CN112148030B - Underwater glider path planning method based on heuristic algorithm - Google Patents
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/105—Simultaneous control of position or course in three dimensions specially adapted for aircraft specially adapted for unpowered flight, e.g. glider, parachuting, forced landing
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
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- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention relates to an underwater glider path planning method based on a heuristic algorithm, which comprises the following steps: the underwater environment is subjected to rasterization, namely, the underwater environment is divided into grids, environment modeling is carried out, a feasible region and an obstacle region are distinguished, the grids positioned in the feasible region are called as feasible grids, 0 is used for representing the feasible region, the grids positioned in the obstacle region are called as obstacle grids, and 1 is used for representing the feasible region and the obstacle region; initializing a population; performing cross operation on individuals in the population; performing mutation operation; calculating the fitness value of individuals in the population, and performing selection operation; after the crossing and the mutation are completed, calculating individual fitness values, and selecting reserved individuals by adopting a roulette mechanism, wherein the higher the fitness is, the easier the reserved individuals are; in addition, elite preservation is carried out before selection operation, namely, the individual with the highest adaptability in the population is selected and directly added into the next generation population, so that the optimal solution is ensured to exist all the time; and updating the pheromone concentration.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle autonomous control, and relates to an underwater glider path planning method based on a heuristic algorithm.
Background
Recently, as technology development and people's understanding of ocean importance are gradually deepened, many advanced devices and instruments are put into the field of ocean exploration and development, which is typically an underwater glider. Currently, underwater gliders are widely applied to the fields of marine resource exploration and the like, and more scientists and technicians begin to pay attention to and develop deep researches on the underwater gliders. Path planning technology is a key part of the underwater glider technology, which finds a viable path for the underwater glider to move from a starting node to a target node in an environment where obstacles are present. In fact, the path planning problem can be regarded as one of the hottest fields in various related research fields of robots, but unlike other robots, underwater gliders are under complex marine environments and are interfered by various complex environmental factors such as ocean currents, the path planning problem is more complex, and research on the path planning problem of the underwater gliders is still continued.
The heuristic algorithm can effectively and flexibly process the complex problem, so that the problem of path planning of the underwater glider can be solved, and the heuristic algorithm comprises a genetic algorithm, an ant colony algorithm, a simulated annealing method and a particle swarm algorithm. The heuristic algorithm can find the optimal solution of the problem according to the given constraint condition in a small-scale scene, but the heuristic algorithm is slow in convergence and is easy to converge in advance in a large-scale scene, the obtained solution cannot be guaranteed to be the global optimal solution, the search space of the algorithm is enlarged to improve the quality of the solution, the convergence speed is slower, and the algorithm efficiency is greatly reduced. Therefore, it is necessary to optimize the heuristic algorithm to solve the path planning problem of the underwater glider.
The fusion algorithm is one of effective ways for optimizing the heuristic algorithm, combines the characteristics of different heuristic algorithms, takes the search solution in different modes at different stages, and improves the possibility of finding the optimal solution while accelerating the convergence speed.
Disclosure of Invention
The invention provides a path planning method of an underwater glider, which can improve planning quality and accelerate searching speed, and the technical scheme is as follows:
an underwater glider path planning method based on heuristic algorithm comprises the following steps:
the method comprises the steps of firstly, rasterizing an underwater environment, namely dividing the underwater environment into grids for conveniently storing information of an environment area, carrying out environment modeling, distinguishing a feasible area from an obstacle area, wherein the grids positioned in the feasible area are called feasible grids, namely accessible grids, which are denoted by 0, the grids positioned in the obstacle area are called obstacle grids, which are denoted by 1, establishing a coordinate system in the grid area, each grid is denoted by unique coordinates, and determining grid positions of a starting point and an end point of path planning;
secondly, initializing a population: the gene node number corresponds to a certain grid, a plurality of genes are combined to form a chromosome, each chromosome corresponds to a path from a starting point to an end point, that is to say, the information stored in the chromosome of the genetic algorithm is the grid number of the environment model; when initializing, firstly adding the gene nodes corresponding to the initial point grids into the chromosome, then selecting eight grids around the last gene node in the chromosome in a circulating way, adding the selected adjacent grids into the last position in the chromosome until the end point grids are also added into the chromosome, completing the initialization of the chromosome, and then continuing the initialization of other chromosomes; then, defining the fitness value of the genetic algorithm individual as the reciprocal of the path length corresponding to the chromosome, so that the individual with high fitness is easy to be reserved in iteration;
thirdly, performing cross operation on individuals in the population: selecting two father chromosomes according to the crossing probability, generating two son chromosomes, checking whether repeated grid nodes exist in the son chromosomes, if so, the crossing is invalid, and re-selecting crossing points of the father chromosomes for crossing until the son chromosomes have no repeated grids; the crossing process of the two parent chromosomes is as follows: randomly selecting two nodes from all repeated grids of two father chromosomes as crossing points, but not selecting a starting point and an ending point, and then exchanging gene fragments between the two crossing points of the father chromosomes to generate two son chromosomes;
fourth, performing mutation operation: the chromosome is selected according to the mutation probability, the path from the mutation node to the end point is searched again, the influence of the concentration of pheromone in the grids is considered when the next node of the last grid is selected, and the probability formula of each feasible grid Ni is as follows:
τ (Ni) is the pheromone concentration within grid Ni, α is the pheromone concentration weight, η (Ni) is the heuristic factor, which is defined as the inverse of the distance of grid Ni to the endpoint, β is the heuristic factor weight;
fifthly, calculating the fitness value of individuals in the population, and performing selection operation; after the crossing and the mutation are completed, calculating individual fitness values, and selecting reserved individuals by adopting a roulette mechanism, wherein the higher the fitness is, the easier the reserved individuals are; in addition, elite preservation is carried out before selection operation, namely, the individual with the highest adaptability in the population is selected and directly added into the next generation population, so that the optimal solution is ensured to exist all the time;
sixthly, updating the concentration of the pheromones, and updating the concentration of the pheromones in all grids after the group initialization and each selection operation are completed;
and seventhly, repeating the third step to the sixth step until the algorithm reaches a preset maximum evolution algebra, and outputting an optimal solution.
The sixth step of updating the pheromone concentration may be as follows:
in the above formula, τ (N) G+1 ) The pheromone concentration in the grid numbered N for the G+1st generation, ρ is the volatility coefficient, τ (N G ) The concentration of pheromone in the grid numbered N for the G generation, K is the population individual number, delta tau i (N) is the increment of pheromone left by the ith individual of the G generation population in the grid with the number N, which is proportional to the individual fitness value fitness, and the constant Q is the total amount of pheromone which can be increased by one individual.
The genetic algorithm converges to the optimal solution at the early stage of the algorithm, and the convergence speed at the later stage is reduced, which is caused by the fact that the genetic algorithm does not fully utilize feedback information in the system. The ant colony algorithm has slow evolution speed in the early stage, which is caused by the lack of pheromone in the early stage, the pheromone gradually accumulates with the increase of the evolution algebra, and the convergence speed of the ant colony algorithm to the optimal solution gradually rises. Aiming at the problems of the two traditional heuristic algorithms, the invention provides a fusion algorithm for fusing a genetic algorithm and an ant colony algorithm to solve the problem of path planning of the underwater glider. The invention solves the problem of path planning of the underwater glider by utilizing the fusion algorithm of the genetic algorithm and the ant colony algorithm, and the fusion algorithm improves the quality of the final planning result and accelerates the searching speed.
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FIG. 1 is a flow chart of the scheme
Detailed Description
The patent fuses the genetic algorithm and the ant colony algorithm, and the purpose is to improve the quality of the obtained solution while improving the convergence speed of the path planning algorithm. The core ideas of the ant colony algorithm are introduced into mutation operators of the genetic algorithm, so that fusion of the two heuristic algorithms is realized, the genetic algorithm overcomes the defect of slow evolution caused by the lack of pheromone in the early stage of the ant colony algorithm, the ant colony algorithm overcomes the defect that the genetic algorithm cannot fully utilize feedback information in the system to gradually reduce the convergence speed of the ant colony algorithm to the optimal solution, and the performance of the fusion algorithm is superior to that of the independent heuristic algorithm.
The preferred embodiments of the present invention are as follows:
the first step: and carrying out rasterization processing on the underwater environment to complete environment modeling, and determining grid positions where the starting point and the end point of the planned path are located.
And a second step of: when the population is initialized, the algorithm firstly adds the starting point into the chromosome, then circularly selects eight grids around the last gene node (the number corresponds to a certain grid) in the chromosome, the selected adjacent grids are added into the chromosome, the end point grids are known to be added into the chromosome, and the initialization of one chromosome is completed. Then the fitness value of the genetic algorithm individual is defined as the reciprocal of the path length, so that the shorter the path length is, the higher the fitness value is, the more the individual can be reserved
And a third step of: and performing crossing operation, namely selecting a father chromosome to be crossed according to a set crossing probability by adopting a double-point crossing mechanism, wherein the two father chromosomes can generate two son chromosomes, selecting the same gene points (corresponding to the same grid) in the two father chromosomes as crossing points during crossing, and exchanging gene segments between the crossing points in the two father chromosomes to generate new son chromosomes. In addition, to detect the generated sub-chromosomes, if repeated grids exist in the sub-chromosomes after crossing, the crossing is invalid, and the algorithm reselects the crossing points to perform crossing operation until the sub-chromosomes pass the detection, so that the generation of bad paths is avoided.
Fourth step: and performing mutation operation, and selecting chromosomes to be mutated according to the set mutation probability. The algorithm randomly selects one gene point except the starting point and the end point from the chromosome as a variation node, and the chromosome re-searches the path from the variation node to the end point in a similar initialization mode. The probability that the surrounding eight grids are selected is:
where Ni is the number of the feasible grids around the current grid, where the feasible grids are defined as non-obstacle, non-exceeding the boundary of the environmental model and currently in the chromosome and not added, τ (Ni) is the pheromone concentration within the grid Ni, α is the pheromone concentration weight, η (Ni) is the heuristic factor, which is defined as the inverse of the distance of the grid Ni to the endpoint, and β is the heuristic factor weight.
Fifth step: the selection operation is carried out, new individuals generated by the crossover operation and the mutation operation in the algorithm are added into the population, so that the population scale is enlarged beyond the set population individual number, the selection operation selects the individuals with the set population individual number from the enlarged population as a new generation population, a roulette mechanism is adopted, and the probability that the individuals with higher fitness are selected is higher, so that the algorithm is prevented from converging early maturing. In addition, we introduce elite retention mechanism, select the most adaptable individuals in the population before roulette selection and directly add them to the next generation population, avoiding the loss of the optimal solution by the roulette mechanism.
Sixth step: the pheromone concentration is updated after the population initialization and each selection operation is completed, and the update formula is as follows:
where τ (N) G+1 ) The pheromone concentration in the grid numbered N for the G+1st generation, ρ is the volatility coefficient, τ (N G ) The concentration of pheromone in the grid numbered N for the G generation, K is the population individual number, delta tau i (N) pheromone delta left by the ith individual of the G generation population in the grid numbered N, the definition of the pheromone delta being:
where Q is the total amount of pheromones that an individual can increase, fitness is the fitness value of the individual, and it is known from (2) that the increment of pheromones produced by an individual is inversely proportional to the path length of the correspondence, that is, the shorter the path length, the thicker the pheromones left on it.
Seventh step: judging whether the evolution algebra reaches the set maximum evolution algebra, if not, returning to the third step, and if so, outputting a path planning scheme corresponding to the optimal chromosome (the chromosome with the highest fitness value) of the generation as a final result.
Claims (2)
1. An underwater glider path planning method based on heuristic algorithm comprises the following steps:
the method comprises the steps of firstly, rasterizing an underwater environment, namely dividing the underwater environment into grids for conveniently storing information of an environment area, carrying out environment modeling, distinguishing a feasible area from an obstacle area, wherein the grids positioned in the feasible area are called feasible grids, namely accessible grids, which are denoted by 0, the grids positioned in the obstacle area are called obstacle grids, which are denoted by 1, establishing a coordinate system in the grid area, each grid is denoted by unique coordinates, and determining grid positions of a starting point and an end point of path planning;
secondly, initializing a population: the gene node number corresponds to a certain grid, a plurality of genes are combined to form a chromosome, each chromosome corresponds to a path from a starting point to an end point, that is to say, the information stored in the chromosome of the genetic algorithm is the grid number of the environment model; when initializing, firstly adding the gene nodes corresponding to the initial point grids into the chromosome, then selecting eight grids around the last gene node in the chromosome in a circulating way, adding the selected adjacent grids into the last position in the chromosome until the end point grids are also added into the chromosome, completing the initialization of the chromosome, and then continuing the initialization of other chromosomes; then, defining the fitness value of the genetic algorithm individual as the reciprocal of the path length corresponding to the chromosome, so that the individual with high fitness is easy to be reserved in iteration;
thirdly, performing cross operation on individuals in the population: selecting two father chromosomes according to the crossing probability, generating two son chromosomes, checking whether repeated grid nodes exist in the son chromosomes, if so, the crossing is invalid, and re-selecting crossing points of the father chromosomes for crossing until the son chromosomes have no repeated grids; the crossing process of the two parent chromosomes is as follows: randomly selecting two nodes from all repeated grids of two father chromosomes as crossing points, but not selecting a starting point and an ending point, and then exchanging gene fragments between the two crossing points of the father chromosomes to generate two son chromosomes;
fourth, performing mutation operation: the chromosome is selected according to the mutation probability, the path from the mutation node to the end point is searched again, the influence of the concentration of pheromone in the grids is considered when the next node of the last grid is selected, and the probability formula of each feasible grid Ni is as follows:
τ (Ni) is the pheromone concentration within grid Ni, α is the pheromone concentration weight, η (Ni) is the heuristic factor, which is defined as the inverse of the distance of grid Ni to the endpoint, β is the heuristic factor weight;
fifthly, calculating the fitness value of individuals in the population, and performing selection operation; after the crossing and the mutation are completed, calculating individual fitness values, and selecting reserved individuals by adopting a roulette mechanism, wherein the higher the fitness is, the easier the reserved individuals are; in addition, elite preservation is carried out before selection operation, namely, the individual with the highest adaptability in the population is selected and directly added into the next generation population, so that the optimal solution is ensured to exist all the time;
sixthly, updating the concentration of the pheromones, and updating the concentration of the pheromones in all grids after the group initialization and each selection operation are completed;
and seventhly, repeating the third step to the sixth step until the algorithm reaches a preset maximum evolution algebra, and outputting an optimal solution.
2. The method according to claim 1, wherein the sixth step of pheromone concentration updating method is as follows:
in the above formula, τ (N) G+1 ) The pheromone concentration in the grid numbered N for the G+1st generation, ρ is the volatility coefficient, τ (N G ) The concentration of pheromone in the grid numbered N for the G generation, K is the population individual number, delta tau i (N) the increment of pheromone left by the ith individual of the G generation population in the grid numbered N, which is proportional to the individual fitness value fitness, the constant Q is oneThe individual can increase the total amount of pheromones.
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