CN106679670A - Unmanned aerial vehicle flight path planning decision-making method based on fusion weighing - Google Patents

Abstract

The invention discloses an unmanned aerial vehicle flight path planning decision-making method based on fusion weighing. The unmanned aerial vehicle flight path planning decision-making method comprises the following steps: firstly inputting a to-be-selected flight path set for unmanned aerial vehicle flight path planning and a corresponding attribute cost matrix; performing objective weighting on the column-normalized attribute cost matrix through a fusion attribute entropy method based on an analytic hierarchy process; then calculating a point correlation coefficient of each of flight paths to the best flight path and a correlation metric matrix and further obtaining a correlation metric ranking; finally outputting the flight path with maximum correlation metric as a decision-making result. Compared with an existing flight path planning decision-making method, the flight path planning decision-making method disclosed by the invention has the benefits that an objective weighting result with smaller difference can be obtained according to a secondary adjusted weight; meanwhile, the subjectivity of weighing is avoided, and the defects of the single attribute decision-making result during final decision-making are overcome; the flight path planning decision-making method is simple and is strong in realizability.

Description

One kind is based on the entitled unmanned vehicle trajectory planning decision method of fusion

Technical field

The present invention relates to unmanned vehicle trajectory planning and multiple attribute decision making (MADM) field, more particularly to it is a kind of based on fusion tax power Unmanned vehicle trajectory planning decision method, it is applied to the flight path decision phase after unmanned vehicle trajectory planning, can be real Now unmanned vehicle carries out the mission planning stage before high-speed flight, to the flight path collection to be selected obtained through offline trajectory planning Close, by Objective Weight, that implicit association carries out flight path decision-making is preferred.

Background technology

Unmanned vehicle trajectory planning is referred in the mobility and aerial mission feature for considering unmanned vehicle Under the premise of, calculate when aerial mission is performed, the position that each moment unmanned vehicle should occur.Unmanned vehicle flight path Planning will not exclusively calculate the safest path of unmanned vehicle, and calculate unmanned vehicle execution task efficiency efficiency Highest flight path.

Eighties of last century fifties, unmanned vehicle is applied to as multiple uses such as target drone, reconnaissance planes.With appoint The complexity of business grows with each passing day, and the importance that unmanned vehicle task grouping is set up increasingly is highlighted, and trajectory planning is one The new and high technology that door grows up along with modern information technologies.

Unmanned vehicle trajectory planning process is divided into segregation reasons with online planning.Unmanned vehicle perform task it Before, according to the priori of task scene, by Path Planning, carry out segregation reasons.Segregation reasons process is generally adopted Distributed Calculation treatment technology, does not do excessive requirement to the time complexity of algorithm.Online planning is referred in unmanned vehicle During the task of carrying out, by loading offline flight path result, according to planning track flight in advance, take further according to unmanned vehicle The remote sensing remote-measuring equipment of load carries out real-time scene exploration, and to newfound threat etc. real-time trajectory planning is carried out.

The flight path scheme of multiple unmanned vehicle flights, flight path referred to as to be selected are commonly available by Path Planning Collection.From the flight path set to be selected of unmanned vehicle, the flight track for preferably going out optimum is exactly the final step of trajectory planning, i.e., The planning of decision level.At present conventional unmanned vehicle flight path decision method has integrate-cost method and implicit association method.The two Common ground is all to be weighted decision-making according to the weights of every attribute, thus weights adding method is exactly a key in this Technology.The most frequently used tax power method is expertise assignment method, and this method possesses the shortcomings of excessively subjectivity, tax are weighed not in time, And the Objective Weighting such as conventional attribute Information Entropy, deviation maximization method is weighed when having excessive to community-internal diversity Value gives too high shortcoming.

The content of the invention

For weak point present in the problems referred to above, the present invention is a kind of based on the entitled unmanned flight of fusion by providing Device trajectory planning decision method, in the final stage of trajectory planning, by merging attribute Information Entropy based on step analysis, carries out visitor The power of tax is seen, then final flight path decision-making is carried out by implicit association method, and then realized and avoid single attribute from determining the result of decision Technique effect.

For achieving the above object, the present invention provides a kind of based on the entitled unmanned vehicle trajectory planning decision-making party of fusion Method, including：

Step 1, parameter input：The flight path set to be selected of input unmanned vehicle trajectory planning and corresponding attribute cost square Battle array, and row standardization is done to attribute cost matrix；

Step 2, Objective Weight：Each Column Properties entropy is calculated to arranging normalized attribute cost matrix according to attribute Information Entropy Value, by normalization the weight of first time Objective Weight is obtained；According to analytic hierarchy process (AHP), the diversity system between each attribute is calculated Number compares matrix；Further according to nonlinear transformation, the importance comparator matrix between computation attribute；Finally by regular decomposition, obtain most The corresponding standard feature of eigenvalue greatly is vectorial, used as the objective weight result of calculation after secondary adjustment；

Step 3, implicit association：The point coefficient of association of each bar flight path and optimal trajectory is calculated according to implicit association method, side by side Sequence takes the flight path of correlation maximum；

Step 4, result output：The result of output flight path decision-making.

As a further improvement on the present invention, the step 1 includes：

The flight path set Path to be selected that step 11, the offline trajectory planning of input unmanned vehicle are obtained；

Path={ (X_S,…,X_i,…,X_E)|X_i∈G_M×N} (1)

In formula, X_i=[x_i,y_i,z_i]^TFor the coordinate of track points, X_SFor the beginning coordinate of track points, X_EFor the knot of track points Beam coordinate, G_M×NFor the set of all track points in trajectory planning scene domain；

In step 12, the flight path set to be selected of input unmanned vehicle, the category of the corresponding V attribute composition of U bars flight path to be selected Property cost matrix P；

In formula, p_uvFor the property value of v-th attribute of the u article flight path to be selected；

Step 13, to attribute cost matrix do row standardization, i.e., to each property value in attribute cost matrix P as the following formula Processed；

Obtain the dimensionless attribute cost matrix after standardization in property value

As a further improvement on the present invention, the step 2 includes：

Step 21, to dimensionless attribute cost matrixMake row normalization, make each flight path property value meet pseudo- probability point The requirement of cloth, i.e.,：

Calculate the attribute entropy E of each attribute in normalized attribute cost matrix_v；

Calculate the weight of first time Objective Weight

Step 22, important ratio is carried out to each attribute compared with the larger attribute of deviation is more attached most importance to compared with the less attribute of deviation Will, therefore according to the weight of first time Objective Weight, diversity coefficient ratio matrix D is calculated to each attribute：

In formula, d_ijFor difference property coefficient of the ith attribute to j-th attribute, and d_ij·d_ji=1；If d_ij＞ 1, then certainly In plan, ith attribute is than j-th Importance of attribute；

Step 23, diversity coefficient ratio is mapped in 1 to 9 mark, obtain the importance comparator matrix R of attribute, square Each element is calculated as follows in battle array：

Wherein, a is regulation coefficient, represents the maximum scores mark when diversity coefficient ratio maps；If d_ij＜ 9, then a take Closest to d_ijInteger, otherwise a=9；

Step 24, regular decomposition is carried out to importance comparator matrix R, seek eigen vector；

In formula, λ is the eigenvalue of matrix, and unitary matrice U is made up of the normal orthogonal characteristic vector of matrix R；Take matrix maximum Eigenvalue λ_maxCorresponding standard feature vector is objective after the calculated secondary adjustment of blending algorithm based on step analysis Weight

As a further improvement on the present invention, the step 3 includes：

Step 31, setting Desired Track scheme P^*Property value be each row in attribute cost matrix to be selected maximum；

Using Desired Track scheme as reference scheme, by all schemes in flight path set to be selected alternately, meter The point incidence coefficient matrix Ξ of each attribute in each attribute and reference scheme in flight path scheme is compared in calculation；

In formula, resolution ratio ρ ∈ [0,1]；

According to objective weight w after secondary adjustment^*, to a coefficient of association weighting, solve the association of trajectory planning decision level Degree metric matrix R^*；

Step 32, according to degree of association metric matrix R^*, obtain comprehensive phase of each optional program relative to desired reference scheme Like property tolerance, according to the degree of association ranking of each scheme, optimum unmanned vehicle flight path is chosen as the final flight boat for adopting Mark scheme.

Compared with prior art, beneficial effects of the present invention are：

When the 1st, overcoming conventional Objective Weighting diversity is larger in single attribute, calculated weights are assigned Give it is excessive, thus result in single attribute determine the result of decision problem；

2nd, without the need for consistency check, decision making process is simple, easily realization for weighed value adjusting method.

Description of the drawings

Fig. 1 is that an embodiment of the present invention is disclosed based on the entitled unmanned vehicle trajectory planning decision method of fusion Flow chart；

Fig. 2 is the result of decision figure of the calculating example in the present invention.

Specific embodiment

To make purpose, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is A part of embodiment of the present invention, rather than the embodiment of whole.Based on the embodiment in the present invention, ordinary skill people The every other embodiment that member is obtained on the premise of creative work is not made, belongs to the scope of protection of the invention.

The present invention solves conventional unmanned vehicle and determines by providing a kind of unmanned vehicle trajectory planning decision method In plan method, weights give subjectivityization, and weights give diversity greatly, cause single attribute to determine the problem of the result of decision.One After secondary Objective Weight, nonlinear adjustment, the variance of increase point coefficient of association distribution, Optimal Decision-making result are carried out.

Technical scheme in the present invention is the technical problem for solving above-mentioned unmanned vehicle trajectory planning decision method, overall Thinking is as follows：

First, input is included after unmanned vehicle Path Planning, the set of all flight paths for obtaining and attribute Cost matrix；Then, the attribute cost to being input into carries out the standardization in attribute, and the standardization attribute to obtaining calculates respectively category Property entropy, renormalization is used as first time Objective Weight result；Then, the difference between each attribute is calculated by using analytic hierarchy process (AHP) Different in nature coefficient ratio matrix, the importance comparator matrix between further computation attribute, by using feature vector method to important ratio Regular decomposition is carried out compared with matrix, the corresponding standard feature vector of the eigenvalue of maximum for obtaining is exactly the Objective Weight knot after adjustment Really；Using implicit association method, each bar flight path is calculated respectively under adjusted Objective Weight result, final relativity measurement Ranking；Finally, output possesses the maximum flight path of a coefficient of association as final decision result.

In order to more fully understand above-mentioned technical proposal, below in conjunction with Figure of description, and specific embodiment pair Above-mentioned technical scheme is described in detail.

As shown in figure 1, the present invention provides a kind of based on the entitled unmanned vehicle trajectory planning decision method of fusion, bag Include：

Step 1, parameter input：Flight path set all to be selected and treat that the offline trajectory planning of input unmanned vehicle is obtained The attribute cost matrix for selecting each property value of each bar flight path in flight path set to constitute, and row specification is done to attribute cost matrix Change；Wherein specifically include：

The flight path set Path to be selected that step 11, the offline trajectory planning of input unmanned vehicle are obtained；

Path={ (X_S,…,X_i,…,X_E)|X_i∈G_M×N} (1)

In formula, X_i=[x_i,y_i,z_i]^TFor the coordinate of track points, X_SFor the beginning coordinate of track points, X_EFor the knot of track points Beam coordinate, G_M×NFor the set of all track points in trajectory planning scene domain；

In step 12, the flight path set to be selected of input unmanned vehicle, the category of the corresponding V attribute composition of U bars flight path to be selected Property cost matrix P；

In formula, p_uvFor the property value of v-th attribute of the u article flight path to be selected；

Step 13, to attribute cost matrix do row standardization, i.e., to each property value in attribute cost matrix P as the following formula Processed；

Obtain the dimensionless attribute cost matrix after standardization in property value

Step 2, Objective Weight：According to Objective Weighting (such as attribute Information Entropy) to arranging normalized attribute cost matrix Each Column Properties entropy is calculated, the weight of first time Objective Weight is obtained by normalization；According to analytic hierarchy process (AHP), each category is calculated Diversity coefficient ratio matrix between property；Further according to nonlinear transformation, the importance comparator matrix between computation attribute；Finally by just Rule are decomposed, and the corresponding standard feature vector of eigenvalue of maximum are obtained, as the objective weight result of calculation after secondary adjustment；Its tool Body includes：

Step 21, to dimensionless attribute cost matrixMake row normalization, make each flight path property value meet pseudo- probability point The requirement of cloth, i.e.,：

Calculate the attribute entropy E of each attribute in normalized attribute cost matrix_v；

Because attribute entropy characterizes the uncertainty of distribution, when probability distribution is to be uniformly distributed, attribute entropy has maximum； In decision level Objective Weight, bigger weights are given by the bigger attribute of deviation, attribute weight is with attribute entropy into negative correlation, meter Calculate the weight of first time Objective Weight

Step 22, important ratio is carried out to each attribute compared with the larger attribute of deviation is more attached most importance to compared with the less attribute of deviation Will, therefore according to the weight of first time Objective Weight, diversity coefficient ratio matrix D is calculated to each attribute：

In formula, d_ijFor difference property coefficient of the ith attribute to j-th attribute, and there is d_ij·d_ji=1 the characteristics of；If d_ij＞ 1, then in decision-making, ith attribute is than j-th Importance of attribute；

Step 23, diversity coefficient ratio is mapped in 1 to 9 mark, obtain the importance comparator matrix R of attribute, square Each element is calculated as follows in battle array：

Wherein, a is regulation coefficient, represents the maximum scores mark when diversity coefficient ratio maps；If d_ij＜ 9, then a take Closest to d_ijInteger, otherwise a=9；

Step 24, regular decomposition is carried out to importance comparator matrix R using feature vector method, ask eigenvalue and feature to Amount；

In formula, λ is the eigenvalue of matrix, and unitary matrice U is made up of the normal orthogonal characteristic vector of matrix R；Take matrix maximum Eigenvalue λ_maxCorresponding standard feature vector, the vector is namely based on the calculated secondary tune of blending algorithm of step analysis Objective weight after whole

Step 3, implicit association：According to each bar flight path under objective weight of the implicit association method calculating after secondary adjustment and most The point coefficient of association of excellent flight path, by a coefficient of association calculating correlation metric matrix, and sequence takes the flight path of correlation maximum； It is specifically included：

Step 31, setting Desired Track scheme P^*Property value be each row in attribute cost matrix to be selected maximum；

Using Desired Track scheme as reference scheme, by all schemes in flight path set to be selected alternately, meter The point incidence coefficient matrix Ξ of each attribute in each attribute and reference scheme in flight path scheme is compared in calculation；

In formula, resolution ratio ρ ∈ [0,1] typically takes ρ=0.5；

According to objective weight w after secondary adjustment^*, to a coefficient of association weighting, solve the association of trajectory planning decision level Degree metric matrix R^*；

Step 32, according to degree of association metric matrix R^*, obtain comprehensive phase of each optional program relative to desired reference scheme Like property tolerance, according to the degree of association ranking of each scheme, optimum unmanned vehicle flight path is chosen as the final flight boat for adopting Mark scheme.

Step 4, result output：It is ranked up by degree of association metric matrix, obtains the flight path with maximum point coefficient of association, The result of output flight path decision-making.

Embodiment 1：

Step 1, parameter input：

A the flight path collection to be selected of () input is combined into 10 flight path point sets to be selected；

B the dimension of the attribute cost matrix of () input is 10 × 4：

C () data normalization, obtains normalized attribute cost matrix dimension for 10 × 4：

Step 2, Objective Weight；

(a) attribute cost matrix row normalization, computation attribute entropy, obtain attribute entropy vector E=[0.9382,0.6699, 0.580]^T4,

The weight of first time Objective Weight is

B () calculates diversity coefficient ratio matrix, obtain

C () calculates importance comparator matrix, obtain

D () regular decomposition is obtained, importance comparator matrix eigenvalue of maximum λ_max=3.2263；

Weights after adjustment are w^*=[0.0694,0.2777,0.3467,0.3063]^T。

Step 3, implicit association：

A () Desired Track scheme is P^*=[1,1,1,1,1]^T,

Obtain an incidence coefficient matrix

And degree of association metric matrix

R^*=(r^*)_10×1=[0.3816,0.3810,9317,0.3593,0.4407,0.3688,0.3654,0.9193, 0.3646,0.3362]^T；

B () obtains relational degree taxis, most relevance degree tolerance is 0.9317, and correspondence flight path attribute cost vector is

P=[284.7748,2.0728,1.4341,30.8548]^T。

Step 4：As a result export：

Output most relevance degree measures corresponding flight path, and the flight path in the case where topography is threatened is as shown in Figure 2.

The present invention provides a kind of based on the entitled unmanned vehicle trajectory planning decision method of fusion, nobody is input into first and is flown The flight path set all to be selected of row device and corresponding attribute cost matrix；To arranging normalized attribute cost matrix, using attribute Information Entropy carries out Objective Weight, and then objective weight-values are calculated with diversity coefficient ratio matrix, then calculates weight by nonlinear mapping The property wanted comparator matrix, by carrying out regular decomposition to importance comparator matrix, obtain the corresponding standard feature of eigenvalue of maximum to Amount, as the Objective Weight result of secondary adjustment；Then point coefficient of association and the degree of association of each flight path to flight path optimization is calculated Metric matrix, and then obtain degree of association tolerance ranking；Finally, flight path of the output with most relevance degree tolerance is tied as decision-making Really.Compared to conventional trajectory planning decision method, the less Objective Weight of diversity can be obtained according to secondary adjustment weights As a result, while when overcoming conventional Objective Weighting diversity is larger in single attribute, calculated weights are assigned Give it is excessive, thus result in single attribute determine the result of decision problem；The present invention weighed value adjusting method without the need for consistency check, Decision making process is simple, easily realization.

The preferred embodiments of the present invention are these are only, the present invention is not limited to, for those skilled in the art For member, the present invention can have various modifications and variations.All any modifications within the spirit and principles in the present invention, made, Equivalent, improvement etc., should be included within the scope of the present invention.

Claims (4)

1. it is a kind of based on the entitled unmanned vehicle trajectory planning decision method of fusion, it is characterised in that to include：

Step 1, parameter input：The flight path set to be selected of input unmanned vehicle trajectory planning and corresponding attribute cost matrix, And to attribute cost matrix do row standardization；

Step 2, Objective Weight：Each Column Properties entropy is calculated to arranging normalized attribute cost matrix according to attribute Information Entropy, is led to Cross the weight that normalization obtains first time Objective Weight；According to analytic hierarchy process (AHP), the diversity coefficient ratio between each attribute is calculated Matrix；Further according to nonlinear transformation, the importance comparator matrix between computation attribute；Finally by regular decomposition, maximum spy is obtained The corresponding standard feature vector of value indicative, as the objective weight result of calculation after secondary adjustment；

Step 3, implicit association：The point coefficient of association of each bar flight path and optimal trajectory is calculated according to implicit association method, and sequence takes The flight path of correlation maximum；

Step 4, result output：The result of output flight path decision-making.

2. it is as claimed in claim 1 based on the entitled unmanned vehicle trajectory planning decision method of fusion, it is characterised in that institute Stating step 1 includes：

The flight path set Path to be selected that step 11, the offline trajectory planning of input unmanned vehicle are obtained；

Path={ (X_S,…,X_i,…,X_E)|X_i∈G_M×N} (1)

In formula, X_i=[x_i,y_i,z_i]^TFor the coordinate of track points, X_SFor the beginning coordinate of track points, X_EEnd for track points is sat Mark, G_M×NFor the set of all track points in trajectory planning scene domain；

In step 12, the flight path set to be selected of input unmanned vehicle, the attribute generation of the corresponding V attribute composition of U bars flight path to be selected Valency matrix P；

In formula, p_uvFor the property value of v-th attribute of the u article flight path to be selected；

Step 13, to attribute cost matrix do row standardization, i.e., each property value in attribute cost matrix P is carried out as the following formula Process；

${\hat{p}}_{uv}=\frac{\underset{1\≤k\≤U}{\max }\left(p_{kv}\right)-p_{uv}}{\underset{1\≤k\≤U}{\max }\left(p_{kv}\right)-\underset{1\≤k\≤U}{\min }\left(p_{kv}\right)}---\left(3\right)$

Obtain the dimensionless attribute cost matrix after standardization in property value

3. it is as claimed in claim 2 based on the entitled unmanned vehicle trajectory planning decision method of fusion, it is characterised in that institute Stating step 2 includes：

Step 21, to dimensionless attribute cost matrixMake row normalization, make each flight path property value meet wanting for pseudo- probability distribution Ask, i.e.,：

${\hat{p}}_{uv}=\frac{{\hat{p}}_{uv}}{\underset{u=1}{\overset{U}{\Σ}}{\hat{p}}_{uv}},v=1,2,...,V---\left(5\right)$

Calculate the attribute entropy E of each attribute in normalized attribute cost matrix_v；

$E_v=-\frac{1}{{\log }_{2}U}\underset{u=1}{\overset{U}{\Σ}}{\hat{p}}_{uv}{\log }_2{\hat{p}}_{uv}---\left(6\right)$

Calculate the weight of first time Objective Weight

${\hat{w}}_v=\frac{1-E_v}{\underset{v=1}{\overset{V}{\Σ}}1-E_v},v=1,2,...,V---\left(7\right)$

Step 22, each attribute is carried out important ratio compared with, the larger attribute of deviation is even more important compared with the less attribute of deviation, Therefore according to the weight of first time Objective Weight, diversity coefficient ratio matrix D is calculated to each attribute：

In formula, d_ijFor difference property coefficient of the ith attribute to j-th attribute, and d_ij·d_ji=1；If d_ij＞ 1, then in decision-making In, ith attribute is than j-th Importance of attribute；

Step 23, diversity coefficient ratio is mapped in 1 to 9 mark, the importance comparator matrix R of attribute is obtained, in matrix Each element is calculated as follows：

$R={\left(r_{ij}\right)}_{V\×V}={\left(\sqrt[a]{\frac{d_{ij}}{a}}\right)}_{V\×V}---\left(9\right)$

Wherein, a is regulation coefficient, represents the maximum scores mark when diversity coefficient ratio maps；If d_ij＜ 9, then a take most Close to d_ijInteger, otherwise a=9；

Step 24, regular decomposition is carried out to importance comparator matrix R, seek eigen vector；

In formula, λ is the eigenvalue of matrix, and unitary matrice U is made up of the normal orthogonal characteristic vector of matrix R；Take matrix maximum feature Value λ_maxCorresponding standard feature vector is the objective weight after the calculated secondary adjustment of blending algorithm based on step analysis

4. it is as claimed in claim 3 based on the entitled unmanned vehicle trajectory planning decision method of fusion, it is characterised in that institute Stating step 3 includes：

Step 31, setting Desired Track scheme P^*Property value be each row in attribute cost matrix to be selected maximum；

$P^{*}={\[p_1^{*},p_2^{*},...,p_v^{*}\]}^T---\left(11\right)$

$p_v^{*}=max(p_{1v},p_{2v},...,p_{Uv}),v=1,2,...,V---\left(12\right)$

Using Desired Track scheme as reference scheme, by all schemes in flight path set to be selected alternately, ratio is calculated Compared with the point incidence coefficient matrix of each attribute in each attribute in flight path scheme and reference scheme

$\mathrm{\Ξ}={\left({\mathrm{\ξ}}_{uv}\right)}_{U\×V}={\left(\frac{\underset{v}{min}\underset{u}{min}|p_v^{*}-p_{uv}|+\mathrm{\ρ}\underset{v}{\max }\underset{u}{\max }|p_v^{*}-p_{uv}|}{|p_v^{*}-p_{uv}|+\mathrm{\ρ}\underset{v}{\max }\underset{u}{\max }|p_v^{*}-p_{uv}|}\right)}_{U\×V}---\left(13\right)$

In formula, resolution ratio ρ ∈ [0,1]；

According to objective weight w after secondary adjustment^*, to a coefficient of association weighting, solve the degree of association degree of trajectory planning decision level Moment matrix R^*；

Step 32, according to degree of association metric matrix R^*, obtain comprehensive similarity of each optional program relative to desired reference scheme Tolerance, according to the degree of association ranking of each scheme, chooses optimum unmanned vehicle flight path as the final flight track side for adopting Case.