CN111273686A - Path planning method for multiple unmanned aerial vehicles to simultaneously reach designated place in three-dimensional environment - Google Patents
Path planning method for multiple unmanned aerial vehicles to simultaneously reach designated place in three-dimensional environment Download PDFInfo
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Abstract
The invention discloses a path planning method for multiple unmanned aerial vehicles to reach a designated place simultaneously in a three-dimensional environment, which comprises the steps of firstly establishing a population, wherein a chromosome represents a comprehensive flight path, and the comprehensive flight path is formed by connecting the flight paths of all the unmanned aerial vehicles in series; setting the fitness of a genetic algorithm, and comprehensively evaluating the violation degree W of the set maneuvering performance constraint of the unmanned aerial vehicle by the total track length L of all unmanned aerial vehicles, the difference degree T of the arrival time of different unmanned aerial vehicles and the track; and optimizing the chromosome by adopting a genetic algorithm, and obtaining the optimal chromosome after completing genetic algorithm iteration. And obtaining the flight path of each unmanned aerial vehicle by adopting optimal chromosome decomposition. The invention can solve the problem of aggregation of multiple unmanned aerial vehicles and realize the technical goal of simultaneously reaching task points, and simultaneously reduces time errors as much as possible.
Description
Technical Field
The invention relates to the technical field related to unmanned aerial vehicle path planning and control, in particular to a path planning method for multiple unmanned aerial vehicles to simultaneously arrive at a designated place in a three-dimensional environment.
Background
Unmanned aerial vehicle has characteristics such as flexibility height, with low costs, security height and disguise are strong, and these peculiar superior performances make unmanned aerial vehicle technique rapid development, become the representative technique of world leading edge science and technology gradually, by fields such as wide application in various rescue after calamity, city express delivery, topography exploration, environmental monitoring. The flight path planning of the unmanned aerial vehicle is an important technology which influences the task efficiency of the unmanned aerial vehicle; the flight path planning refers to planning an optimal flight path from a starting point to a target point on the premise of considering environmental constraints and unmanned aerial vehicle maneuvering performance constraints, the optimal flight path planning can reduce the total cost of the unmanned aerial vehicle in the whole flight path to the minimum, balance the importance among various constraints, minimize the flight path length on the basis, reduce the fuel cost of the unmanned aerial vehicle and improve the cruising ability and the task completion rate of the unmanned aerial vehicle to the maximum extent; due to the defects of thin potential, small operation range, low fault tolerance rate and the like, a single unmanned aerial vehicle is often difficult to meet the requirement of a complex task, so that the cooperative operation of multiple unmanned aerial vehicles is more and more applied to real life; when a single unmanned aerial vehicle executes a task, if sudden failures occur, the task fails directly; when multiple unmanned aerial vehicles work cooperatively, if a certain unmanned aerial vehicle loses control, other unmanned aerial vehicles in the cluster still continue to execute tasks, the completion rate of the tasks is greatly improved, and the multiple unmanned aerial vehicles cluster is more suitable for complex and changeable real environments than a single unmanned aerial vehicle, so that the multiple unmanned aerial vehicles can become an important cooperative work style in future production and life. Therefore, the cooperative operation of multiple unmanned aerial vehicles has become an important research direction;
the problem of path planning of multiple unmanned aerial vehicles to reach a destination simultaneously in a three-dimensional environment is that a feasible and safe path capable of reaching the target position simultaneously is planned for each unmanned aerial vehicle respectively by optimizing certain performance indexes on the premise of meeting certain constraint conditions (collision avoidance among the multiple unmanned aerial vehicles, obstacle avoidance, simultaneous arrival at a target point and dynamics characteristics of the unmanned aerial vehicles); the path planning technology has very typical application value in the aspects of rescue and relief work, data exchange, tracking and monitoring and the like of the aggregation of multiple unmanned aerial vehicles;
in the prior art, collaborative path planning of an unmanned aerial vehicle in a three-dimensional environment mostly focuses on establishment of a path generated by a geometric method or a collaborative motion model, and the application background is lacked when the unmanned aerial vehicle in the three-dimensional environment simultaneously reaches: sahingo et al propose a multi-UAV flight path planning method based on Genetic Algorithm (GA), first calculate a feasible path with GAs method, then utilize Bezier curve to carry on the smoothing treatment to the path; madheavan Shanmugavel et al studied the collaborative path planning problem for a group of drones, using the dubin path with a clothoid arc to generate a path for each drone; christoph Rasche et al propose a multi-unmanned aerial vehicle path planning method in a three-dimensional environment, and provide a solution for exploring disaster areas; the invention provides a novel and practical multi-unmanned aerial vehicle collaborative path planning scheme by combining unmanned aerial vehicle dynamics constraint, safety constraint and terrain condition constraint mainly aiming at a specific application scene that multiple unmanned aerial vehicles arrive at a target point at the same time.
Disclosure of Invention
In order to solve the technical goals that multiple unmanned aerial vehicles are aggregated and arrive at a task point at the same time, and reduce time errors as much as possible, the invention provides a path planning method for multiple unmanned aerial vehicles to arrive at a designated place at the same time in a three-dimensional environment based on a genetic algorithm, so that the flight cost can be minimized and the optimal flight path from a starting point to a target point can be found on the premise of meeting the constraint.
In order to solve the technical problem, the invention is realized as follows:
a path planning method for multiple unmanned aerial vehicles to reach a designated place simultaneously in a three-dimensional environment adopts a genetic algorithm to realize path planning, and comprises the following steps:
step1, establishing a population: each chromosome in the population corresponds to a solution; the chromosome represents a comprehensive track, and the comprehensive track is formed by connecting tracks of all unmanned aerial vehicles in series; the track consists of a series of discrete track points, and the three-dimensional coordinates of the track points form gene codes in the chromosome;
step2, setting the fitness of a genetic algorithm, and comprehensively evaluating the violation degree W of the set maneuvering performance constraint of the unmanned aerial vehicle by the total track length L of all unmanned aerial vehicles, the difference degree T of the arrival time of different unmanned aerial vehicles and the track;
step3, optimizing chromosomes by adopting a genetic algorithm; when the intersection operation of the genetic algorithm is carried out, only the waypoints belonging to the same unmanned aerial vehicle are allowed to intersect; obtaining an optimal chromosome after genetic algorithm iteration is completed;
and 4, obtaining the flight path of each unmanned aerial vehicle by adopting optimal chromosome decomposition.
Preferably, in step3, when executing the genetic algorithm, the population initialization process is as follows:
aiming at each unmanned aerial vehicle i, in a two-dimensional plane, starting point A corresponding to the unmanned aerial vehicle iiThe connecting line of the target point B common to all the unmanned aerial vehicles is a symmetrical middle line and draws a rectangle which is a flight path planning area of the unmanned aerial vehicle i; according to the number N of track points set for unmanned aerial vehicle iiStarting point AiThe connecting line between the target point B is equally divided into NiDividing the planning region into NiEach small rectangle is a planning area of a corresponding single track point; when a track point is initialized, randomly generating an initial value of the track point in a planning area corresponding to the track point under the constraint of a flyable environment to obtain a coordinate of the track point under a two-dimensional plane, and randomly generating a height value of the track point under the constraint of the flyable environment to obtain an initial three-dimensional coordinate of the track point; according to the starting point AiSequential generation of N to target point BiThe initial three-dimensional coordinates form an initial track of the unmanned aerial vehicle i; the initial tracks of all unmanned aerial vehicles are connected in series to obtain an initial chromosome; and generating a plurality of initial chromosomes to finish the initialization of the population.
Preferably, in the step3, when performing a mutation operation of the genetic algorithm, for a track point to be mutated, a new track point is randomly generated again in the planning region corresponding to the track point.
Preferably, the step3 further performs a perturbation operation after the mutation operation when the genetic algorithm is executed; the perturbation operation is only effective for each track point except the starting point and the target point;
the perturbation operation comprises: determining whether the chromosome executes perturbation operation according to the set perturbation probability; if the execution is needed, selecting part of disturbance points from the track points except the starting point and the target point according to the disturbance proportion to apply micro-disturbance; the disturbance amount of the micro-disturbance is 1% -5% of the original value of the track point; calculating the fitness of the new chromosome after the chromosome is disturbed, and replacing the chromosome before disturbance with the chromosome after disturbance if the fitness is greater than the fitness before disturbance; otherwise, no replacement is performed.
Preferably, the perturbation probability is 1% -5%.
Preferably, the applying perturbations are: the disturbance to the feasible track is small, and the disturbance to the non-feasible track is large; the feasible flight path is a flight path which meets the maneuvering performance constraint of the unmanned aerial vehicle, and if the feasible flight path does not meet the maneuvering performance constraint, the feasible flight path is an infeasible flight path.
Preferably, the step3 uses the adaptive crossover probability P when performing crossover and mutation operations of the genetic algorithmcAnd adaptive mutation probability Pm：
Wherein k is1，k2Determining the maximum value of the cross probability and the mutation probability respectively as a constant; f. ofmaxRepresents the maximum fitness of the chromosome within the current population, favgRepresenting the mean fitness of the current population, fcDenotes the fitness of the chromosome currently requiring crossover operation, fmRepresenting the fitness of the chromosome which needs mutation operation at present; k is a radical of3And k is4Is a set constant.
Preferably, the step2 is: for each chromosome, three fitness components E1, E2, E3 are calculated, including:
calculating the track length of each unmanned aerial vehicle, and summing to obtain L; the shorter L, the larger the fitness component E1;
calculating the time of each unmanned aerial vehicle flying to a target point according to the track point; the difference between the longest time and the shortest time is the maximum difference T of the arrival times of different unmanned aerial vehicles; the smaller the maximum difference T is, the larger the fitness component E2 is;
calculating whether the actual maneuvering performance of each unmanned aerial vehicle during the flying process according to the track points violates the corresponding maneuvering performance constraint of the unmanned aerial vehicle; for a violater, acquiring each constraint violation component, respectively normalizing all the constraint violation components, and then weighting and summing to obtain the constraint violation of a single unmanned aerial vehicle; weighting and summing constraint violations of all unmanned aerial vehicles to obtain total violations; if all the unmanned aerial vehicles do not violate the constraint, the total violation amount adopts a set value; the smaller the total violation, the larger the fitness component E3;
and normalizing the three fitness components E1, E2 and E3, and then weighting and summing to obtain the fitness of the chromosome.
Preferably, after the tracks of the unmanned aerial vehicles are obtained, the unmanned aerial vehicles are further connected by using a three-dimensional Dubins curve to generate a flyable path of the unmanned aerial vehicle.
Has the advantages that:
(1) the invention combines the flight path optimization problems of multiple unmanned aerial vehicles into the planning problem of the comprehensive path, and designs a fitness calculation method suitable for planning the comprehensive path, thereby obtaining the path which has the minimum flight cost and the minimum constraint violation quantity and can arrive at the same time.
(2) The invention improves the selection schemes of an initialization method, a cross scheme and a variation scheme. The method has the advantages that by adopting a special initialization method, track points are strictly generated from the starting point to the target point in sequence, redundant flight is avoided, the track quality is higher, the search space is greatly reduced, and the search efficiency of the algorithm is obviously improved; the cross operation is applied to tracks of different unmanned aerial vehicles respectively, so that the cross between the different unmanned aerial vehicles is avoided; the variation operation is completed in a planning area corresponding to the waypoint, and the deterioration caused by the variation is reduced.
(3) The invention adds a perturbation operation. Disturbance enables the non-feasible flight path to evolve towards the feasible direction, enables the feasible flight path to evolve towards the direction with lower cost, plays a role in correcting and correcting the flight path, can promote generation of new chromosomes, expands the range of the population and enhances the diversity of the population.
(4) The invention also increases a self-adaptive updating scheme of the cross mutation probability, so that the chromosome with the highest fitness has certain cross and mutation probability, the diversity of the population can be better kept, and the premature and local convergence are prevented.
Drawings
FIG. 1 is a schematic diagram of a maximum yaw constraint;
FIG. 2 is a mountain peak modeling effect diagram;
FIG. 3 is a schematic view of a no-fly zone range;
FIG. 4 is an environmental global modeling effect diagram;
FIG. 5 is a schematic representation of gene coding;
FIG. 6 is a schematic view of track initialization;
FIG. 7 is a flow chart of a genetic algorithm based track planning algorithm;
FIG. 8 is a schematic diagram of a three-dimensional Dubins path;
FIG. 9 is a schematic diagram of three drones generating equal-length Dubins paths;
fig. 10 is a schematic diagram of paths of three unmanned aerial vehicles in terrain with obstacles and no-fly zones.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention is explained in detail below with reference to the drawings and examples;
the invention provides a path planning method for multiple unmanned aerial vehicles to arrive at a designated place simultaneously in a three-dimensional environment, which combines the flight path optimization problem of the multiple unmanned aerial vehicles into the planning problem of a comprehensive path and designs a fitness calculation method suitable for the comprehensive path planning, thereby obtaining the path which has the advantages of minimum flight cost, minimum constraint violation quantity and capability of arriving simultaneously.
The path planning method for multiple unmanned aerial vehicles to simultaneously reach the designated place in the three-dimensional environment comprises the following specific steps:
step1, establishing various constraint and fitness function evaluation schemes.
The constraints include unmanned aerial vehicle maneuvering performance constraints and flight three-dimensional environment constraints. In this embodiment, unmanned aerial vehicle mobility performance constraint includes: maximum yaw angle constraint, maximum pitch angle constraint, fly height constraint, and maximum flight distance constraint. As shown in fig. 1, the maximum yaw angle constraint is that an included angle between projection vectors of adjacent track segments in the horizontal ground OXY is smaller than the maximum yaw angle θ, and the specific size of the angle is determined according to the performance of the unmanned aerial vehicle. The maximum pitch angle constraint is that the included angle between the track section and the projection of the track section on the horizontal plane OXY is smaller than the maximum pitch angle
The specific size of the angle is determined according to the performance of the unmanned aerial vehicle. The flying height of the unmanned aerial vehicle is too low, and the risk coefficient of the unmanned aerial vehicle hitting a mountain and falling to the ground is increased. Therefore, the minimum flying height of the unmanned aerial vehicle needs to be limited, and the safety of the unmanned aerial vehicle capable of flying is guaranteed. The maximum flight distance constraint of the unmanned aerial vehicle is to ensure that the unmanned aerial vehicle cannot run out of energy sources in the task execution process.
Modeling a flight three-dimensional environment: as shown in fig. 2,3, and 4, the method mainly includes mountain terrain modeling and no-fly zone modeling to simulate the actual flight environment of the unmanned aerial vehicle. The no-fly zone can be represented as a reasonably smooth type distribution model.
The fitness function evaluation mode is expressed by three fitness components E1, E2 and E3. The total track length L of all the unmanned aerial vehicles, the difference T of the arrival time of different unmanned aerial vehicles and the violation degree W of the track on the set maneuvering performance constraint of the unmanned aerial vehicles are considered. The shorter the total track length L is, the larger the corresponding fitness component E1 is; the smaller the difference T of the arrival time of different unmanned aerial vehicles is, the larger the corresponding fitness component E2 is; the smaller the violation degree wstotals of the unmanned aerial vehicle maneuvering performance constraints are, the larger the fitness component E3 is.
Step 2: and running a multi-unmanned aerial vehicle track planning algorithm based on a genetic algorithm. The main process of the multi-unmanned aerial vehicle flight path planning algorithm is the same as that of the conventional genetic algorithm, and the multi-unmanned aerial vehicle flight path planning algorithm is improved aiming at a chromosome construction scheme, an initialization method, a cross scheme, a variation scheme, a selection scheme of cross variation probability and a fitness calculation scheme. Meanwhile, a perturbation step is added. As shown in the flow chart of fig. 7, the improved genetic algorithm comprises the following steps:
step 1: initializing the flight path according to a specific initialization method to generate an initial population; step 2: calculating fitness values of all chromosomes in the population; step 3: selecting a certain number of chromosomes to form a breeding pool by adopting a championship selection strategy according to the fitness; step 4: calculating the maximum fitness and the average fitness in the breeding pool, determining the crossing and mutation probability of each chromosome, and carrying out crossing and mutation operation on the chromosomes in the breeding pool according to the probability. Step 5: and (4) carrying out disturbance operation on the chromosomes in the breeding pool, comparing the fitness of the new chromosomes with that of the original chromosomes, and reserving the chromosomes with high fitness. Step 6: judging whether the maximum iteration times is reached, if so, turning to Step8, otherwise, turning to the next Step; step 7: emptying the breeding pond, and transferring to Step 2; step 8: and selecting the chromosome with the maximum fitness in the breeding pond as the optimal track.
The following describes in detail the improved parts of the present application, including chromosome construction schemes, initialization methods, crossover schemes, variation schemes, selection schemes of crossover variation probability, fitness calculation schemes, and perturbation operations.
(1) The chromosome construction scheme comprises the following steps: each chromosome in the population represents a solution to the problem, a solution to the problem consisting of the flight trajectories of a plurality of drones together, that is to say each chromosome represents a synthetic trajectory formed by the concatenation of the trajectories of all drones, each flight trajectory consisting of a series of discrete trajectory points. For example, 3 unmanned aerial vehicle tracks all include 3 track points, then the comprehensive track point after establishing ties includes D that connects gradually11、D12、D13、D21、D22、D23、D31、D32、D33。DijRepresenting the jth track point of drone i.
The encoding of the gene is the encoding of the track point. Each chromosome is encoded by using a fixed-length real-valued gene encoding mode, genes of the chromosomes correspond to track points, and the gene encoding is a three-dimensional coordinate value of the track points, as shown in fig. 5.
(2) Population initialization: the flight path planning under the three-dimensional environment has a large search space, the problems of low search efficiency, long planning time and the like can be caused, and in order to reduce the search space and simultaneously not reduce the quality of an optimal solution as much as possible, a special initialization method is adopted for initializing the population.
Randomly generating track points in an area divided according to a certain rule, and then performing variation operation on the track points in a specified area; aiming at each unmanned aerial vehicle i, in a two-dimensional plane, starting point A corresponding to the unmanned aerial vehicle iiThe line connecting the target point B and the target point B is a symmetrical middle line and draws a rectangle, and the length of the rectangle is equal to the length of the starting point AiThe width of the rectangle may be a set value, preferably equal to the starting point A, as the distance to the target point BiThe distance to the target point B is large. This rectangle is the flight path planning region for drone i. According to the precision required by the path, setting the number N of track points for the unmanned aerial vehicle ii. The number of tracks of different drones may be different or the same. Starting point AiThe line connecting B with the target point is equally divided into NiOn the basis of which the planning region is equally divided into NiAnd small rectangles, wherein each small rectangle is a planning area of a corresponding single track point. When the track point is initialized, under the constraint of a three-dimensional flying environment (namely under the constraint of a flyable range), each track point is randomly generated in a corresponding planning area to obtain the coordinate of the track point under a two-dimensional plane, and then the height value of the track point is randomly generated in the flyable range to obtain the three-dimensional coordinate of the track point as an initial three-dimensional coordinate. According to the starting point AiOrder to target point B, from NiGenerating a total of N in small rectanglesiEach initial three-dimensional coordinate constitutes an initial trajectory of drone i. And generating an initial track for each unmanned aerial vehicle, and splicing to obtain an initial chromosome. Obtaining a plurality of initial chromosomes to complete the populationInitialization of (2). Initializing the track in this way requires the use of a coordinate transformation.
As shown in fig. 6, on the basis of the ground plane coordinate system, a new coordinate system is established by taking the starting point S as an origin, taking the direction of the starting point toward the target point G as the x-axis direction, and taking the x-axis rotated counterclockwise by 90 ° as the y-axis. According to the distance length between the starting point and the target point and the number of the track points, the random range of the coordinate value of the horizontal axis of each track point can be determined, the length of the rectangular planning area of the track point is determined, and therefore the random range of the coordinate value of the vertical axis of the track point can be known. After the coordinates of each track point under a newly established two-dimensional coordinate system are determined by using a random function, coordinate transformation is carried out to obtain the coordinates of the track points under a ground plane coordinate system, and the terrain height of the position of each track point can be obtained according to peak terrain information stored in peak modeling, so that the track points randomly generate an elevation value in a height range where the unmanned aerial vehicle can safely fly, and certain environmental constraints are met. If the track points are randomly generated in the whole planning space, the whole track after the track points are connected is disordered, and the initialization method used by the application enables the track points to be sequentially generated from the starting point to the target point strictly, so that redundant flight is avoided, the track quality is higher, the search space is greatly reduced, and the search efficiency of the algorithm is obviously improved.
(3) Designing a fitness function: in the genetic algorithm, fitness is the only standard for judging the chromosome quality of a population, and all judgment information of chromosomes, including constraint conditions and track cost functions, is contained. And on the premise of meeting the constraint condition, the planned flight path is the effective flight path. Chromosome fitness should include the following three indicators:
① the total track length L of the drone is as short as possible;
② the maximum difference T between the times required for different drones to reach the target point is as small as possible;
③ the amount of violation W of the set drone maneuver performance constraints is as small as possible.
As mentioned above, the shorter the total track length L is, the larger the corresponding fitness component E1 is; the smaller the difference T of the arrival time of different unmanned aerial vehicles is, the larger the corresponding fitness component E2 is; the smaller the violation degree wstotals of the unmanned aerial vehicle maneuvering performance constraints are, the larger the fitness component E3 is.
Considering the above factors comprehensively, the fitness function F is designed by using the simplest corresponding relationship, i.e. reciprocal relationship, and can be expressed as:
wherein, wi(i-1, 2,3) is a weight value of each performance index. The three components in the fitness function F correspond to three fitness components E1, E2, E3 in sequence. E1, E2 and E3 are also normalized before calculation. In practice, other corresponding relations can be adopted to realize the correspondence that the smaller the L/T/W is, the larger the E is.
Here, a calculation method of L, T, W is explained:
l is used for realizing the evaluation to unmanned aerial vehicle total track length. Calculating the track length of each unmanned aerial vehicle and summing the track lengths, i.e.
Where n is the number of unmanned aerial vehicles, liIs the flight path length of the ith unmanned aerial vehicle.
T is used for evaluating whether the unmanned aerial vehicles can reach the target area simultaneously. Calculating the time of each unmanned aerial vehicle flying to the target point according to the track point, wherein T is max (T)i)-min(ti) I is 1,2 … n. Wherein, tiRepresents the time required for the ith unmanned aerial vehicle to reach the target point, max (t)i) Indicates that all unmanned aerial vehicles adopt time tiMaximum value of (d), min (t)i) Indicates that all unmanned aerial vehicles adopt time tiIs measured.
W is used to represent the magnitude of the flight path violation constraint. Calculating whether the actual maneuvering performance of each unmanned aerial vehicle during the flying process according to the track points violates the corresponding maneuvering performance constraint of the unmanned aerial vehicle; for a violater, acquiring each constraint violation component, respectively normalizing all the constraint violation components, and then weighting and summing to obtain the constraint violation of a single unmanned aerial vehicle; weighting and summing constraint violations of all unmanned aerial vehicles to obtain total violations; and if all the unmanned aerial vehicles do not violate the constraint, adopting a set value for the total violation amount. The evaluation of the track segment violating the constraint condition is realized,
wherein, WiThe constraint violation amount of the ith unmanned aerial vehicle. Here, a direct summation is adopted, and preferably, a weighted summation can be adopted to obtain the total violation amount W. μ is a constant, and Δ cos, Δ tan, and Δ h are all constraint violations components as exemplified in the present embodiment, where a normalized direct summation manner is used, and preferably, a weighted summation manner can be used. And delta cos represents the violation amount of the track on the maximum yaw angle, the cosine value of the yaw angle between every two adjacent track sections is calculated and is compared with the cosine value of the maximum yaw angle, if the constraint is violated, the difference is made between the two values to obtain the violation amount of the yaw angle of the track section, delta tan represents the violation amount of the track on the maximum pitch angle, the sine value of the pitch angle of each track section is calculated and is compared with the sine value of the maximum pitch angle, and if the constraint is violated, the difference is made between the two values to obtain the violation amount of the pitch angle of the track section. And delta h represents the violation amount of the track on the terrain environment, describes the degree of the track blocked by the terrain, samples the track section, and determines the violation amount of each sampling point by calculating the difference between the height of the sampling track point and the height of the peak. These constraint violation components are averaged to represent the amount of violation of the terrain environment for the entire track segment.
Different constraint violations have different orders of magnitude, so that factors with smaller orders of magnitude cannot influence function values, and the optimization effect of the part on the flight path planning is lost, so that the index factors and the constraint violations need to be normalized and converted into the same order of magnitude. All normalization schemes in the present invention can be appliedTo normalize the scheme with the maximum lowest value. By track length liFor the purpose of example only,
wherein l0Is the actual track length before normalization, lminIs the minimum track length, l, of all track chromosomes within a generationmaxThe maximum flight path length in all flight path chromosomes in the generation, l is the flight path length after normalization processing, and the normalization principle of each parameter is the same. The normalization method does not select a fixed maximum value and a fixed minimum value, because as algebra increases, the flight path gradually evolves towards the direction with small constraint violation, short path length and low threat cost, and the fixed maximum value and the fixed minimum value cause the normalization effect to be worse as time goes on and the iteration times increase. Therefore, the index factors and the constraint violations of the track cost are normalized by selecting the maximum value and the minimum value in the current generation, and the evolution process of the track can be better adapted.
(4) And (5) improvement of a crossover operator. The specific operation mode aiming at the flight path planning problem is that every two flight path chromosomes in the propagation pool are paired randomly, whether each pair of flight paths carry out cross operation or not is determined according to preset cross probability, and the cross operation is carried out on the flight path pairs needing to be crossed to obtain two new flight path chromosomes. Because a chromosome comprises many unmanned aerial vehicle's flight path jointly, so the crossover operation will be used respectively on the flight path of different unmanned aerial vehicle, avoids crossing between the different unmanned aerial vehicle. Therefore, the invention needs to be limited to allow only the waypoints belonging to the same drone to intersect when the intersection operation of the genetic algorithm is performed.
(5) And (5) mutation operators. The specific operation mode aiming at the flight path planning problem is as follows: and for each chromosome in the population, determining whether to perform mutation operation according to the preset mutation probability. For the selected chromosome, a certain track point, namely a variation point, is randomly selected, and the improvement point of the invention is that a new track point is randomly generated again in a planning area (namely a small rectangle) corresponding to the track point to replace the original track point to complete gene variation. Similarly, the same chromosome is required to perform variation on the tracks represented by different unmanned aerial vehicles respectively.
(6) And (5) disturbing an operator. The use of the perturbation operator follows the mutation operation. The perturbation operator only works for every track point except the start and target points. The disturbance operation is specifically as follows: and determining whether the chromosome carries out perturbation operation or not according to the set perturbation probability, wherein the perturbation probability is set to be a small probability, so that the population diversity is increased, the infeasible solution is improved, and the small probability can be selected from 1-5%. And if the micro-disturbance is judged to be needed, selecting part of disturbance points from the track points except the starting point and the target point according to the disturbance proportion to apply micro-disturbance. The perturbation may be applied in such a way that the same amount of perturbation is applied for the coordinates of each direction. The disturbance amount can also be determined according to whether the flight path is feasible or not, the disturbance on the feasible flight path is small, and the disturbance on the non-feasible flight path is large; the feasible flight path is a flight path which meets the maneuvering performance constraint of the unmanned aerial vehicle, and if the feasible flight path does not meet the maneuvering performance constraint, the feasible flight path is an infeasible flight path. The perturbation quantity of the micro-perturbation can be set to be 1% -5% of the original value of the track point. Calculating the fitness of the new chromosome after the chromosome is disturbed, and replacing the original chromosome with the new chromosome if the fitness is greater than the fitness before the disturbance; if the fitness of the chromosome after disturbance is lower than that before disturbance, the original chromosome is reserved and is not replaced. Disturbance enables the non-feasible flight path to evolve towards the feasible direction, enables the feasible flight path to evolve towards the direction with lower cost, plays a role in correcting and correcting the flight path, can promote generation of new chromosomes, expands the range of the population and enhances the diversity of the population.
(7) Improvement of genetic operators. The genetic algorithm has the defect of premature convergence easily caused by the loss of diversity of the population, and in order to overcome the defect, the self-adaptive genetic algorithm is adopted for flight path planning, a common probability calculation method is improved, the cross probability and the variation probability can be adjusted according to the self-fitness and the population average fitness, and the premature convergence is prevented. Thus, when performing crossover and mutation operations of a genetic algorithm, an adaptive crossover probability P is usedcAnd adaptive mutation probability PmThe following were used:
wherein, PcTo adapt the cross probability, PmTo adapt the mutation probability, k1，k2The maximum values of the crossover and mutation probabilities are determined as constants, respectively. f. ofmaxRepresents the maximum fitness of the chromosome within the current population, favgRepresenting the mean fitness of the current population, fcDenotes the fitness of the chromosome currently requiring crossover operation, fmRepresenting the fitness of the chromosome which needs mutation operation at present; pcAnd PmAdding k on the original basis3And k is4The chromosomes with the highest fitness also have certain crossover and mutation probability, so that the diversity of the population can be better kept, and premature and local convergence are prevented. If k is removed3And k is4When f isc＝fmaxWhen is, Pc、PmAnd 0, if so, at the initial stage of evolution, the chromosomes with the highest fitness hardly have variation, generations are preserved, and thus the population is converged quickly because the strongest individual has great influence on the direction of evolution of the population. Plus k3And k is4The problem that the maximum fitness individual does not have cross variation can be avoided.
And step 3: the optimal chromosome obtained through the genetic algorithm of the step2 contains the flight tracks of all the unmanned aerial vehicles, and reverse splitting is carried out according to the splicing scheme of the unmanned aerial vehicles, so that the flight tracks of all the unmanned aerial vehicles are obtained.
And 4, step 4: and (4) flight path processing can be carried out. The flight path of the unmanned aerial vehicle obtained in the step3 is a plurality of broken line segments, the flight of the unmanned aerial vehicles is guided, and although the flight path meets the self maneuvering performance constraint of the unmanned aerial vehicle, the flight path is still a plurality of non-flyable flight paths. Considering the limitation of the turning speed of the unmanned aerial vehicle, the unmanned aerial vehicle has the maximum curvature constraint in the steering process, any point on the aircraft meets the constraint, and the flight path is flyable. To generate a flyable path between the start pose point and the end pose point, as shown in fig. 8, a three-dimensional Dubins path is designed and used to connect the waypoints obtained by the genetic algorithm, which is expected to generate a smooth flyable path as shown in fig. 9.
The following example is given:
in order to verify the effectiveness of the path planning method for multiple unmanned aerial vehicles to reach the designated place simultaneously in the three-dimensional environment, the Matlab programming environment is used in the embodiment, and the related parameters are set as follows:
the population size is as follows: 100, respectively; maximum number of iterations: 100, respectively; adaptive cross probability maximum/minimum: 0.7/0.1; adaptive mutation probability max/min: 0.25/0.05; number of unmanned aerial vehicles: 3; number of flight path segments: 5; maximum yaw angle: 60 degrees; maximum pitch angle: 30 degrees; minimum flying height: 100 m; minimum turning radius: 20 m; the maximum flying speed is as follows: 200 km/h; minimum flying speed: 50 km/h; planning a flight path in a 500km x 2000m three-dimensional environment with terrain constraint and no-fly area threat, setting starting point coordinates of three unmanned planes as (205,59,1783), (110,122,594) and (495,480,1133),
the coordinates of the target point are (188,209,750), and the three-dimensional flight path planning effect is obtained as shown in FIG. 10. It can be seen that under the condition that terrain constraint and no-fly area threat exist, the three-dimensional flight path planning evolves towards the direction with small threat, short path length and low flying height on the premise of not colliding with the terrain, and meanwhile, on the premise of fixing the speed of the unmanned aerial vehicle, the total length error of a plurality of flight path sections is not more than 1 m.
The embodiments disclosed above are implemented on the premise of the technical solution of the present invention, and detailed implementation and specific operation procedures are given, but the scope of protection of the present invention is not limited to the embodiments; from the foregoing, it will be appreciated that many of the present inventions can be modified and substituted, and that certain parameters have been set forth in this embodiment only to better illustrate the principles and applications of the present invention, so as to facilitate understanding and use; the invention is not limited to the specific embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A path planning method for multiple unmanned aerial vehicles to reach a designated place simultaneously in a three-dimensional environment adopts a genetic algorithm to realize path planning, and is characterized by comprising the following steps:
step1, establishing a population: each chromosome in the population corresponds to a solution; the chromosome represents a comprehensive track, and the comprehensive track is formed by connecting tracks of all unmanned aerial vehicles in series; the track consists of a series of discrete track points, and the three-dimensional coordinates of the track points form gene codes in the chromosome;
step2, setting the fitness of a genetic algorithm, and comprehensively evaluating the violation degree W of the set maneuvering performance constraint of the unmanned aerial vehicle by the total track length L of all unmanned aerial vehicles, the difference degree T of the arrival time of different unmanned aerial vehicles and the track;
step3, optimizing chromosomes by adopting a genetic algorithm; when the intersection operation of the genetic algorithm is carried out, only the waypoints belonging to the same unmanned aerial vehicle are allowed to intersect; obtaining an optimal chromosome after genetic algorithm iteration is completed;
and 4, obtaining the flight path of each unmanned aerial vehicle by adopting optimal chromosome decomposition.
2. The method of claim 1, wherein in step3, when executing the genetic algorithm, the population initialization process is:
aiming at each unmanned aerial vehicle i, in a two-dimensional plane, starting point A corresponding to the unmanned aerial vehicle iiThe connecting line of the target point B common to all the unmanned aerial vehicles is a symmetrical middle line and draws a rectangle which is a flight path planning area of the unmanned aerial vehicle i; according to the number N of track points set for unmanned aerial vehicle iiStarting point AiThe connecting line between the target point B is equally divided into NiDividing the planning region into NiEach small rectangle is a planning area of a corresponding single track point; when the track point is initialized, the initial value of the track point is randomly generated in the planning area corresponding to the track point under the constraint of the flyable environment, the coordinate of the track point under the two-dimensional plane is obtained, and then the track point is randomly generated under the constraint of the flyable environmentThe height value is obtained, so that the initial three-dimensional coordinates of the track point are obtained; according to the starting point AiSequential generation of N to target point BiThe initial three-dimensional coordinates form an initial track of the unmanned aerial vehicle i; the initial tracks of all unmanned aerial vehicles are connected in series to obtain an initial chromosome; and generating a plurality of initial chromosomes to finish the initialization of the population.
3. The method according to claim 2, wherein, in the step3, when performing the mutation operation of the genetic algorithm, for the track point to be mutated, a new track point is randomly generated again in the planning area corresponding to the track point.
4. The method of claim 1, wherein step3 further performs a perturbation operation after the mutation operation when performing the genetic algorithm; the perturbation operation is only effective for each track point except the starting point and the target point;
the perturbation operation comprises: determining whether the chromosome executes perturbation operation according to the set perturbation probability; if the execution is needed, selecting part of disturbance points from the track points except the starting point and the target point according to the disturbance proportion to apply micro-disturbance; the disturbance amount of the micro-disturbance is 1% -5% of the original value of the track point; calculating the fitness of the new chromosome after the chromosome is disturbed, and replacing the chromosome before disturbance with the chromosome after disturbance if the fitness is greater than the fitness before disturbance; otherwise, no replacement is performed.
5. The method of claim 4, wherein the perturbation probability is 1% -5%.
6. The method of claim 4, wherein the applying perturbations are: the disturbance to the feasible track is small, and the disturbance to the non-feasible track is large; the feasible flight path is a flight path which meets the maneuvering performance constraint of the unmanned aerial vehicle, and if the feasible flight path does not meet the maneuvering performance constraint, the feasible flight path is an infeasible flight path.
7. The method of claim 4Method, characterized in that said step3 uses an adaptive crossover probability P when performing crossover and mutation operations of a genetic algorithmcAnd adaptive mutation probability Pm：
Wherein k is1，k2Determining the maximum value of the cross probability and the mutation probability respectively as a constant; f. ofmaxRepresents the maximum fitness of the chromosome within the current population, favgRepresenting the mean fitness of the current population, fcDenotes the fitness of the chromosome currently requiring crossover operation, fmRepresenting the fitness of the chromosome which needs mutation operation at present; k is a radical of3And k is4Is a set constant.
8. The method of claim 1, wherein step2 is:
for each chromosome, three fitness components E1, E2, E3 are calculated, including:
calculating the track length of each unmanned aerial vehicle, and summing to obtain L; the shorter L, the larger the fitness component E1;
calculating the time of each unmanned aerial vehicle flying to a target point according to the track point; the difference between the longest time and the shortest time is the maximum difference T of the arrival times of different unmanned aerial vehicles; the smaller the maximum difference T is, the larger the fitness component E2 is;
calculating whether the actual maneuvering performance of each unmanned aerial vehicle during the flying process according to the track points violates the corresponding maneuvering performance constraint of the unmanned aerial vehicle; for a violater, acquiring each constraint violation component, respectively normalizing all the constraint violation components, and then weighting and summing to obtain the constraint violation of a single unmanned aerial vehicle; weighting and summing constraint violations of all unmanned aerial vehicles to obtain total violations; if all the unmanned aerial vehicles do not violate the constraint, the total violation amount adopts a set value; the smaller the total violation, the larger the fitness component E3;
and normalizing the three fitness components E1, E2 and E3, and then weighting and summing to obtain the fitness of the chromosome.
9. The method of claim 1, wherein after obtaining the flight path of each drone, further connecting with three-dimensional Dubins curves to generate a flyable path of the drone.
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