CN111157002B - Aircraft 3D path planning method based on multi-agent evolutionary algorithm - Google Patents
Aircraft 3D path planning method based on multi-agent evolutionary algorithm Download PDFInfo
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Abstract
The invention provides an aircraft 3D path planning method based on multi-agent evolution, which comprises the following steps: setting the starting point coordinate, the end point coordinate and the multi-agent evolution parameter of the aircraft, and initializing the multi-agent grid L t Then, the tth generation multi-agent grid L is calculated t Middle intelligent agent L i,j t According to the energy of the agent, obtaining the global optimal agent Best t Then to L t Middle intelligent agent L i,j t Performing neighborhood competition operators, evolving competition failure agents through crossover and mutation operators, and gathering competition winning agents into Part win The intelligent agent in the system carries out self-learning operator to complete the evolution of the intelligent agent grid, and finally, the maximum iteration times are controlled to calculate L t And (5) acquiring the 3D path planning result of the aircraft by the medium intelligent body energy. The method can effectively reduce the calculated amount of the 3D path planning of the aircraft and improve the convergence speed of the 3D path planning of the aircraft on the basis of ensuring the practicability of the 3D path planning result of the aircraft.
Description
Technical Field
The invention belongs to the technical field of path planning, relates to a 3D path planning method of an aircraft, and particularly relates to a 3D path planning method of an aircraft based on a multi-agent evolutionary algorithm, which can be used in the fields of intelligent navigation, unmanned flight and the like of the aircraft.
Background
The aircraft path planning can enable the aircraft to automatically calculate the optimal path from the starting point to the end point, and is a key factor for realizing autonomous navigation and control of all aircraft. At present, a plurality of path planning methods such as a random tree method, an A star algorithm and the like are provided around the 2D path planning of a two-dimensional map, and a good planning effect is obtained. Compared with 2D path planning, the environment for 3D path planning is more complex, modeling difficulty is higher, the planned optimal path is not always the shortest path, and more importantly, complex terrain factors, dangerous areas in the environment and the like need to be considered. In recent years, with the wide application of aircraft in fields such as forest fire prevention, aerial survey, on-site rescue, express delivery, military reconnaissance. The complex environment brings great difficulty to the 3D path planning of the aircraft, and on the other hand, the aircraft can be subjected to efficient real-time path planning in a dynamic environment due to more demands. Therefore, the efficient path planning method in the 3D space is designed, and the method has great significance for automatic navigation control and unmanned flight of the aircraft. The 3D path planning of the aircraft is widely applied in practice, theoretically, modeling modes of the problem are rich, the problem belongs to a typical NP-hard problem, and the problem is difficult to solve, so that how to efficiently obtain a better path planning result is fundamentally a difficult problem.
At present, research methods for planning 3D paths of aircrafts in documents are mainly divided into traditional search algorithms represented by a random tree method and an A-star algorithm, and meta-heuristic algorithms represented by a genetic algorithm, a simulated annealing algorithm and a particle swarm optimization algorithm. In the traditional search algorithm, a 3D environment space is modeled into a network topology connected graph model, and then path planning is converted into a problem of solving the shortest path on the network topology connected graph, so that high-speed path planning can be rapidly carried out. However, the method for constructing the network topology connectivity graph has the problem of difficulty in modeling the environment when the complex environment is faced, and in the actual many applied 3D spaces, the best path is not the shortest path generally. Therefore, the practical application of the traditional search algorithm in 3D path planning is limited to a great extent. Compared with the traditional search algorithm, the meta-heuristic algorithm only needs to design the loss function value of the planned path in the complex environment, and the modeling complexity is high-efficiency and low in difficulty. Meta-heuristic algorithms are currently generally considered to be the best method after trade-offs in terms of performance, scalability, and ease of implementation. The genetic algorithm is used as an efficient meta-heuristic algorithm and is effectively applied to the field of path planning. For example, the paper "Fast Genetic Algorithm Path Panel for Fixed-Wing Military UAV Using GPU" (IEEE Transactions on Aerospace and Electronic Systems, Page No.: 99, 2018) published by Roberge et al proposes a 3D Path planning method Using Genetic Algorithm GA. The method comprises the steps of coding a planned path into chromosomes, randomizing and generating a large number of chromosomes on the basis of simulating biological evolution to form a chromosome population representing the path, calculating the fitness value of each path according to the environment where the path is located, calculating the competitiveness of each individual in the whole population to ensure that the path represented by the outstanding chromosome can propagate and survive in the population, and realizing parallel calculation of the fitness values of the individuals in the population by using a graphic processor GPU when calculating the fitness value of the path, so that the calculation process of evaluating the whole population is accelerated, and finally aiming at the path planning problem, designing variation on the path represented by the chromosome and performing cross operation to ensure that the chromosome is evolved. The method realizes highly parallel and self-adaptive optimization of path planning, and has the advantages of low loss value of the optimized path result and strong practicability. However, this method is essentially a GA-based algorithm, and the individuals used for generating offspring are selected from the whole population according to fitness, so the fitness distribution of the whole population must be predetermined and then compared and competed globally, and therefore the planning of the trajectory is not promoted from the perspective of the algorithm. In fact, natural selection is itself a local phenomenon that only makes information globally shared by the gradual diffusion of local competition, in relation to the local environment in which the individual is located. In this method, the amount of calculation is doubled when the population size of the path constituents represented by the designed chromosome increases. The path represented by each chromosome focuses on the fitness value of other paths in the whole population in the optimization process, and the path planning process needs the synchronous co-evolution of the whole population formed by all paths, which results in the defects of large calculation amount and slow convergence speed of the method to a great extent.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an aircraft 3D path planning method based on a multi-agent evolutionary algorithm, and aims to reduce the calculated amount of the aircraft 3D path planning and improve the convergence speed of the aircraft 3D path planning on the basis of ensuring the practicability of the aircraft 3D path planning result.
In order to achieve the purpose, the technical scheme adopted by the invention comprises the following steps:
(1) setting the starting point coordinates and the end point coordinates of the aircraft:
setting the starting point coordinate of the aircraft in the digital elevation map model map as st ═ (x) 0 ,y 0 ,z 0 ) The coordinate of the end point is end ═ x N ,y N ,z N ) Dividing the path from st to end into N sections, wherein N is more than 20;
(2) setting parameters of multi-agent evolution:
setting a multi-agent grid adopted by multi-agent evolution as L and a self-learning multi-agent grid as sL, wherein the size of L is L size ×L size ，L size More than 4, the evolution algebra is T, the maximum evolution algebra is T, T is more than 40, and the size of sL is sL size ×sL size ，3≤sL size The self-learning evolution algebra is ts, the self-learning maximum evolution algebra is sT, sT is more than 3 and less than 9, and t is equal to 0;
(3) initializing Multi-agent grid L t ：
(3a) The position coordinate set of N-1 path planning points of the random initial aircraft in the map is path i,j ＝{(x 1 ,y 1 ,z 1 ) i,j ,(x 2 ,y 2 ,z 2 ) i,j ,…,(x N-1 ,y N-1 ,z N-1 ) i,j And will path i,j St and end are combined to obtain the agent L of the ith row and the jth column in the L i,j t ,L i,j t ＝[st,path i,j ,end]；
(3b) Mixing L with size ×L size Initialization multi-agent grid L formed by combining intelligent agents t ，
(4) Computing the tth generation multi-agent grid L t Middle intelligent agent L i,j t Can ofQuantity:
through t-th generation multi-agent grid L t Middle intelligent agent L i,j t Loss function value F on map loss (L i,j t Map), calculate L in Lt i,j t Energy En (L) of i,j t )＝1/F loss (L i,j t ,map)；
(5) Obtaining a globally optimal agent Best t ：
Judging whether t is equal to 0 or not, if yes, judging L t Agent cBest with maximum medium energy t Best as a globally optimal agent t Otherwise, comparing the agent cBest t And Best t-1 And taking the agent with large energy as the global optimal agent Best t ；
(6) To L t Middle intelligent agent L i,j t Performing neighborhood competition operators:
(6a) definition of L t Middle intelligent agent L i,j t The four neighborhood agents of
The agent with the highest energy among the four neighborhood agents is L i,j max Wherein, the% is expressed as mathematical modulus operation, and the competition failure agent set is Part lose The competition winning agent set is Part win And make an order
(6b) Determine En (L) i,j t )＜En(L i,j max ) If yes, executing the step (6c), otherwise, executing the step (6 d);
(6c) with P c Is a probability pair L i,j max And L i,j t Crossing to obtain offspring agent L i,j g And with P m Is a probability pair L i,j g Performing mutation to obtain new agent L i,j new Then add all new agents to the set Part lose After (5), executing step (6 e);
(6d) will agent L i,j t As a winning agent L i,j win And add all winning agents to the set Part win After (5), executing step (6 e);
(6e) part is to be lose Each new agent and Part win Adding each winning intelligent agent to the corresponding position in the multi-intelligent-agent grid L to obtain L t To L t+1 Middle generation multi-agent grid L t+1/2 ；
(7) Part of competitive winning intelligent agent set win The agent in (1) carries out self-learning operator:
(7a) defining the set after the competition winning agent evolves as Part win_new And make an order
(7b) Part is treated according to the method of step (6c) win Each winning agent L in i,j win Carrying out sL size ×sL size Minor variation to give a sum of L i,j win The associated ts-th generation of self-learning multi-agent mesh, let ts equal 0,
(7c) calculating sL according to the method of step (4) i,j,ts Middle intelligent agent sL m,n i,j,ts And according to the method of step (6) on sL i,j,ts Middle sL m,n i,j,ts Performing field competition to obtain an intermediate generation self-learning intelligent agent grid sL i,j,ts+1/2 ；
(7d) Judging whether ts < sT is true, if yes, making sL i,j,ts+1 ＝sL i,j,s+1/2 Ts +1 and step (7c) is performed, otherwise sL is calculated i,j,ts The energy of each agent, and will sL i,j,ts Middle with the highest energySelf-learning agent L i,j win_new Add set Part win_new After (5), executing step (7 e);
(7e) part is to be lose Each new agent and Part win_new Adding each self-learning agent into the corresponding position in the multi-agent grid L to obtain the t +1 th generation multi-agent grid L t+1 ；
(8) Obtaining a 3D path planning result of the aircraft:
let T be T +1, and judge T < T and hold, if yes, carry out step (4), otherwise, calculate L t The energy of each agent in the system, and comparing L t Agent cBest with maximum medium energy t And Best t-1 The agent with large energy is taken as the global optimal agent Best t And outputting to realize the 3D path planning of the aircraft.
Compared with the prior art, the invention has the following advantages:
the method comprises the steps that 1, 3D path planning of an aircraft is achieved based on a multi-agent evolution algorithm, each planned path is represented as an agent and forms a multi-agent grid in the planning process, each agent can only sense local environment, information of one agent is transmitted to field agents after the agent interacts with the neighborhood of the agent, and the information is gradually diffused to the whole agent grid.
2. When the multi-agent evolution algorithm is applied to the aircraft 3D path planning problem, on the basis of representing each planning path as an agent, a neighborhood competition operator and a self-learning operator of the agent are designed, the neighborhood competition operator can extract a group formed by elite paths in the group, the paths represented by weak individuals are optimized, and the self-learning operator can ensure that the elite individuals are optimized more deeply, so that local search can be effectively carried out in the evolution process.
3. According to the invention, by deeply excavating and utilizing the intelligent evolution resources of organisms and using a multi-agent evolution mode based on the perception and the counteraction of agents to the environment, the model of the multi-agent grid is closer to a real natural evolution mechanism, compared with the prior art, the loss value of the obtained 3D path is equivalent, and the practicability of the 3D path planning result of the aircraft is ensured.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention.
FIG. 2 is a schematic diagram of the multi-agent grid of the present invention.
FIG. 3 is a schematic diagram of crossover and variation implementations of the present invention.
FIG. 4 is a graph comparing simulation of traces obtained under different size populations according to the present invention and the prior art.
FIG. 5 is a graph comparing simulations of convergence speed of the present invention and the prior art.
Fig. 6 is a simulation comparison graph of the stability of the 3D path planning results of the present invention and the prior art.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
Referring to fig. 1, the present invention includes the steps of:
step 1) setting a starting point coordinate and an end point coordinate of the aircraft:
setting the coordinates of a starting point of the aircraft in the digital elevation map model map as st ═ 0,0 and the coordinates of an end point as end ═ 400,0, and dividing a path between the st and the end into N sections, wherein N is 27;
step 2) setting parameters of multi-agent evolution:
setting a multi-agent grid adopted by multi-agent evolution as shown in FIG. 2 as L, a self-learning multi-agent grid as sL, and one agent living in each grid position, wherein the grids have direct connection relation, namely, are neighbors to each other, and each agent can only interact with the neighborhood, wherein LIs of size L size ×L size ，L size 5, T is evolution algebra, T is maximum evolution algebra, T is 200, and sL is size sL size ×sL size ，sL size The self-learning evolution algebra is ts, the self-learning maximum evolution algebra is sT, the sT is 6, and t is 0;
step 3) initializing multi-agent grid L t The method comprises the following steps that a multi-agent grid is initialized according to a task target of 3D path planning, each agent in the grid represents a planned path, all agents exist in a grid environment, the agents transmit information to field agents after acting with neighborhoods of the agents, the information is gradually diffused to the whole agent grid, the population scale can be smaller, the condition that each individual in the prior art lives in a population independently is avoided, the individual in an algorithm is endowed with the capacity of local mutual communication, and the calculated amount of the 3D path planning of the aircraft can be effectively reduced:
(3a) the position coordinate set of N-1 path planning points of the random initial aircraft in the map is path i,j ＝{(x 1 ,y 1 ,z 1 ) i,j ,(x 2 ,y 2 ,z 2 ) i,j ,…,(x N-1 ,y N-1 ,z N-1 ) i,j And will path i,j St and end are combined to obtain the agent L of the ith row and the jth column in the L i,j t ,L i,j t ＝[st,path i,j ,end]；
(3b) Will L size ×L size Individual agent combined initialization multi-agent grid L t ，
Step 4) calculating the tth generation multi-agent grid L t Middle intelligent agent L i,j t Energy of (2):
through t-th generation multi-agent grid L t Middle intelligent agent L i,j t Loss function value F on map loss (L i,j t Map), calculate L t Middle L i,j t Energy En (L) of i,j t )：
En(L i,j t )＝1/F loss (L i,j t ,map)；
F loss (L i,j t ,map)＝w 1 *Loss path (L i,j t )+w 2 *Loss altitude (L i,j t ,map)；
Wherein Loss path (L i,j t ) For path length Loss, Loss altitude (L i,j t Map) is the loss in altitude, w, of the path projected onto the map 1 And w 2 Are respectively a pair of Loss path (L i,j t ) And Loss altitude (L i,j t Penalty weight of (x) map k ,y k ,z k ) Represents L i,j The value of the k-th point coordinate in the agent,
representing the summation of the N sample calculated values taken, | · | | non-woven phosphor 2 2 norm of the vector, [ (x) k-1 ,y k-1 ,z k-1 )→(x k ,y k ,z k )] M Is shown at L i,j M point coordinates obtained by performing M times of mean value sampling on the road section connected by the k-1 point to the k point, map [ [ (x [ ]) k-1 ,y k-1 ,z k-1 )→(x k ,y k ,z k )] M The method comprises the following steps of (1) solving M altitude values of positions where M point coordinates obtained by sampling are projected to map, wherein mean {. cndot } represents the average of element values in a set;
step 5) obtaining the global maximumBest intelligent agent Best t ：
Judging whether t is equal to 0 or not, if yes, judging L t Agent cBest with maximum medium energy t Best as a globally optimal agent t Otherwise, comparing the agent cBest t And Best t-1 And the agent with large energy is taken as the global optimal agent Best t ；
Step 6) for L t Middle intelligent agent L i,j t Performing neighborhood competition operators, optimizing disadvantaged individuals in the population through the domain competition, extracting elite populations in the population, and finally obtaining a semi-evolved multi-agent grid:
(6a) definition of L t Middle intelligent agent L i,j t The four neighborhood agents of
The agent with the highest energy among the four neighborhood agents is L i,j max Wherein, the% is expressed as mathematical modulus operation, and the competition failure agent set is Part lose The competitive winning agent is set as Part win And make an order
(6b) Judging En (L) i,j t )＜En(L i,j max ) If yes, executing the step (6c), otherwise, executing the step (6 d);
(6c) with P c Is a probability pair L i,j max And L i,j t Crossing to obtain offspring agent L i,j g And with P m Is a probability pair L i,j g Performing mutation to obtain new agent L i,j new Then add all new agents to the set Part lose After (5), step (6e) is performed, wherein the new agent L i,j new Is obtained byThe method comprises the following steps:
(6c1) randomly generated real numbers r between 0 and 1 1 If r is 1 ＞P c Let the offspring agent L i,j g ＝L i,j max Executing the step (6c3), otherwise, executing the step (6c 2);
(6c2) to L i,j max And L i,j t Performing crossover operator, and randomly selecting an integer p in a range of (1, N-1) n Then from p n Position dividing two agents into front and back two segments, agent L i,j max And L i,j t The posterior parts are connected to form a filial generation individual, as shown in fig. 3 (a):
randomly generating a random integer p between 1 and N-1 n Ae of No. 1, N-1, and taking L i,j max Middle 0 th position coordinate to p th position coordinate n Coordinate set path of path segment formed by position coordinates 1 Taking L i,j t Middle (p) n The +1 position coordinate to the Nth position coordinate form a coordinate set path of a path segment 2 Will path 1 、path 2 Combined to form a offspring agent L i,j g ＝[path 1 ,path 2 ]；
(6c3) Randomly generated real numbers r between 0 and 1 2 If r is 2 ＞P m Let L i,j new ＝L i,j g Executing the step (6c5), otherwise, executing the step (6c 4);
(6c4) to L i,j g Performing mutation operator, randomly selecting an integer N in a range of (1, N-1), and then changing the coordinate of the nth position into a value between the coordinates of the (N-1) th position and the (N + 1) th position to obtain a new agent, as shown in fig. 3 (b):
randomly generating a random integer N ← random (1, N-1) between 1 and N-1, obtaining L i,j g Middle (n-1) position coordinate (x) n-1 ,y n-1 ,z n-1 ) And n +1 position coordinates (x) n+1 ,y n+1 ,z n+1 ) Randomly generating 3 real numbers r between 0 and 1 x 、r y 、r z And calculating the coordinate of the variation position as (x) r ,y r ,z r )：
x r ＝x n-1 +r x (x n+1 -x n-1 )；
y r ＝y n-1 +r y (y n+1 -y n-1 )；
z r ＝z n-1 +r z (z n+1 -z n-1 )；
Mixing L with i,j g The nth position coordinate is reassigned to (x) r ,y r ,z r ) And make L i,j new ＝L i,j g ；
(6c5) Will new agent L i,j new Add to set Part lose The preparation method comprises the following steps of (1) performing;
(6d) will agent L i,j t As a winning agent L i,j win And add all winning agents to the set Part win After (5), executing step (6 e);
(6e) will Part lose Each new agent and Part in win Adding each winning intelligent agent to the corresponding position in the multi-intelligent-agent grid L to obtain L t To L t+1 Middle generation multi-agent grid L t+1/2 ；
Step 7) set Part of competitive winning intelligent agent win The intelligent agent in the system carries out a self-learning operator, the self-learning operator can ensure that elite individuals in the population are deeply optimized, and local search is carried out near high-quality individuals in the optimization process of the population, so that the aim of accelerating the convergence speed of the whole population is fulfilled:
(7a) defining the set after the competition winning agent evolves as Part win_new And make an order
(7b) Part is treated according to the method of step (6c) win Each winning agent L in i,j win To carry out sL size ×sL size Minor variation to give a sum of L i,j win Related ts generation self-learning multi-agent grid, let ts equal 0
(7c) Calculating sL according to the method of step (4) i,j,ts Middle intelligent agent sL m,n i,j,ts And according to the method of step (6) on sL i,j,ts Middle sL m,n i,j,ts Performing field competition to obtain an intermediate generation self-learning intelligent agent grid sL i,j,ts+1/2 ；
(7d) Judging whether ts < sT is true, if yes, making sL i,j,ts+1 ＝sL i,j,s+1/2 Ts +1 and step (7c) is performed, otherwise sL is calculated i,j,ts Energy of each agent, and comparing sL i,j,ts Self-learning agent L with highest energy in i,j win_new Add set Part win_new After (5), executing step (7 e);
(7e) will Part lose Each new agent and Part win_new Adding each self-learning agent into the corresponding position in the multi-agent grid L to obtain the t +1 th generation multi-agent grid L t+1 Let t be t + 1;
step 8) obtaining a 3D path planning result of the aircraft:
judging whether T is more than T, if so, executing the step (4), otherwise, calculating L t The energy of each agent in the system, and comparing L t Agent cBest with maximum medium energy t And Best t-1 The intelligent agent with large energy is taken as a global optimal intelligent agent Best t And outputting to realize the 3D path planning of the aircraft.
The technical effects of the present invention will be further described with reference to simulation experiments.
1. Simulation conditions and contents:
environment of experimental run: the operating system is Microsoft windows 10, and the programming simulation language is matlab. The experiment uses four digital elevation map model data, and the convergence rate curve and the loss function value are averaged results after 40 independent experiments.
Simulation 1: the results of comparative simulation of the trajectories obtained by the present invention and the prior GA techniques under different scale populations are shown in fig. 4.
Simulation 2: the results of comparative simulation of the convergence rate of the present invention and the prior art GA are shown in fig. 5.
Simulation 3: the results of the comparison and simulation of the stability of the 3D path planning results of the present invention and the existing GA technology are shown in fig. 6.
2. And (3) simulation result analysis:
referring to FIG. 4, FIG. 4(a) compares the trace results obtained from 3D path planning performed on a real elevation data map model according to the prior art GA, and FIG. 4(b) compares the trace results obtained from 3D path planning performed on a synthetic virtual elevation data map model according to the prior art GA. The altitude represented by the circular area in the figure is infinitely high. In different map environments, the size of the population scale in the prior art is adjusted, so that the prior art can obtain a path planning result similar to that of the method. The invention sets the number of agents in the multi-agent grid to be 25, and when the finally used prior art population size is 100, the track result of 3D path planning similar to that of the invention in figure 4 can be obtained. The invention uses smaller-scale population, obtains the result similar to the prior art, and shows that the invention can effectively reduce the calculated amount of the 3D path planning of the aircraft.
Referring to fig. 5, the abscissa is the number of iterations and the ordinate is the normalized loss function value of the optimal individual in the population during the iteration process, the present invention uses the neighborhood competition and the self-learning process, thereby accelerating the evolution process of outstanding individuals. The method has absolute advantage in convergence speed, and can tend to converge in 40 iterations, while the algebraic number required for achieving convergence in the prior art is 70. Experimental results show that the method can effectively improve the convergence speed of the 3D path planning of the aircraft
Referring to fig. 6, the abscissa represents four different 3D maps, and the ordinate represents the average loss function value of the population, and the loss function values of the trajectories optimized by the present invention are comparable to the results obtained by the prior art in four environments. The method can ensure the practicability of the aircraft 3D path planning result.
Claims (3)
1. An aircraft 3D path planning method based on a multi-agent evolution algorithm is characterized by comprising the following steps:
(1) setting the starting point coordinates and the end point coordinates of the aircraft:
setting the starting point coordinate of the aircraft in the digital elevation map model map as st ═ (x) 0 ,y 0 ,z 0 ) The coordinate of the end point is end ═ x N ,y N ,z N ) Dividing a path from st to end into N sections, wherein N is more than 20;
(2) setting parameters of multi-agent evolution:
setting a multi-agent grid adopted by multi-agent evolution as L and a self-learning multi-agent grid as sL, wherein the size of L is L size ×L size ，L size More than 4, the evolution algebra is T, the maximum evolution algebra is T, T is more than 40, and the size of sL is sL size ×sL size ，3≤sL size The self-learning evolution algebra is ts, the self-learning maximum evolution algebra is sT, sT is more than 3 and less than 9, and t is equal to 0;
(3) initializing Multi-agent grid L t ：
(3a) The position coordinate set of N-1 path planning points of the random initial aircraft in the map is path i,j ＝{(x 1 ,y 1 ,z 1 ) i,j ,(x 2 ,y 2 ,z 2 ) i,j ,...,(x N-1 ,y N-1 ,z N-1 ) i,j And will path i,j St and end are combined to obtain the agent L of the ith row and the jth column in the L i,j t ,L i,j t ＝[st,path i,j ,end]；
(3b) Mixing L with size ×L size Initialization multi-agent grid L formed by combining intelligent agents t ，
(4) Computing tth generation multi-agent grid L t Middle intelligent agent L i,j t Energy of (2):
through t-th generation multi-agent grid L t Middle intelligent agent L i,j t Loss function value F on map loss (L i,j t Map), calculate L t Middle L i,j t Energy En (L) of i,j t )＝1/F loss (L i,j t ,map)；
(5) Obtaining a globally optimal agent Best t ：
Judging whether t is equal to 0 or not, if yes, judging L t Agent cBest with maximum medium energy t Best as a globally optimal agent t Otherwise, comparing the agent cBest t And Best t-1 And the agent with large energy is taken as the global optimal agent Best t ；
(6) To L t Middle intelligent agent L i,j t Performing neighborhood competition operators:
(6a) definition of L t Middle intelligent agent L i,j t The four neighborhood agents of
The agent with the highest energy among the four neighborhood agents is L i,j max Wherein, the percentage is expressed as mathematical modular operation, and the competition failure agent set is Part lose The competitive winning agent is set as Part win And make an order
(6b) Determine En (L) i,j t )＜En(L i,j max ) If yes, executing the step (6c), otherwise, executing the step (6 d);
(6c) with P c Is a probability pair L i,j max And L i,j t Crossing to obtain offspring agent L i,j g And with P m Is a probability pair L i,j g Performing mutation to obtain new agent L i,j new Then add all new agents to the set Part lose After (5), executing step (6 e);
(6d) will agent L i,j t As a winning agent L i,j win And add all winning agents to the set Part win After (5), executing step (6 e);
(6e) part is to be lose Each new agent and Part win Adding each winning intelligent agent to the corresponding position in the multi-intelligent-agent grid L to obtain L t To L t+1 Middle generation multi-agent grid L t+1/2 ；
(7) Part of competitive winning intelligent agent set win The agent in (1) performs self-learning operator:
(7a) defining the set after the competition winning agent evolves as Part win_new And make an order
(7b) Part is treated according to the method of step (6c) win Each winning agent L in i,j win Carrying out sL size ×sL size Minor variation to give sum L i,j win The associated ts-th generation of self-learning multi-agent mesh, let ts equal 0,
(7c) calculating sL according to the method of step (4) i,j,ts Middle intelligent agent sL m,n i,j,ts And according to the method of step (6) on sL i,j,ts Middle sL m,n i,j,ts Performing field competition to obtain an intermediate generation self-learning intelligent agent grid sL i,j,ts+1/2 ；
(7d) Judging whether ts < sT is true, if yes, making sL i,j,ts+1 ＝sL i,j,s+1/2 Ts +1 and step (7c) is performed, otherwise sL is calculated i,j,ts The energy of each agent, and will sL i,j,ts Self-learning agent L with highest energy in i,j win_new Add set Part win_new After (5), executing step (7 e);
(7e) part is to be lose Each new agent and Part in win_new Adding each self-learning agent into the corresponding position in the multi-agent grid L to obtain the t +1 th generation multi-agent grid L t+1 Let t be t + 1;
(8) obtaining a 3D path planning result of the aircraft:
and judging whether T is more than T, if so, executing the step (4), otherwise, calculating L t The energy of each agent in the system, and comparing L t Agent cBest with maximum medium energy t And Best t-1 The intelligent agent with large energy is taken as a global optimal intelligent agent Best t And outputting to realize the 3D path planning of the aircraft.
2. The multi-agent evolutionary algorithm-based 3D path planning method for aircraft according to claim 1, wherein the tth generation multi-agent mesh L in step (4) t Middle intelligent agent L i,j t Loss function value F on map loss (L i,j t Map), the calculation formula is:
F loss (L i,j t ,map)＝w 1 *Loss path (L i,j t )+w 2 *Loss altitude (L i,j t ,map)；
wherein Loss path (L i,j t ) For path length Loss, Loss altitude (L i,j t Map) is the loss in altitude, w, of the path projected onto the map 1 And w 2 Are respectively a pair of Loss path (L i,j t ) And Loss altitude (L i,j t Penalty weight of (x) map k ,y k ,z k ) Represents L i,j The value of the k-th point coordinate in the agent,
representing the summation of the N sample calculated values taken, | · | | non-woven phosphor 2 2 norm of the vector, [ (x) k-1 ,y k-1 ,z k-1 )→(x k ,y k ,z k )] M Is shown at L i,j M point coordinates obtained by performing M times of mean value sampling on the road section connected by the k-1 point to the k point, map [ [ (x [ ]) k-1 ,y k-1 ,z k-1 )→(x k ,y k ,z k )] M The method comprises the steps of calculating M altitude values of positions where M point coordinates obtained by sampling are projected to map, and mean {. cndot } represents the average of element values in a set.
3. 3D aircraft path planning method based on multi-agent evolutionary algorithm, as claimed in claim 1, characterized in that the new agent L in step (6c) is i,j new The acquisition comprises the following steps:
(6c1) randomly generated real numbers r between 0 and 1 1 If r is 1 ＞P c Let the offspring agent L i,j g ＝L i,j max Executing the step (6c3), otherwise, executing the step (6c 2);
(6c2) to L i,j max And L i,j t And (3) carrying out a crossover operator:
randomly generating a random integer p between 1 and N-1 n Ae of No. 1, N-1, and taking L i,j max Middle 0 th position coordinate to p th position coordinate n Path formed by position coordinatesCoordinate set path of segments 1 Taking L i,j t Middle (p) n +1 position coordinate to Nth position coordinate to form the coordinate set path of the path segment 2 Will path 1 、path 2 Combined to form a descendant agent L i,j g ＝[path 1 ,path 2 ]；
(6c3) Randomly generated real numbers r between 0 and 1 2 If r is 2 ＞P m Let L i,j new ＝L i,j g Otherwise, performing step (6c 4);
(6c4) to L i,j g Carrying out mutation operator:
randomly generating a random integer N ← random (1, N-1) between 1 and N-1, obtaining L i,j g Middle (n-1) position coordinate (x) n-1 ,y n-1 ,z n-1 ) And n +1 position coordinates (x) n+1 ,y n+1 ,z n+1 ) Randomly generating 3 real numbers r between 0 and 1 x 、r y 、r z And calculating the coordinate of the variation position as (x) r ,y r ,z r )：
x r ＝x n-1 +r x (x n+1 -x n-1 )；
y r ＝y n-1 +r y (y n+1 -y n-1 )；
z r ＝z n-1 +r z (z n+1 -z n-1 )；
Mixing L with i,j g The middle nth position coordinate is reassigned to (x) r ,y r ,z r ) And make L i,j new ＝L i,j g 。
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