points of a circuit;

II, whether be crossover node: the point value that intersects is " 1 ", and ordinary node is " 0 ";

III, affiliated circuit: ordinary node has and only has an affiliated circuit, and crossover node has many affiliated circuits.

Although the present invention discloses as above with preferred embodiment, and is not used to limit scope of the invention process.Any those of ordinary skill in the art, not departing from invention scope of the present invention, when doing a little improvement, namely every equal improvement done according to the present invention, should be scope of the present invention and contained.

Claims (5)

1. patrol and examine an intelligent shaft tower connected graph construction method for trajectory planning for unmanned plane, it is characterized in that, described connected graph construction method comprises the steps:

A, employing unitary non-linear regression Forecasting Methodology, predict electric power line pole tower distribution, generate some shaft tower circuits, every bar shaft tower circuit covers some shaft towers;

B, the critical condition utilizing shaft tower to distribute, make described some shaft tower circuits be communicated with and form circuit connected graph; Wherein said critical condition comprises: scissors crossing situation, many parallel distribution situations of circuit close together, shaft towers turn to situation, shaft tower branch situation;

C, build the storage organization of described circuit connected graph.

2. connected graph construction method according to claim 1, is characterized in that, described steps A specifically comprises the steps:

A1, dimension-reduction treatment: dimension-reduction treatment is carried out to three-dimensional shaft tower geographic coordinate and is converted to two-dimensional coordinate;

The establishment of A2, regression equation: the one-variable linear regression forecast model formula being applied to transmission line of electricity mission planning is as follows:

${\hat{Y}}_t=a+{\mathrm{bx}}_t$

X in formula _trepresent t shaft tower longitude coordinate, represent t estimated latitude coordinate;

A3, fetch and return prediction step to be N, obtain the solving of parameter a, b in regression equation as follows:

$\left\{\begin{array}{c}a=\frac{{\mathrm{\ΣY}}_i}{N}-b\frac{{\mathrm{\ΣX}}_i}{N}\\ b=\frac{\mathrm{N\Σ}{Y}_i{X}_i-\mathrm{\Σ}{Y}_i\mathrm{\Σ}{X}_i}{\mathrm{N\Σ}{X_i}^2-{\left(\mathrm{\Σ}{X}_i\right)}^2}\end{array}\right.$

Wherein, $\mathrm{\Σ}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}},$ N is prediction moving step length.

3. connected graph construction method according to claim 2, is characterized in that, in described steps A 3, step-length N is 5 meters-10 meters.

4. connected graph construction method according to claim 2, is characterized in that, described steps A comprises steps A 4 further: optimum prediction matching, by the determined curve of optimum fit curve determination shaft tower coordinate.

5. connected graph construction method according to claim 1, is characterized in that, described step C specifically comprises the steps:

C1, set up shaft tower matrix, wherein ranks coordinate represents shaft tower number, and matrix data is shaft tower geographical location information; According to predicting the outcome shaft tower is divided into two types: node shaft tower and not a node shaft tower; Its interior joint comprises circuit start-stop shaft tower and intersection shaft tower; Not a node shaft tower is the inside shaft tower only belonging to uniline;

C2, for node shaft tower, build node shaft tower adjacency list;

C3, for not a node shaft tower, carry out matrix structure storage, row matrix coordinate represents affiliated circuit, and row coordinate represents shaft tower number.