CN117148851A - Unmanned parafoil sectional type flight path planning method based on particle swarm optimization algorithm - Google Patents
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Abstract
The invention discloses an unmanned parafoil sectional type track planning method based on a particle swarm optimization algorithm, which comprises the following steps: firstly, according to the glide ratio characteristics of an unmanned parafoil, a track planning objective function taking drop point precision as an index is established, a parameter solving range to be optimized is determined according to an actual flight experiment result, then, a particle swarm optimization algorithm is used for carrying out iterative solution on the objective function, the adaptive coefficient of the altitude is automatically multiplied in the process to cope with different throwing altitude conditions, after an optimal track parameter is obtained, a sectional track is calculated according to the geometric relation of the sectional track, after the optimal track parameter is obtained, the sectional track is calculated according to the geometric relation, and a desired parafoil track point discrete sequence is output. The invention can automatically adjust flight path parameters according to the throwing height and the flight characteristics of the parafoil, and can restrict the track of the second half section of the parafoil within the range of target points, thereby realizing high-precision fixed-point throwing and recycling of the unmanned parafoil.
Description
Technical Field
The invention relates to the field of unmanned parafoil control, in particular to an unmanned parafoil sectional type flight path planning method based on a particle swarm optimization algorithm.
Background
Unmanned parachute wings combine the characteristics of unmanned aerial vehicle and parachute wing aircraft, generally adopt the parachute wing structure, have longer wing and relatively less fuselage. Unmanned parachute wings can provide lift force through a self power system or by utilizing air flow, so that flight is realized. The unmanned parachute wings can be used for various applications, such as aerial photography, scientific research, agricultural monitoring and the like.
The unmanned parachute-wing navigation algorithm is mainly used for determining the route and the target point of the flight and realizing autonomous navigation and the flight. The following are some common unmanned umbrella wing navigation algorithms:
global Positioning System (GPS) navigation: the unmanned parachute wing can acquire satellite signals by using the GPS receiver, determine longitude and latitude coordinates of the unmanned parachute wing and realize navigation by combining map data. By setting the target point, the position is continuously calibrated in the flight process so as to realize the flight of a preset route.
Inertial Navigation System (INS): the unmanned umbrella wing can be provided with inertial sensors, such as an accelerometer and a gyroscope, and can calculate displacement and attitude angle by measuring the change of acceleration and angular velocity and realize autonomous navigation by combining initial position information. INS may provide sustained navigation capability in the event of a weak or interrupted GPS signal.
Visual navigation: unmanned wings may use cameras or other visual sensors to sense the surrounding environment and identify and track key landmarks or feature points through image processing and computer vision algorithms. These feature points can be used for positioning and navigation, implementing landmark navigation or visual odometry.
Path planning algorithm: the unmanned aerial vehicle wing can calculate an optimal route according to the starting point, the ending point and the obstacle avoidance information by using a path planning algorithm. Common path planning algorithms include Dijkstra algorithm, genetic algorithm, and the like, which can consider factors such as terrain, wind field, and route length to generate a route suitable for an unmanned parachute.
Autonomous system integration: the navigation algorithm of the unmanned parachute wing can be integrated with other autonomous systems, such as an obstacle avoidance system, a gesture control system and the like. Autonomous flight and navigation capabilities are achieved through the cooperative work of a plurality of algorithms.
For example: the patent document with the patent number 201520945664.5 discloses a parafoil unmanned aerial vehicle flight controller, which comprises a data acquisition module, a flight control navigation computer, an actuating mechanism and a ground station module; the purpose of this scheme is to provide the autonomous intelligent flight control of parafoil, and the characteristics of current parafoil controller and parafoil unmanned aerial vehicle's work requirement are aimed at, and this flight controller controls parafoil unmanned aerial vehicle system and flies according to established route, carries out conventional unmanned aerial vehicle's such as similar reconnaissance, communication relay, forest fire prevention task, makes the parafoil work like unmanned aerial vehicle.
According to the scheme, the sensor group, the flight control navigation and task computer, the executing mechanism, the flight control algorithm, the ground measurement and control link communication module and the ground station module are arranged, data acquisition is provided through the system, and the parafoil is controlled, so that functions of flight path control, autonomous landing, data transmission, ground control and the like are realized, and the unmanned aerial vehicle system of the parafoil is controlled by the flight controller to fly according to a set flight path.
However, the following drawbacks still exist:
unmanned parafoil is used as a flexible unmanned aerial vehicle, a flexible parachute body is adopted to provide lifting force, so that a parafoil system is greatly influenced by an external wind field, flexible deformation and nonlinear dynamics characteristics exist, the factors make the implementation of flight path planning and accurate landing point control of the parafoil very difficult, and the flight path planning of the parafoil is required to consider a plurality of factors, such as landing point precision, energy consumption control, windward landing and the like.
Disclosure of Invention
The invention aims to provide an unmanned parafoil sectional track planning method based on a particle swarm optimization algorithm, so as to realize accurate drop point self-adaptive track planning of the unmanned parafoil. According to the method, firstly, a flight path planning objective function taking drop point precision as an index is established according to the glide ratio characteristic of the unmanned parafoil, a parameter solving range to be optimized is determined according to an actual flight experiment result, then the objective function is iteratively solved by using a particle swarm optimization algorithm, the height self-adaptive coefficient is automatically multiplied in the process to cope with different throwing height conditions, after the optimal track parameter is obtained, a sectional flight path is calculated according to a geometric relation, a desired parafoil flight path point discrete sequence is output, and high-precision fixed-point throwing recovery of the parafoil is realized.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the unmanned parafoil sectional type flight path planning method based on the particle swarm optimization algorithm comprises the following steps:
s101) establishing a track planning objective function
According to the initial position, the target position, the glide ratio characteristic and the geometric relation of the segmented flight path of the unmanned parafoil, an optimal planning target function taking the drop point precision as an index is established;
s102) determining the range of the track parameters to be optimized
Determining the range of the track parameter to be optimized according to the minimum turning radius of the unmanned parafoil and the glide ratio range under the control effect, which are measured by an actual flight experiment, so as to ensure that the planned track accords with the actual motion characteristic of the unmanned parafoil;
s103) solving optimal track parameters based on particle swarm optimization algorithm
Initializing a particle population according to the range of the track parameters to be optimized, circularly updating the position and the speed of the particle population according to a set rule, calculating the fitness of an optimization objective function, and recording the optimal position and the fitness of each particle in the particle population and the particle population until the precision requirement is met;
s104) adjusting the height adaptive parameters
In step S103), when the loop exceeds the maximum iteration number and still does not meet the accuracy requirement, multiplying the height adaptive parameter, and repeating step S103) until the accuracy requirement is met;
s105) outputting segmented track results
And calculating three-dimensional position coordinates of a series of discrete points on the track according to the geometric relationship between the optimal track parameters and the sectional track obtained by solving, and finally outputting a sectional track planning result of the unmanned parafoil.
Further, in the step S101), the objective function is established as follows:
according to the characteristic that the ratio of the horizontal flight distance to the vertical falling height is a fixed value when the unmanned parafoil glides, namely the glide ratio is k, the absolute value of the k times of the difference between the horizontal flight distance and the vertical falling height is taken as an objective function J, and when the J is smaller, the landing point error is smaller, and the planned track is closer to the expected track.
Further, in the step S101), the segmented track includes three main track segments and a plurality of turning transition segments;
wherein the primary track segment comprises:
the centripetal homing section is put in to adjust the heading of the parafoil and slides to the target area through the straight line section;
the energy control section, the parafoil descends in a large radius spiral around the target drop point, and consumes gravitational potential energy;
and the windward landing section is used for adjusting the heading of the parafoil to the upwind direction, and the parafoil slides linearly to land to the target point.
Further, the track parameters to be optimized in the step S102) include a glide ratio k and an energy control section turning radius R ep Energy control segment hover angle θ ep 。
Further, in the step S102), the control amounts of the unmanned parafoil include an asymmetric flap deflection control amount that affects yaw motion and a symmetric flap deflection control amount that affects pitch motion;
when the asymmetric flap deflection control takes the maximum value, the minimum turning radius of the unmanned parafoil can be measured, and the minimum turning radius plus a safety margin is the minimum value of the turning radius of the transition turning section and the turning radius of the energy control section during track planning;
when the deflection control of the symmetrical flap takes the maximum value, the glide ratio range of the flight process of the parafoil can be measured, namely the value range of the objective function independent variable glide ratio in track planning; the value range of the hover angle of the energy control section to be optimized is defaulted to be [0,2n pi ], wherein n is a height self-adaptive parameter, and the initial value is 1.
Further, in the step S103), an iterative process of solving the track planning objective function using the particle swarm optimization algorithm is as follows:
step one: randomly initializing particle positions in a populationSpeed-> As an initial solution of the optimization problem, i=1, 2, …, N is the population number, d is the dimension of the parameter to be optimized;
step two: calculating the fitness of an objective function J corresponding to the position of each particle, and recording the historical optimal positions and the historical optimal fitness of all particles and particle populations; if the optimal fitness of the particle population is smaller than the acceptable threshold epsilon, the cycle is exited in advance, and the code is ended;
step three: updating the velocity and position of each particle, the rules being
x i ←x i +v i ,
Wherein ω is a positive real number representing the inertia of the particle motion,for the historical best position, x, before the current moment of the ith particle gb For the historical best position of the population before the current moment c 1 、c 2 For characterizing the positive coefficients of the weights, rand is a random number uniformly distributed between 0 and 1; the update of the particle velocity includes the inertia term omega.v i Individual cognitive termAnd population cognitive term c 2 ·rand·(x gb -x i ) Three parts;
step four: checking and correcting the updated position of each particle, and ensuring that the position of each particle is in the parameter solving range of the optimization problem;
step five: repeating the steps two to four until convergence or the maximum iteration number is reached.
Further, in the step S104), the initial value of the adaptive coefficient is set to n 0 And (1) when the objective function iterative optimization reaches the maximum times and the objective function value is still larger than the set threshold, doubling the coefficient n, and carrying out optimization solution again until the planning error meets the requirement.
Further, in step S105), in order to ensure that the output track point sequence interval is uniform, the height difference between two adjacent points is set to be a constant value, and the track segment length between two points is set to be a constant value.
Compared with the prior art, the invention has the following beneficial effects:
in order to realize accurate drop point track planning of the unmanned parafoil, the invention solves the optimal track parameters of the segmented track of the parafoil based on a particle swarm optimization algorithm, and self-adaptively adjusts the track according to the throwing height, so that the invention can adapt to different throwing conditions, accords with the actual motion characteristic of the parafoil, and meets the requirement of accurate drop point throwing and recycling.
The parafoil track planning algorithm provided by the invention has better self-adaptability, and can automatically adjust track parameters according to the thrown height and the flight characteristics of the parafoil; meanwhile, the sectional track planning can restrict the track of the second half section of the parafoil to the track taking the target point as the circle center and the radius as R ep Is more suitable for being used in environments with barriers around and complex environments.
Drawings
Fig. 1 is a flow chart of an unmanned parafoil sectional type flight path planning method based on a particle swarm optimization algorithm.
Fig. 2 is a schematic diagram of a centripetal turn section, an energy control section, a windward landing section, and a transition turn section in a segmented track planning according to the present invention.
Fig. 3 is a schematic diagram of a solution process of input and output of a particle swarm optimization algorithm, related parameters and track parameters to be optimized for solving optimal parameters of track planning according to the present invention.
FIG. 4 is a schematic diagram of the parafoil of the present invention adaptively adjusting the flight path under high launch altitude and dissipating gravitational potential energy by constantly hovering over an energy control segment.
Detailed Description
In order that the manner in which the above-recited features, advantages, objects and advantages of the invention are obtained, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
As shown in fig. 1, the unmanned parafoil sectional type flight path planning method based on the particle swarm optimization algorithm provided by the invention comprises the following steps:
s101) establishing a track planning objective function
According to the initial position and the target position of the unmanned parafoil, the glide ratio characteristics of the unmanned parafoil and the geometric relationship of the sectional track, an optimal planning target function taking the drop point precision as an index is established;
s102) determining the range of the track parameters to be optimized
Determining the range of the track parameter to be optimized according to the minimum turning radius of the unmanned parafoil measured by an actual flight experiment and the glide ratio range under the control action so as to ensure that the planned track accords with the actual motion characteristic of the unmanned parafoil;
s103) solving optimal track parameters based on particle swarm optimization algorithm
Initializing a particle population according to the range of the track parameters to be optimized, circularly updating the position and the speed of the particles according to a certain rule, calculating the value (fitness) of an optimization objective function, and recording the optimal position and the fitness of the population and each particle until the precision threshold is met;
s104) adjusting the height adaptive parameters
In step S103), when the cycle exceeds the maximum iteration number but still does not meet the precision requirement, multiplying the height self-adaptive parameter, and repeating step S103) until the precision requirement is met;
s105) outputting the sectional track result
And calculating three-dimensional position coordinates of a series of discrete points on the track according to the solved geometric relationship between the optimal track parameters and the sectional type track, and finally outputting a sectional type track planning result of the unmanned parafoil.
In step S101) of the present invention, according to the characteristic that the ratio of the horizontal flight distance to the vertical falling height is a constant value (glide ratio k) when the unmanned parafoil glides, the absolute value of the difference between the horizontal flight distance and the vertical falling height is k times may be used as the objective function J, and when the smaller the J is, the smaller the landing point error is, and the closer the planned track is to the ideal expected track.
In step S101) of the present invention, the segmented track comprises three main track segments and a series of turning transition segments, wherein the main track segments comprise: the centripetal homing section is put in to adjust the heading of the parafoil and slides to the target area through the straight line section; the energy control section, the parafoil descends in a large radius spiral around the target drop point, and consumes gravitational potential energy; and the windward landing section is used for adjusting the heading of the parafoil to the upwind direction, and the parafoil slides linearly to land to the target point.
In the step S102), the track parameter to be optimized for the unmanned parafoil sectional track planning has a parafoil glide ratio k and an energy control section turning radius R ep Energy control segment hover angle θ ep 。
In the step S102), the control quantity of the unmanned parafoil comprises an asymmetric flap deflection control quantity (influencing the yaw motion of the parafoil) and a symmetric flap deflection control quantity (influencing the pitch motion of the parafoil), when the asymmetric flap deflection control quantity is at the maximum value, the minimum turning radius of the parafoil can be measured, and the minimum turning radius is added with a certain safety margin, namely the minimum turning radius of a transition turning section and the turning radius of an energy control section in track planning; when the deflection control of the symmetrical flap takes the maximum value, the glide ratio range of the flight process of the parafoil can be measured, namely the value range of the objective function independent variable glide ratio in track planning; the value range of the hover angle of the energy control section to be optimized is defaulted to be [0,2n pi ], wherein n is a height self-adaptive parameter, and the initial value is 1.
In step S103) of the present invention, a particle swarm optimization algorithm is used to solve a track planning objective function, and the iterative process is as follows:
step one: randomly initializing particle positions in a populationSpeed-> As an initial solution of the optimization problem, i=1, 2, …, N is the population number, d is the dimension of the parameter to be optimized;
step two: and calculating the value (namely fitness) of an objective function J corresponding to the position of each particle, and recording the historical optimal position and the historical optimal fitness of each individual and each population. If the optimal fitness of the population is smaller than the acceptable threshold epsilon, the cycle is exited in advance, and the code is ended;
step three: updating the velocity and position of each particle, the rules being
x i ←x i +v i ,
Wherein ω is a positive real number representing the inertia of the particle motion,for the historical best position, x, before the current moment of the ith particle gb For the historical best position of the population before the current moment c 1 、c 2 To characterize the positive coefficients of the weights, rand is a random number uniformly distributed between 0 and 1. The update of the particle velocity includes the inertia term omega.v i Individual cognitive termAnd population cognitive term c 2 ·rand·（x gb -x i ) Three parts;
step four: checking and correcting the updated position of each particle, and ensuring that the position of each particle is in the parameter solving range of the optimization problem;
step five: repeating the steps two to four until convergence or the maximum iteration number is reached.
In step S104) of the present invention, the initial value of the adaptive coefficient is set to n 0 When the objective function iterative optimization reaches the maximum times, but the objective function value is still larger than the set threshold, the coefficient n is doubled, and the optimization solution is performed again until the planning error meets the requirement.
In step S105), in order to ensure that the output track point sequence interval is uniform, the height difference between two adjacent points is set to be a fixed value, and the track segment length between two points is set to be a fixed value.
Example 1
And carrying out track planning on the unmanned parafoil in a simulation environment.
Step S101: when the initial delivery position is (x 0 ,y 0 ,z 0 ) In this case, according to the geometric relationship diagram of the sectional track planning shown in fig. 2, an optimal track planning objective function is established as
Step S102: determining the minimum turning radius R of the unmanned parafoil according to the actual flight data of the unmanned parafoil min And controlling the range of the paraglider ratio k under the action.
Step S103: taking the objective function J as a fitness function, solving in the parameter range determined in the step S102 according to the particle swarm optimization algorithm flow shown in FIG. 3, and directly entering the step S105 if the requirement of the precision E is met before the maximum iteration number; otherwise, the process advances to step S104.
Step S104: if the number of loops in the solving process is maximum and still does not meet the precision requirement, automatically multiplying the height self-adaptive coefficient n, increasing the number of convolutions of the parafoil in the energy control section, and returning to the step S103 to solve again.
Step S105: according to the geometric relation diagram of the sectional type track planning shown in fig. 2, the track of the parafoil is uniquely determined according to the solved optimal track parameters, and the track is equally spaced into a series of discrete track points by taking deltaz as a height interval, so that the result shown in fig. 4 is obtained.
The parafoil track planning algorithm provided by the invention has better self-adaptability, and can automatically adjust track parameters according to the thrown height and the flight characteristics of the parafoil; meanwhile, the sectional track planning can restrict the track of the second half section of the parafoil to the track taking the target point as the circle center and the radius as R ep Is more suitable for being used in environments with barriers around and complex environments.
In this document, the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", "vertical", "horizontal", etc. refer to the directions or positional relationships based on those shown in the drawings, and are merely for clarity and convenience of description of the expression technical solution, and thus should not be construed as limiting the present invention.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a list of elements is included, and may include other elements not expressly listed.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The unmanned parafoil sectional type flight path planning method based on the particle swarm optimization algorithm is characterized by comprising the following steps of:
s101) establishing a track planning objective function
According to the initial position, the target position, the glide ratio characteristic and the geometric relation of the segmented flight path of the unmanned parafoil, an optimal planning target function taking the drop point precision as an index is established;
s102) determining the range of the track parameters to be optimized
Determining the range of the track parameter to be optimized according to the minimum turning radius of the unmanned parafoil and the glide ratio range under the control effect, which are measured by an actual flight experiment, so as to ensure that the planned track accords with the actual motion characteristic of the unmanned parafoil;
s103) solving optimal track parameters based on particle swarm optimization algorithm
Initializing a particle population according to the range of the track parameters to be optimized, circularly updating the position and the speed of the particle population according to a set rule, calculating the fitness of an optimization objective function, and recording the optimal position and the fitness of each particle in the particle population and the particle population until the precision requirement is met;
s104) adjusting the height adaptive parameters
In step S103), when the loop exceeds the maximum iteration number and still does not meet the accuracy requirement, multiplying the height adaptive parameter, and repeating step S103) until the accuracy requirement is met;
s105) outputting segmented track results
And calculating three-dimensional position coordinates of a series of discrete points on the track according to the geometric relationship between the optimal track parameters and the sectional track obtained by solving, and finally outputting a sectional track planning result of the unmanned parafoil.
2. The unmanned aerial vehicle segmented flight path planning method based on the particle swarm optimization algorithm according to claim 1, wherein in the step S101), the objective function is established as follows:
according to the characteristic that the ratio of the horizontal flight distance to the vertical falling height is a fixed value when the unmanned parafoil glides, namely the glide ratio is k, the absolute value of the k times of the difference between the horizontal flight distance and the vertical falling height is taken as an objective function J, and when the J is smaller, the landing point error is smaller, and the planned track is closer to the expected track.
3. The method for planning a segmented flight path of an unmanned parafoil based on particle swarm optimization according to claim 1, wherein in step S101), the segmented flight path comprises three main flight path segments and a plurality of turning transition segments;
wherein the primary track segment comprises:
the centripetal homing section is put in to adjust the heading of the parafoil and slides to the target area through the straight line section;
the energy control section, the parafoil descends in a large radius spiral around the target drop point, and consumes gravitational potential energy;
and the windward landing section is used for adjusting the heading of the parafoil to the upwind direction, and the parafoil slides linearly to land to the target point.
4. The method for planning a segmented flight path of an unmanned parafoil based on a particle swarm optimization algorithm according to claim 1, wherein the trajectory parameters to be optimized in step S102) include a glide ratio k and an energy control segment turning radius R ep Energy control segment hover angle θ ep 。
5. The method for planning a sectional track of an unmanned parafoil based on a particle swarm optimization algorithm according to claim 1, wherein in the step S102), the control amounts of the unmanned parafoil include an asymmetric flap deflection control amount affecting yaw motion and a symmetric flap deflection control amount affecting pitch motion;
when the asymmetric flap deflection control takes the maximum value, the minimum turning radius of the unmanned parafoil can be measured, and the minimum turning radius plus a safety margin is the minimum value of the turning radius of the transition turning section and the turning radius of the energy control section during track planning;
when the deflection control of the symmetrical flap takes the maximum value, the glide ratio range of the flight process of the parafoil can be measured, namely the value range of the objective function independent variable glide ratio in track planning; the value range of the hover angle of the energy control section to be optimized is defaulted to be [0,2n pi ], wherein n is a height self-adaptive parameter, and the initial value is 1.
6. The unmanned aerial vehicle segmented flight path planning method based on the particle swarm optimization algorithm according to claim 1, wherein in the step S103), the iterative process of solving the flight path planning objective function using the particle swarm optimization algorithm is as follows:
step one: randomly initializing particle positions in a populationSpeed-> As an initial solution of the optimization problem, i=1, 2, …, N is the population number, d is the dimension of the parameter to be optimized;
step two: calculating the fitness of an objective function J corresponding to the position of each particle, and recording the historical optimal positions and the historical optimal fitness of all particles and particle populations; if the optimal fitness of the particle population is smaller than the acceptable threshold epsilon, the cycle is exited in advance, and the code is ended;
step three: updating the velocity and position of each particle, the rules being
x i ←x i +v i ,
Wherein ω is a positive real number representing the inertia of the particle motion,for the historical best position, x, before the current moment of the ith particle gb For the historical best position of the population before the current moment c 1 、c 2 For characterizing the positive coefficients of the weights, rand is a random number uniformly distributed between 0 and 1; the update of the particle velocity includes the inertia term omega.v i Individual cognitive termAnd population cognitive term c 2 ·rand·(x gb -x i ) Three parts;
step four: checking and correcting the updated position of each particle, and ensuring that the position of each particle is in the parameter solving range of the optimization problem;
step five: repeating the steps two to four until convergence or the maximum iteration number is reached.
7. The method for planning a sectional type flight path of an unmanned aerial vehicle based on a particle swarm optimization algorithm according to claim 1, wherein in the step S104), the initial value of the adaptive coefficient is set to n 0 And (1) when the objective function iterative optimization reaches the maximum times and the objective function value is still larger than the set threshold, doubling the coefficient n, and carrying out optimization solution again until the planning error meets the requirement.
8. The method for planning the sectional type flight path of the unmanned aerial vehicle based on the particle swarm optimization algorithm according to claim 1, wherein in the step S105), in order to ensure the uniform interval of the output track point sequence, the height difference between two adjacent points is set to be a fixed value, and the length of the track segment between the two points is set to be a fixed value.
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| CN117371640B (en) * | 2023-12-08 | 2024-04-12 | 山东省地质测绘院 | Mapping route optimization method and system based on unmanned aerial vehicle remote sensing |
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