CN116772880B - Unmanned aerial vehicle path planning method based on unmanned aerial vehicle vision - Google Patents
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Abstract
The invention provides an unmanned vehicle path planning method based on unmanned aerial vehicle vision, which comprises the following steps of: establishing a grid map of a ground environment, acquiring the grid map established by the unmanned aerial vehicle, and correcting and supplementing the grid map by combining with the environmental information perceived by the unmanned aerial vehicle; according to the corrected and supplemented grid map, establishing an objective function of unmanned vehicle path planning taking the shortest moving path, the shortest time consumption or the least energy consumption as constraint; according to the objective function, updating the optimal position through an improved Wuyangull optimization algorithm, and determining the optimal position of the Wuyangull; and determining an optimal path planning result according to the optimal Uighur positions which are updated in sequence according to the preset maximum iteration times. Wherein, the improved Wuyangull optimization algorithm introduces a solution enhancement mechanism of the Longgy-Kutta optimization algorithm to comprehensively consider the optimal position of the Wuyangull. The method overcomes the defects of the existing path planning method, and can remarkably improve the effect of unmanned vehicle path planning.
Description
Technical Field
The invention relates to the technical field of unmanned vehicles, in particular to an unmanned vehicle path planning method based on unmanned aerial vehicle vision.
Background
An air-ground heterogeneous robot system formed by an air unmanned aerial vehicle and a ground unmanned aerial vehicle is a hot problem of research on a distributed artificial intelligence technology, and the organic coordination, cross-domain cooperation and the like of the unmanned aerial vehicle and the unmanned aerial vehicle lead a new mode of future robot technology and application. The unmanned vehicle can accurately position the ground target in a short distance, but under the condition that the environment information is unknown or partially known, the sensing capability of the vehicle-mounted sensor to the environment is greatly limited, and only partial path planning can be realized. The unmanned aerial vehicle has a wider field of view, and can obtain global information of the surrounding environment at a specific height, but a lot of local information is lost due to the height. Through the cooperation of the two, the advantages are complementary, and the global path planning of the unmanned vehicle can be realized.
Unmanned vehicle path planning based on unmanned vehicle vision is one of the key technologies of an air-ground heterogeneous robot system. Firstly, establishing a grid map of a ground environment based on a perception system, a positioning navigation system and the like carried by an unmanned aerial vehicle; secondly, the unmanned aerial vehicle receives unmanned aerial vehicle grid map information in real time, corrects the supplementary grid map by combining with self-perceived environment information, and then plans a collision-free optimal moving path from a departure point to a terminal point in a self-traveling manner, wherein the optimal path can meet the shortest moving path, can meet the shortest time consumption, can meet the minimum energy consumption and the like.
The unmanned vehicle path planning problem based on unmanned aerial vehicle vision can be practically regarded as a complex optimization problem with constraint conditions. Therefore, some intelligent optimization algorithms play a positive role in improving the effect of unmanned vehicle path planning, and many students have conducted a great deal of research work. For example, chen Xuejun, etc. have improved the intelligent water drop algorithm, have proposed the unmanned vehicle of the improved water drop algorithm to avoid the obstacle route planning method (Chen Xuejun, bei Shaoyi. An unmanned vehicle based on improved intelligent water drop algorithm avoids the obstacle route planning method [ P ]. Jiangsu province: CN110703767A, 2020-01-17.); liu Hongdan et al propose quantum wolf's algorithm and are used for the automatic obstacle avoidance study of unmanned intelligent vehicles (Liu Sheng, zhang Lanyong, ding Yixuan, li Bing, li, sun. Unmanned intelligent vehicle automatic collision avoidance method based on quantum wolf's algorithm [ P ]. Heilongjiang province: CN110471426A, 2019-11-19.); jiang Pengcheng et al propose unmanned vehicle path planning methods based on ant colony algorithm (Jiang Pengcheng, cong Hua, mianhao, zhang Nan, feng Fuzhou, zhang Chuanqing, liu Xixia, zhang Lixia, he Jiawu, zhang Xiaoming, wang Zhirong, yang Changwei. Unmanned vehicle hybrid path planning algorithm [ P ]. Beijing: CN110609557a, 2019-12-24.); ge Hongwei et al propose an improved multi-objective particle swarm algorithm, and a path planning study of the unmanned aerial vehicle was performed by taking the improved multi-objective particle swarm algorithm as an optimization method (Ge Hongwei, qian Xiaoyu, ge Yang. Unmanned aerial vehicle path planning method based on the improved multi-objective particle swarm algorithm [ P ]. Jiangsu: CN107992051A,2018-05-04 ].
According to the current research results, the intelligent optimization algorithm is an effective path planning method. The Sootyternoptibetiongorithm (STOA) is a novel intelligent optimization algorithm for simulating the foraging behavior of the Sooternoptibetel, and can be applied to the problem of path planning. However, the wuyangull optimization algorithm still has some defects, so that the algorithm is easy to sink into local optimum and has low convergence accuracy, and an ideal path planning effect is often not achieved when path planning is performed.
Disclosure of Invention
In order to solve the problems, the invention provides the unmanned vehicle path planning method based on unmanned aerial vehicle vision, which overcomes the defects of the existing path planning method and can remarkably improve the unmanned vehicle path planning effect.
In order to achieve the above purpose, the present invention provides the following technical solutions.
An unmanned vehicle path planning method based on unmanned vehicle vision comprises the following steps:
establishing a grid map of a ground environment based on a sensing system and a positioning navigation system carried by the unmanned aerial vehicle, acquiring the grid map established by the unmanned aerial vehicle, and correcting and supplementing the grid map by combining with self-sensed environment information;
according to the corrected and supplemented grid map, establishing an objective function of unmanned vehicle path planning taking the shortest moving path, the shortest time consumption or the least energy consumption as constraint;
according to the objective function, updating the optimal position through an improved Wuyangull optimization algorithm, and determining the optimal position of the Wuyangull;
determining an optimal path planning result according to the optimal Uighur positions which are updated in sequence according to the preset maximum iteration times;
wherein, the improved Wuyangull optimization algorithm is as follows: introducing Gaussian mapping to initialize the position of the Wuyangull population, and introducing a solution enhancement mechanism of a Longgy-Kutta optimization algorithm to comprehensively consider the optimal position of the Wuyangull; the improved Wuyangull optimization algorithm further comprises the step of further updating the optimal position through dimension-by-dimension bidirectional sine variation.
Preferably, the step of introducing Gaussian mapping to initialize the positions of the Wuyangull population comprises the following steps:
determining the size N of a population, and optimizing a lower boundary LB and an upper boundary UB of the Wuyangull;
random number x generation by gaussian mapping t ：
Wherein mod (·) is a remainder function, x t+1 Is the next random number;
initializing the position of the Wuyangull by using the generated Gaussian random number:
P s (t)＝(UB-LB)×x t +LB。
preferably, the solution enhancement mechanism introducing the Longgy-Kutta optimization algorithm comprehensively considers the optimal position of the Wuyangull, and comprises the following steps:
collision avoidance: the collision avoidance behavior process of the Wuyangull is simulated and expressed by the following formula:
C s (t)＝S A ×P s (t)
wherein: p (P) s (t) represents the position of the mew at the current t-th iteration; c (C) s (t) represents the new position of the gull without colliding with other gulls; s is S A Representing a variable factor for avoiding collision, for calculating the position after collision, the constraint condition formula is as follows:
S A ＝C f -(t×C f /Miter)
wherein: c (C) f For adjusting S A Is a control variable of (2); t represents the current iteration number; s is S A As the number of iterations increases, from C f Gradually reducing to 0; such as hypothesis C f Is 2, S A Gradually reducing from 2 to 0; miter is the number of iterations;
aggregation: the aggregation refers to that the current Wuyangull is close to the best position in the adjacent Wuyangull under the premise of avoiding collision, namely, the current Wuyangull is close to the best position, and the mathematical expression is as follows:
M s (t)＝C B ×(P bs (t)-P s (t))
wherein: p (P) bs (t) is the optimal position of the t-th iteration Uighur; m is M s (t) represents at different positions P s (t) toward the optimum position P bs (t) a process of moving; c (C) B Is a random variable that makes exploration more comprehensive, and varies according to the following formula:
C B ＝0.5×rand
wherein: rand is a random number in the range of [0,1 ];
updating: updating means that the current Wuyangull moves towards the direction of the optimal position, and the position is updated, and the mathematical expression is as follows:
D s (t)＝C s (t)+M s (t)
wherein: d (D) s (t) is the distance the gull moves from the current position to the optimal position;
attack behavior: in the migration process, the gull can increase the flying height through wings, and can also adjust the speed and attack angle of the gull, and when prey is attacked, the hover behavior of the gull in the air can be defined as the following mathematical model:
wherein: r is the radius of each spiral; θ is a random angle value in the range of [0,2π ]; u and v are correlation constants defining a spiral shape, and can be set to 1; e is the base of natural logarithms;
solution enhancement mechanism introducing Dragon-Kutta optimization algorithm, generating new solution P by using the following formula snew2 (t)：
Wherein:
P snew1 (t)＝β·((D s (t)×(x+y+z))×P bs (t))+(1-β)·P avg (t)
φ＝round((1+rand)·(1-rand))
wherein rand is [0,1]Random numbers in between; randn is a random number obeying normal distribution; c is [0,5 ]]Random numbers in between; ρ is a random integer among 1, 0 or-1; w is a random number, decreasing as the number of iterations increases; rand (0, 2) is [0,2]Random numbers in between; beta is [0,1]]Random numbers in between; p (P) r1 (t)、P r2 (t) and P r3 (t) is a table representing three mew individuals randomly selected at the t-th iteration, r1+.r2+.r3; p (P) avg (t) is P r1 (t)、P r2 (t) and P r3 An average value of (t); p (P) bs (t) represents the optimal position of the gull of the t-th iteration; p (P) snew1 (t) is a new solution; phi is a random number; round (·) is a rounding operation;
to enhance the quality of the solution, another new solution P will be generated snew3 (t) defined as follows:
wherein:
P PK (t)＝k 1 +2×k 2 +2×k 3 +k 4
wherein,is [0,2]Random numbers in between; p (P) PK (t) is a position updating mechanism based on a 4-order Dragon-Kutta method, k 1 、k 2 、k 3 And k 4 Is P PK A weight factor of (t); SF is an adaptive factor, and a and b are control parameters of the adaptive factor; p (P) sbi (t) is P r1 (t)、P r2 (t) and P r3 An optimal position in (t);
in conclusion, the improved position formula of the Wuyangull is as follows:
calculating a fitness value:
fitness(t)＝F f (P s (t+1))
wherein F is f (. Cndot.) is the fitness function when calculating fitness values;
record the optimal wuyangull in the current iteration.
Preferably, the further performing the optimal position update through dimension-by-dimension bidirectional sine mutation includes the following steps:
for the dimension j, calculating a sine chaos value according to the current iteration times, and switching positive and negative directions with equal probability:
sin Value＝sin(πx 0 )
wherein rand is a random number of 0 to 1; x is x 0 Is an iterative sequence value;
and carrying out mutation disturbance on the optimal position:
P bs(j) (t+1)′＝P bs(j) (t+1)+SinValue×P bs(j) (t+1)
wherein: p (P) bs(j) (t+1) represents the optimum of the t+1st iterationPosition P bs The j-th dimension of (t+1);
greedy update:
after each dimension is mutated, the mutation is stopped.
The invention has the beneficial effects that:
according to the invention, the Gaussian mapping is introduced to initialize the position of the Wuyangull population, so that the uniformity and diversity of the population position distribution can be improved, the stability of the algorithm is further enhanced, and the planning effect of path planning is further improved; the invention improves the position updating mode of the Wuyangull, introduces a solution enhancement mechanism of a Longgy-Kutta optimization algorithm, comprehensively considers the optimal position of the iterative Wuyangull, randomly selects other Wuyangull positions and other factors in the population, generates new solutions according to different conditions, realizes the increase of the searching range of the algorithm, and enhances the adaptability of the algorithm; the optimal Wuyangull is subjected to bidirectional sine chaotic mapping variation, so that the capability of the algorithm to jump out of a local optimal solution in the later period is realized, the searching capability is stronger, and a better moving path can be obtained rapidly.
Drawings
Fig. 1 is a flowchart of an unmanned vehicle path planning method based on unmanned aerial vehicle vision according to an embodiment of the present invention;
fig. 2 is a path planning result of an unmanned vehicle path planning method based on unmanned aerial vehicle vision according to an embodiment of the present invention;
fig. 3 is an iteration process curve of an unmanned vehicle path planning method based on unmanned aerial vehicle vision according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The invention provides an unmanned vehicle path planning method based on unmanned aerial vehicle vision, which aims at several problems existing in STOA, provides an improved Uighur's optimization algorithm (Improve Sooty tern optimization algorithm, ISTOA) and is used for unmanned vehicle path planning based on air-space information interaction, wherein the flow of the unmanned vehicle path planning method based on unmanned aerial vehicle vision is shown in figure 1, and the specific steps are as follows:
s1: and establishing a grid map of the ground environment based on a sensing system, a positioning navigation system and the like carried by the unmanned aerial vehicle, acquiring the grid map established by the unmanned aerial vehicle, and correcting and supplementing the grid map by combining with the self-sensed environment information.
S2: and establishing an objective function funtion (which can be set to be the shortest moving path, the shortest time consumption, the least energy consumption and the like according to actual needs) of unmanned vehicle path planning based on unmanned vehicle vision, and simultaneously setting corresponding constraint conditions and the number D of key nodes of the path.
S3: parameter setting is carried out, and mainly comprises the following steps: the size of the gull population (namely the number of gull individuals) N; the maximum number of iterations (i.e., conditions for iteration stop) Miter; optimizing a lower boundary LB by Wuyangull; the UB is optimized by Wuyangull.
S4: determining the size N of a population, and optimizing a lower boundary LB and an upper boundary UB of the Wuyangull; introducing Gaussian mapping to initialize the position of the Wuyangull population, comprising the following steps:
determining the size N of a population, and optimizing a lower boundary LB and an upper boundary UB of the Wuyangull;
random number x generation by gaussian mapping t ：
Wherein mod (·) is a remainder function, x t+1 Is the next random number;
initializing the position of the Wuyangull by using the generated Gaussian random number:
P s (t)＝(UB-LB)×x t +LB。
s5: collision avoidance: the collision avoidance behavior process of the Wuyangull is simulated and expressed by the following formula:
C s (t)＝S A ×P s (t)
wherein: p (P) s (t) represents the position of the mew at the current t-th iteration; c (C) s (t) represents the new position of the gull without colliding with other gulls; s is S A Representing a variable factor for avoiding collision, for calculating the position after collision, the constraint condition formula is as follows:
SA＝C f -(t×C f /Miter)
wherein: c (C) f For adjusting S A Is a control variable of (2); t represents the current iteration number; s is S A As the number of iterations increases, from C f Gradually reducing to 0; such as hypothesis C f Is 2, S A Gradually reducing from 2 to 0; miter is the number of iterations;
aggregation: the aggregation refers to that the current Wuyangull is close to the best position in the adjacent Wuyangull under the premise of avoiding collision, namely, the current Wuyangull is close to the best position, and the mathematical expression is as follows:
M s (t)＝C B ×(P bs (t)-P s (t))
wherein: p (P) bs (t) is the optimal position of the t-th iteration Uighur; m is M s (t) represents at different positions P s (t) toward the optimum position P bs (t) a process of moving; c (C) B Is a random variable that makes exploration more comprehensive, and varies according to the following formula:
C B ＝0.5×rand
wherein: rand is a random number in the range of [0,1 ];
updating: updating means that the current Wuyangull moves towards the direction of the optimal position, and the position is updated, and the mathematical expression is as follows:
D s (t)＝C s (t)+M s (t)
wherein: d (D) s (t) is the distance the gull moves from the current position to the optimal position；
Attack behavior: in the migration process, the gull can increase the flying height through wings, and can also adjust the speed and attack angle of the gull, and when prey is attacked, the hover behavior of the gull in the air can be defined as the following mathematical model:
wherein: r is the radius of each spiral; θ is a random angle value in the range of [0,2π ]; u and v are correlation constants defining a spiral shape, and can be set to 1; e is the base of natural logarithms;
in the original Wuyangull algorithm, the position of the Wuyangull is updated only by guiding by utilizing the optimal position of the Wuyangull, in order to more effectively improve the global searching capability of the Wuyangull, a solution enhancement mechanism of a Longg-Kuta optimization algorithm is introduced, the optimal position of the Wuyangull is comprehensively considered, other positions of the Wuyangull in the population are randomly selected, and a new solution P is generated according to different conditions snew1 (t)、P snew2 (t) and P snew3 And (t) avoiding local optimization in each iteration, and further improving the global searching capability of the Uighur optimization algorithm.
Solution enhancement mechanism introducing Dragon-Kutta optimization algorithm, generating new solution P by using the following formula snew2 (t)：
Wherein:
P snew1 (t)＝β·((D s (t)×(x+y+z))×P bs (t))+(1-β)·P avg (t)
φ＝round((1+rand)·(1-rand))
wherein rand is [0,1]Random numbers in between; randn is a random number obeying normal distribution; c is [0,5 ]]Random numbers in between; ρ is a random integer among 1, 0 or-1; w is a random number, decreasing as the number of iterations increases; rand (0, 2) is [0,2]Random numbers in between; beta is [0,1]]Random numbers in between; p (P) r1 (t)、P r2 (t) and P r3 (t) is a table representing three mew individuals randomly selected at the t-th iteration, r1+.r2+.r3; p (P) avg (t) is P r1 (t)、P r2 (t) and P r3 An average value of (t); p (P) bs (t) represents the optimal position of the gull of the t-th iteration; p (P) snew1 (t) is a new solution; phi is a random number; round (·) is a rounding operation;
to enhance the quality of the solution, another new solution P will be generated snew3 (t) defined as follows:
wherein:
P PK (t)＝k 1 +2×k 2 +2×k 3 +k 4
wherein,is [0,2]Random numbers in between; p (P) PK (t) is a position updating mechanism based on a 4-order Dragon-Kutta method, k 1 、k 2 、k 3 And k 4 Is P PK A weight factor of (t); SF is an adaptive factor, and a and b are control parameters of the adaptive factor; p (P) sbi (t) is P r1 (t)、P r2 (t) and P r3 An optimal position in (t);
in conclusion, the improved position formula of the Wuyangull is as follows:
s6: and calculating the fitness value.
fitness(t)＝F f (P s (t+1))
Wherein F is f (. Cndot.) is the fitness function when calculating fitness values;
s7: recording information, and recording the optimal Wuyangull in the current iteration.
S8: and carrying out dimension-by-dimension bidirectional sine variation on the optimal Wuyangull. For dimension j. Firstly, calculating a sine chaos value according to the current iteration times. And the positive and negative directions are switched with equal probability.
sin Value＝sin(πx 0 )
Wherein rand is a random number of 0 to 1; x is x 0 Is an iterative sequence value;
and carrying out mutation disturbance on the optimal position:
P bs(j) (t+1)′＝P bs(j) (t+1)+SinValue×P bs(j) (t+1)
wherein: p (P) bs(j) (t+1) represents the optimal position P of the t+1st iteration bs The j-th dimension of (t+1);
greedy update:
after each dimension is mutated, the mutation is stopped.
S9: recording information, and recording the optimal Wuyangull in the current iteration.
S10: and (5) repeatedly executing the steps S5 to S9, stopping the algorithm after the maximum iteration number Miter is reached, and outputting an optimal path result.
In this embodiment:
and taking MATLAB as a simulation platform, assuming a constructed 20X 20 grid map, and taking the shortest moving distance as a target to analyze the STOA method and the ISTOA method. The parameters in the STOA algorithm are: n=50, maxiter=200, lb=1, ub=20; the parameters in the ISTOA algorithm are: n=50, maxiter=200, lb=1, ub=20. The motion paths obtained by the simulation environment and the two methods are shown in fig. 2, and fig. 3 is an iteration process curve. Table 1 is a comparison of the data results of the two algorithms.
TABLE 1 comparison of Algorithm Path results
| Algorithm | Path length |
| STOA | 35.5563 |
| ISTOA | 32.1421 |
From fig. 2, it can be intuitively found that the movement path obtained by STOA is longer than that obtained by ISTOA, the path is more detour, and the path obtained by ISTOA is more reasonable. Further analysis of the results in fig. 2 and 3 shows that the algorithm converges at a slower rate when using the STOA algorithm; when the ISTOA algorithm is adopted, the convergence speed is higher, and a better path can be found out more quickly. It can be seen that the ISTOA algorithm designed herein has a faster convergence speed and convergence accuracy. Simulation results show that under a plurality of same environments, the ISTOA algorithm has stronger searching capability, obtains a better moving path and verifies the effectiveness of the algorithm.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (3)
1. The unmanned vehicle path planning method based on unmanned vehicle vision is characterized by comprising the following steps of:
establishing a grid map of a ground environment based on a sensing system and a positioning navigation system carried by the unmanned aerial vehicle, acquiring the grid map established by the unmanned aerial vehicle, and correcting and supplementing the grid map by combining with self-sensed environment information;
according to the corrected and supplemented grid map, establishing an objective function of unmanned vehicle path planning taking the shortest moving path, the shortest time consumption or the least energy consumption as constraint;
according to the objective function, updating the optimal position through an improved Wuyangull optimization algorithm, and determining the optimal position of the Wuyangull;
determining an optimal path planning result according to the optimal Uighur positions which are updated in sequence according to the preset maximum iteration times;
wherein, the improved Wuyangull optimization algorithm is as follows: introducing Gaussian mapping to initialize the position of the Wuyangull population, and introducing a solution enhancement mechanism of a Longgy-Kutta optimization algorithm to comprehensively consider the optimal position of the Wuyangull; the improved Wuyangull optimization algorithm further comprises the step of further updating the optimal position through dimension-by-dimension bidirectional sine variation;
the solution enhancement mechanism introducing the Dragon-Kutta optimization algorithm comprehensively considers the optimal position of the Wuyangull, and comprises the following steps:
collision avoidance: the collision avoidance behavior process of the Wuyangull is simulated and expressed by the following formula:
C s (t)＝S A ×P s (t)
wherein: p (P) s (t) represents the position of the mew at the current t-th iteration; c (C) s (t) represents the new position of the gull without colliding with other gulls; s is S A Representing a variable factor for avoiding collision, for calculating the position after collision, the constraint condition formula is as follows:
S A ＝C f -(t×C f /Miter)
wherein: c (C) f For adjusting S A Is a control variable of (2); t represents the current iteration number; s is S A As the number of iterations increases, from C f Gradually decrease to 0 ； Miter is the number of iterations;
aggregation: the aggregation refers to that the current Wuyangull is close to the best position in the adjacent Wuyangull under the premise of avoiding collision, namely, the current Wuyangull is close to the best position, and the mathematical expression is as follows:
M s (t)＝C B ×(P bs (t)-P s (t))
wherein: p (P) bs (t) is the optimal position of the t-th iteration Uighur; m is M s (t) represents at different positions P s (t) toward the optimum position P bs (t) a process of moving; c (C) B Is a random variable that makes exploration more comprehensive, and varies according to the following formula:
C B ＝0.5×rand
wherein: rand is a random number in the range of [0,1 ];
updating: updating means that the current Wuyangull moves towards the direction of the optimal position, and the position is updated, and the mathematical expression is as follows:
D s (t)＝C s (t)+M s (t)
wherein: d (D) s (t) is the distance the gull moves from the current position to the optimal position;
attack behavior: in the migration process, the gull can increase the flying height through wings, and can also adjust the speed and attack angle of the gull, and when prey is attacked, the hover behavior of the gull in the air can be defined as the following mathematical model:
wherein: x, y and z are coordinates in three directions, and r is the radius of each spiral; θ is a random angle value in the range of [0,2π ]; u and v are correlation constants defining a spiral shape, and can be set to 1; e is the base of natural logarithms;
solution enhancement mechanism introducing Dragon-Kutta optimization algorithm, generating new solution P by using the following formula snew2 (t)：
Wherein:
P snew1 (t)＝β·((D s (t)×(x+y+z))×P bs (t))+(1-β)·P avg (t)
φ＝round((1+rand)·(1-rand))
wherein rand is [0,1]Random numbers in between; randn is a random number obeying normal distribution; c is [0,5 ]]Random numbers in between; ρ is a random integer among 1, 0 or-1; w is a random number, decreasing as the number of iterations increases; rand (0, 2) is [0,2]Random numbers in between; beta is [0,1]]Random numbers in between; p (P) r1 (t)、P r2 (t) and P r3 (t) is a table representing three mew individuals randomly selected at the t-th iteration, r1+.r2+.r3; p (P) avg (t) is P r1 (t)、P r2 (t) and P r3 An average value of (t); p (P) bs (t) represents the optimal position of the gull of the t-th iteration; p (P) snew1 (t) is a new solution; phi is a random number; round (·) is a rounding operation;
to enhance the quality of the solution, another new solution P will be generated snew3 (t) defined as follows:
wherein:
P PK (t)＝k 1 +2×k 2 +2×k 3 +k 4
wherein,is [0,2]Random numbers in between; p (P) PK (t) is a position updating mechanism based on a 4-order Dragon-Kutta method, k 1 、k 2 、k 3 And k 4 Is P PK A weight factor of (t); SF is an adaptive factor, and a and b are control parameters of the adaptive factor; p (P) sbi (t) is P r1 (t)、P r2 (t) and P r3 An optimal position in (t);
in conclusion, the improved position formula of the Wuyangull is as follows:
calculating a fitness value:
fitness(t)＝F f (P s (t+1))
wherein F is f (. Cndot.) is the fitness function when calculating fitness values;
record the optimal wuyangull in the current iteration.
2. The unmanned vehicle path planning method based on unmanned vehicle vision according to claim 1, wherein the step of introducing gaussian mapping to initialize the positions of the gull population comprises the following steps:
determining the size N of a population, and optimizing a lower boundary LB and an upper boundary UB of the Wuyangull;
random number x generation by gaussian mapping t ：
Wherein mod (·) is a remainder function, x t+1 Is the next random number;
initializing the position of the Wuyangull by using the generated Gaussian random number:
P s (t)＝(UB-LB)×x t +LB。
3. the unmanned vehicle path planning method based on unmanned vehicle vision according to claim 1, wherein the further updating of the optimal position by dimension-wise bidirectional sine variation comprises the following steps:
for the dimension j, calculating a sine chaos value according to the current iteration times, and switching positive and negative directions with equal probability:
sin Value＝sin(πx 0 )
wherein rand is a random number of 0 to 1; x is x 0 Is an iterative sequence value;
and carrying out mutation disturbance on the optimal position:
P bs(j) (t+1)′＝P bs(j) (t+1)+SinValue×P bs(j) (t+1)
wherein: p (P) bs(j) (t+1) represents the optimal position P of the t+1st iteration bs The j-th dimension of (t+1);
greedy update:
after each dimension is mutated, the mutation is stopped.
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