CN117330085B - Unmanned aerial vehicle path planning method based on non-line-of-sight factors - Google Patents

Unmanned aerial vehicle path planning method based on non-line-of-sight factors Download PDF

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CN117330085B
CN117330085B CN202311633708.6A CN202311633708A CN117330085B CN 117330085 B CN117330085 B CN 117330085B CN 202311633708 A CN202311633708 A CN 202311633708A CN 117330085 B CN117330085 B CN 117330085B
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sight
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aerial vehicle
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敖洋钒
刘云平
潘慧婷
牛天宇
周朝杰
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Nanjing University of Information Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
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Abstract

The invention discloses an unmanned aerial vehicle path planning method based on non-line-of-sight factors, which comprises the following steps: (1) Introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and constructing a target optimization function by combining path length constraint; (2) Performing iterative optimization on the objective function through an improved particle swarm algorithm, and calculating to finally obtain an unmanned aerial vehicle path with minimum non-line-of-sight error; according to the invention, a chaotic strategy is introduced, so that the defect that a particle swarm algorithm is easy to trap into local optimum is effectively solved, and the global searching capability is enlarged; and introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and combining path length constraint and obstacle avoidance constraint, so that the planned path is shorter on the basis of ensuring that the non-line-of-sight error is as small as possible, positioning accuracy is improved, and excellent range is ensured.

Description

Unmanned aerial vehicle path planning method based on non-line-of-sight factors
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method based on non-line-of-sight factors.
Background
The traditional three-dimensional path planning of the unmanned aerial vehicle generally considers optimizing the minimum flight energy consumption, the shortest path or the shortest time, and the like, but the unmanned aerial vehicle is always in a refusing environment due to the problems of terrain limitation, satellite signal shielding, and the like in the urban combat environment. The wireless positioning is used as an unmanned aerial vehicle positioning mode under the refusing environment, can provide more accurate position estimation for the unmanned aerial vehicle under the full-view-range condition, and shows poorer positioning effect under the condition of a large number of non-view-range conditions. If the influence of non-line-of-sight factors is considered when the unmanned aerial vehicle plans the flight path, the problems of large positioning error, even positioning failure and the like of the unmanned aerial vehicle can be effectively relieved.
The unmanned aerial vehicle path planning method disclosed in patent number CN202011147730.6 adds the requirements of energy constraint, video transmission quality and the like of the unmanned aerial vehicle into path planning, optimizes the total energy loss of the unmanned aerial vehicle, reduces the total flight distance and total take-off times of the unmanned aerial vehicle, and improves the accuracy and the practicability of unmanned aerial vehicle path planning. However, the unmanned aerial vehicle positioning effect under satellite rejection is not considered, and the influence of non-line-of-sight on wireless positioning is not optimized.
An indoor three-dimensional positioning method based on non-line-of-sight error suppression is disclosed in patent number CN 201810353498.8. According to the method, the non-line-of-sight error is analyzed, the positioning problem is converted into a non-linear constraint problem, the non-line-of-sight error is estimated, the non-line-of-sight problem is converted into the line-of-sight problem, and finally the accurate three-dimensional coordinate is obtained by combining the linear least square estimation. But this method compensates the position estimate based on the error model only and is not considered from the point of view of reducing non-line-of-sight situations.
Disclosure of Invention
The invention aims to: the invention aims to provide an unmanned aerial vehicle path planning method based on non-line-of-sight factors, which is based on the construction of a non-line-of-sight error model, and utilizes an improved particle swarm algorithm to iteratively solve an optimal unmanned aerial vehicle flight path, so that the non-line-of-sight error on the path is minimized, and the wireless positioning precision of an unmanned aerial vehicle is improved.
The technical scheme is as follows: the invention discloses an unmanned aerial vehicle path planning method based on non-line-of-sight factors, which comprises the following steps:
(1) Introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and constructing a target optimization function by combining path length constraint;
(2) Performing iterative optimization on the objective function through an improved particle swarm algorithm, and calculating to obtain an unmanned aerial vehicle path with the minimum non-line-of-sight error; the method comprises the following steps:
(21) Population initialization of a particle swarm algorithm is improved by using Tent mapping;
(22) And (5) improving a partial particle updating mode of the particle swarm algorithm by using the Tent mapping.
Further, the step (1) includes the following steps:
(11) Constructing a non-line-of-sight deviation model;
(12) Establishing a path length constraint condition;
(13) And establishing a target optimization function.
Further, the formula of the step (11) is as follows:
；
wherein,the relative dielectric constant of the wall is 3.0-9.0; />The thickness of the wall body is 0.25-0.75 m.
Further, the path length constraint condition formula in the step (12) is as follows:
；
wherein,，/>，/>the components of the path in x, y, z, respectively.
Further, the objective optimization function formula in the step (13) is as follows:
；
wherein,，/>，/>respectively are roadsComponents in x, y, z; />Representing non-line-of-sight deviation term coefficients; />Is the angle of refraction.
Further, the step (21) specifically includes the following steps: mapping the randomly generated particle positions to [0,1] according to formula (4); then carrying out three iterations according to the formula (5) to obtain three chaotic points; finally, mapping the chaotic points back to the original space respectively according to a formula (6), and selecting the chaotic point with the optimal fitness value as a new position of the particle; the specific formula is as follows:
；
；
；
wherein,representing the normalized position; />Representing a result obtained by chaotic mapping; />Representing the position after mapping back to the original space; />Representing the dimension of a single particle; />A definition field representing a j-th dimension variable;is a chaotic parameter.
Further, the step (22) specifically includes the following steps: in the particle iterative updating process, firstly, evaluating the fitness value of an initialized particle and selecting partial particles with better fitness, and if the current fitness of the partial particles is higher than the historical optimal fitness value, re-acquiring a new position of the particle according to the Tent mapping; if the position velocity is lower than the historical optimal fitness value, updating the position velocity according to the formula (7) (8) together with the rest particles; the specific formula is as follows:
（7）；
（8）；
wherein,and->Represents the velocity and position of the i (i=1, 2,) th particle in the k (k=1, 2,) th, N iterations, respectively; w is an inertial weight representing the extent of influence of the current speed of the particle; c (C) 1 ，C 2 Is an acceleration coefficient, and takes the value as a constant; r is R 1 ，R 2 Is a random number between (0, 1); />And->Representing the individual optimal solution and the global optimal solution reached by particle i at iteration k times, respectively.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: the chaos strategy is introduced, so that the defect that a particle swarm algorithm is easy to fall into local optimum is effectively solved, and the global searching capability is enlarged; and introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and combining path length constraint and obstacle avoidance constraint, so that the planned path is shorter on the basis of ensuring that the non-line-of-sight error is as small as possible, positioning accuracy is improved, and excellent range is ensured.
Drawings
FIG. 1 is a flow chart of the present invention.
Description of the embodiments
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a non-line-of-sight factor-based unmanned aerial vehicle path planning method, which includes the following steps:
(1) Introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and constructing a target optimization function by combining path length constraint; the method comprises the following steps:
(11) Constructing a non-line-of-sight deviation model; the formula is as follows:
；
wherein,the relative dielectric constant of the wall is 3.0-9.0; />The thickness of the wall body is 0.25-0.75 m.
(12) Establishing a path length constraint condition; the path length constraint formula is as follows:
；
wherein,，/>，/>respectively are pathsComponents in x, y, z.
(13) And establishing a target optimization function. The formula is as follows:
；
wherein,，/>，/>components of the path in x, y, z, respectively; />Representing non-line-of-sight deviation term coefficients;is the angle of refraction.
(2) Performing iterative optimization on the objective function through an improved particle swarm algorithm, and calculating to obtain an unmanned aerial vehicle path with the minimum non-line-of-sight error; the method comprises the following steps:
(21) Population initialization of a particle swarm algorithm is improved by using Tent mapping; the method comprises the following steps: mapping the randomly generated particle positions to [0,1] according to formula (4); then carrying out three iterations according to the formula (5) to obtain three chaotic points; finally, mapping the chaotic points back to the original space respectively according to a formula (6), and selecting the chaotic point with the optimal fitness value as a new position of the particle; the specific formula is as follows:
；
；
；
wherein,representing the normalized position; />Representing a result obtained by chaotic mapping; />Representing the position after mapping back to the original space; />Representing the dimension of a single particle; />A definition field representing a j-th dimension variable;is a chaotic parameter.
(22) And (5) improving a partial particle updating mode of the particle swarm algorithm by using the Tent mapping. The method comprises the following steps: in the particle iterative updating process, firstly, evaluating the fitness value of an initialized particle and selecting partial particles with better fitness, and if the current fitness of the partial particles is higher than the historical optimal fitness value, re-acquiring a new position of the particle according to the Tent mapping; if the position velocity is lower than the historical optimal fitness value, updating the position velocity according to the formula (7) (8) together with the rest particles; the specific formula is as follows:
（7）；
（8）；
wherein,and->Represents the velocity and position of the i (i=1, 2,) th particle in the k (k=1, 2,) th, N iterations, respectively; w is an inertial weight representing the extent of influence of the current speed of the particle; c (C) 1 ，C 2 Is an acceleration coefficient, and takes the value as a constant; r is R 1 ，R 2 Is a random number between (0, 1); />And->Representing the individual optimal solution and the global optimal solution reached by particle i at iteration k times, respectively.
The invention selects the 7 common reference test functions of Sphere, step, quartic, alpine, ackley, six-Hump Camel-Back, goldstein-Price to verify the basic PSO algorithm, SPSO algorithm, GWO algorithm, WOA algorithm and improve the optimizing performance of the PSO algorithm. Wherein,as a unimodal function>As a function of the multiple peaks,is a fixed dimension multimodal function. Specific information of the benchmark function is shown in table 1. The particle count M is set to 80, the iteration number N is 500, the test number K is 30, and the optimizing results of the five algorithms for each reference test function are shown in table 2.
TABLE 1 benchmark test function
Table 2 results of each algorithm test comparison
Table 2 reflects the average value and success rate of the improved PSO algorithm of the present invention and other optimization algorithms for 30 tests on different test functions. As can be seen from table 2, the improved PSO algorithm has improved convergence accuracy compared with other optimization algorithms. In particular, for the unimodal function, the average value obtained by improving the PSO algorithm is better than that of PSO and SPSO, and the convergence effect in the PSO algorithm is slightly inferior to that of WOA and GWO, but the PSO algorithm approaches to higher convergence accuracy in the PSO algorithm and has higher success rate. For the multimodal function, the optimizing result of the improved PSO algorithm is superior to other algorithms, and is improved by 2-4 orders of magnitude, so that the effectiveness of the improved strategy is proved. In the test of the multimodal function, the SPSO and GWO perform more closely to the theoretical optimal value than the improved PSO algorithm, but compared with the PSO and WOA, the method has a larger improvement on optimizing precision. For a fixed-dimension multimodal function, the improved PSO algorithm also found the theoretical optimum in multiple tests.
Table 3 shows the comparison of non-line-of-sight points and non-line-of-sight deviations before and after improvement according to the present invention; as can be seen from the experimental data in Table 3, the improved particle swarm algorithm in different scenes has a good optimizing effect, and the performance of the unmanned aerial vehicle path on non-line-of-sight points and deviation models is greatly improved. The number of non-line-of-sight points on the unmanned plane path in the sparse scene is reduced to 4, and the Error value of the deviation model is 2.292, and is reduced by 86.75%. The number of non-line-of-sight points on the unmanned plane path in a general scene is reduced to 4, and the Error value of the deviation model is 2.819, and is reduced by 89.28%. The number of non-line-of-sight points on the unmanned plane path in the dense scene is reduced to 3, and the Error value of the deviation model is 2.475 and is reduced by 93.05%.

Claims (5)

1. The unmanned aerial vehicle path planning method based on the non-line-of-sight factors is characterized by comprising the following steps of:
(1) Introducing non-line-of-sight factors, constructing a non-line-of-sight deviation model, and constructing a target optimization function by combining path length constraint; the step (1) comprises the following steps:
(11) Constructing a non-line-of-sight deviation model;
(12) Establishing a path length constraint condition;
(13) Establishing a target optimization function; the formula is as follows:
；
wherein,，/>，/>components of the path in x, y, z, respectively; />Representing non-line-of-sight deviation term coefficients; />Is the angle of refraction; />The relative dielectric constant of the wall is 3.0-9.0; />The wall thickness is 0.25-0.75 m;
(2) Performing iterative optimization on the objective function through an improved particle swarm algorithm, and calculating to obtain an unmanned aerial vehicle path with the minimum non-line-of-sight error; the method comprises the following steps:
(21) Population initialization of a particle swarm algorithm is improved by using Tent mapping;
(22) And (5) improving a partial particle updating mode of the particle swarm algorithm by using the Tent mapping.
2. The unmanned aerial vehicle path planning method of claim 1, wherein the formula of step (11) is as follows:
；
wherein,the relative dielectric constant of the wall is 3.0-9.0; />The wall thickness is 0.25-0.75 m.
3. The unmanned aerial vehicle path planning method of claim 2, wherein the path length constraint equation of step (12) is as follows:
；
wherein,，/>，/>the components of the path in x, y, z, respectively.
4. The unmanned aerial vehicle path planning method according to claim 1, wherein the step (21) is specifically as follows: mapping the randomly generated particle positions to [0,1] according to formula (4); then carrying out three iterations according to the formula (5) to obtain three chaotic points; finally, mapping the chaotic points back to the original space respectively according to a formula (6), and selecting the chaotic point with the optimal fitness value as a new position of the particle; the specific formula is as follows:
；
；
；
wherein,representing the normalized position; />Representing a result obtained by chaotic mapping; />Representing the position after mapping back to the original space; />Representing the dimension of a single particle; />A definition field representing a j-th dimension variable;is a chaotic parameter.
5. The unmanned aerial vehicle path planning method according to claim 1, wherein the step (22) is specifically as follows: in the particle iterative updating process, firstly, evaluating the fitness value of an initialized particle and selecting partial particles with better fitness, and if the current fitness of the partial particles is higher than the historical optimal fitness value, re-acquiring a new position of the particle according to the Tent mapping; if the position velocity is lower than the historical optimal fitness value, updating the position velocity according to the formula (7) (8) together with the rest particles; the specific formula is as follows:
（7）；
（8）；
wherein,and->Representing the velocity and position of the ith particle in the kth iteration, respectively; w is an inertial weight representing the extent of influence of the current speed of the particle; c (C) 1 ，C 2 Is an acceleration coefficient, and takes the value as a constant; r is R 1 ，R 2 Is a random number between (0, 1);and->Respectively representing an individual optimal solution and a global optimal solution which are reached by the particle i in iterating k times; where k=1, 2, N; i=1, 2,..m.
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