CN114020032A - Unmanned aerial vehicle path planning method based on artificial potential field method and annealing algorithm - Google Patents
Unmanned aerial vehicle path planning method based on artificial potential field method and annealing algorithm Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle path planning method based on an artificial potential field method and an annealing algorithm, which enables an unmanned aerial vehicle to generate disturbance autonomously and generate a new solution in a mode of simulating the annealing algorithm, thereby solving the problem of local minimum value existing in the traditional artificial potential field method; meanwhile, aiming at interference factors such as wind existing in the external environment, the gravitation perpendicular to the air route of the unmanned aerial vehicle is added in an original potential field, and the gravitation is restrained by the perpendicular distance between the unmanned aerial vehicle and the air route, so that the unmanned aerial vehicle only acts when deviating from the air route, the anti-jamming capability of the unmanned aerial vehicle is improved, and the stability of the air route during flying is guaranteed.
Description
Technical Field
The invention relates to an unmanned aerial vehicle path planning technology, in particular to an unmanned aerial vehicle path planning method based on an artificial potential field method and an annealing algorithm.
Background
According to the traditional artificial potential field algorithm principle, the environment where the unmanned aerial vehicle is located is simulated into a virtual field in physics, in the virtual field, a task target point causes attraction to the unmanned aerial vehicle, the attraction is larger as the distance is farther, the obstacle causes repulsion to the unmanned aerial vehicle, the repulsion is larger as the distance is closer, and under the action of the resultant force of the attraction and the repulsion, the unmanned aerial vehicle moves to the target point and finally reaches the target point. However, when the environment becomes complex, the problem of local minimum value occurs in the conventional artificial potential field method, that is, the unmanned aerial vehicle falls into the local optimal condition. At the moment, the attractive force and the repulsive force borne by the unmanned aerial vehicle are just the same in magnitude and opposite in direction, and the unmanned aerial vehicle can be stuck in stagnation or vibrate. As shown in fig. 1, the Matlab simulation diagram is changed into the Matlab simulation diagram in which the unmanned aerial vehicle falls into the local minimum value under the traditional artificial potential field method, and at this time, the unmanned aerial vehicle cannot autonomously jump off the local optimum.
In addition, because unmanned aerial vehicle still can receive the influence of external disturbance such as wind when complex environment during operation, consequently still need strengthen unmanned aerial vehicle's flight stability, make unmanned aerial vehicle return to optimum route fast.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an unmanned aerial vehicle path planning method based on an artificial potential field method and an annealing algorithm, which can improve the problem of local minimum in an artificial potential field algorithm and enhance the flight stability of an unmanned aerial vehicle under the influence of the outside.
The technical scheme is as follows: the invention relates to an unmanned aerial vehicle path planning method based on an artificial potential field method and an annealing algorithm, which comprises the following steps:
(1) setting a starting point, a terminal point, an obstacle and a position parameter;
(2) respectively calculating the attraction of the target point to the unmanned aerial vehicle, the repulsion of the obstacle to the unmanned aerial vehicle and the attraction of the air route to the unmanned aerial vehicle according to the parameters, and finally calculating the resultant force borne by the unmanned aerial vehicle;
(3) judging whether the unmanned aerial vehicle sinks into the local minimum, if not, continuing flying to the target point, and if so, entering the next step;
(4) when the artificial potential field method is trapped in a local minimum, randomly selecting a point x on a circle with the point x as the center of the circle at the current point x1Separately calculating a point x and a point x1Potential field of (U) (x)1)；
(5) If U (x) is satisfied1) Less than or equal to U (x), receiving the point x1As the next point; if U (x) is satisfied1) If U (x) is greater, the probability is calculated
Δ＝U(x1) -u (x) and accepting the point as the next point with a probability P, while T decreases in a manner such that T (T) ═ α T (T-1), 0.85 < α < 1;
(6) repeating the steps (3) to (5) until the local minimum value is escaped.
In the technical scheme, the unmanned aerial vehicle can autonomously generate disturbance and generate a new solution in a mode of simulating an annealing algorithm, so that the problem of local minimum value existing in the traditional artificial potential field method is solved. Meanwhile, aiming at interference factors such as wind existing in the external environment, the gravitation perpendicular to the air route of the unmanned aerial vehicle is added in an original potential field, and the gravitation is restrained by the perpendicular distance between the unmanned aerial vehicle and the air route, so that the unmanned aerial vehicle only acts when deviating from the air route, the anti-jamming capability of the unmanned aerial vehicle is improved, and the stability of the air route during flying is guaranteed.
Preferably, in step (2), regarding the drone as a point, the point moves in a virtual field formed by superimposing a gravitational field of the drone by the target point and a repulsive field caused by the obstacle to the drone, and the direction in which the drone moves is the direction in which the potential field function drops, and the mathematical model is:
U(q)＝Uatt(q)+Urep(q)
wherein q is the coordinate of the unmanned aerial vehicle, Uatt(q) is the gravitational field, Urep(q) is the repulsive force field, from which it can be derived that the resultant force experienced by the drone in the field is:
the formula of the gravitational field is:
wherein, KattThe gravity gain constant, from which the magnitude of gravity can be calculated:
preferably, in step (2), the calculation formula of the repulsive force field is as follows:
from this, the formula for the calculation of the repulsive force is:
preferably, in step (2), the gravity is added on an artificial potential field method, that is, the unmanned aerial vehicle has a real-time vertical gravity with the target route, and the function of the gravity field is as follows:
where σ is the gravitational gain coefficient, ρ2(q,qline) The square of the current linear distance between the unmanned plane and the flight path foot is obtained, and the gravity function is as follows:
preferably, in step (2), the resultant force applied to the drone is:
Fsum(q)＝Fatt(a)+Freg(q)+Frep(q)
at this moment, the unmanned aerial vehicle has relatively strengthened path following capability, the direction of the force can be calculated by calculating the resultant force applied to the unmanned aerial vehicle, and then the next optimal target point can be obtained, wherein the calculation formula of the optimal target point is as follows:
Xk+1＝Xkz±1*cos(posangle(θ))
Yk+1＝Yk±1*sin(posangle(θ))
wherein, 1 is the step length of calculation each time, and theta is the direction angle of the resultant force that current unmanned aerial vehicle receives.
Preferably, the specific processes in steps (3), (4) and (5) are as follows: firstly setting x-S (S is a local minimum or starting point), secondly selecting an annealing strategy, setting T-T0When Tf ≦ T and no local minimum is escaped, a random point x is generated1X + Δ x, where Δ x is a random point near point x, the random point being a step from point x, then calculating U (x1), i.e. the potential energy at point x1, setting Δ U (x1) -U (x), if Δ ≦ 0, x ═ x1, if Δ > 0, with probability
Δ＝U(x1) And U (x) judging, setting a random probability a, when P is more than a, setting x as x1, otherwise, setting the random point as a failed random point, resetting the random point, finally, if U (x1) is less than or equal to U(s), successfully escaping from the local minimum value, and if the U (x1) is less than or equal to U(s), returning to reselect the random point.
Has the advantages that: compared with the prior art, the unmanned aerial vehicle anti-jamming method has the obvious advantages that the unmanned aerial vehicle autonomously generates disturbance to generate a new solution by combining a simulated annealing algorithm, so that the problem of a local minimum value is solved, meanwhile, the attraction perpendicular to a flight line is added into a potential field, and when the unmanned aerial vehicle deviates from the flight line, the action is generated, so that the anti-jamming capability of the unmanned aerial vehicle is improved.
Drawings
FIG. 1 is a conventional artificial potential field method path diagram;
fig. 2 is a path simulation diagram of the scheme of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The unmanned aerial vehicle path planning method based on the artificial potential field method and the annealing algorithm comprises the following steps:
(1) setting parameters such as a starting point, a key point, an obstacle, a position and the like;
(2) regard as an point with unmanned aerial vehicle, this point removes in the virtual field that is formed by the repulsion field stack that the target point pair unmanned aerial vehicle caused to unmanned aerial vehicle, with the barrier, and the direction that unmanned aerial vehicle removed is the direction that potential field function descends, and this mathematical model is:
U(q)＝Uatt(q)+Urep(q)
wherein q is the coordinate of the unmanned aerial vehicle, Uatt(q) is the gravitational field, Urep(q) is the repulsive force field, from which it can be derived that the resultant force experienced by the drone in the field is:
the formula for the gravitational field is:
wherein, KattThe gravity gain constant, from which the magnitude of gravity can be calculated:
the repulsive force field is then calculated, which has the formula:
from this, the formula for the calculation of the repulsive force is:
increasing the gravity on an artificial potential field method, namely the gravity of real-time vertical gravity between the unmanned aerial vehicle and a target air route, wherein the function of the gravity field is as follows:
where σ is the gravitational gain coefficient, ρ2(q,qline) The square of the current linear distance between the unmanned plane and the flight path foot is obtained, and the gravity function is as follows:
and finally, calculating the resultant force borne by the unmanned aerial vehicle as follows:
Fsum(q)＝Fatt(a)+Freg(q)+Frep(q)
at this moment, the unmanned aerial vehicle has relatively strengthened path following capability, the direction of the force can be calculated by calculating the resultant force applied to the unmanned aerial vehicle, and then the next optimal target point can be obtained, wherein the calculation formula of the optimal target point is as follows:
Xk+1＝Xkz±1*cos(posangle(θ))
Yk+1＝Yk±1*sin(posangle(θ))
wherein, 1 is the step length of calculation each time, and theta is the direction angle of the resultant force that current unmanned aerial vehicle receives.
(3) Judging whether the unmanned aerial vehicle sinks into the local minimum, if not, continuing to fly the king target point, and if so, entering the next step;
(4) when the artificial potential field method falls into the local minimum, firstly setting x to S (S is the local minimum or the starting point), secondly selecting an annealing strategy, and setting T to T0When T isfWhen T is less than or equal to T and no escape from the local minimum value, randomly selecting a circle point x with x as the center of a circle at the current point x1X of the1X + Δ x, Δ x is a random point near point x, which is a step from point x, and then point x and point x are calculated separately1Potential field of (U) (x)1) Setting Δ ═ U (x)1) -u (x), if Δ ≦ 0, x ═ x1If Δ > 0, with probability
Δ＝U(x1) -u (x) making a decision, setting a random probability a, when P > a, x ═ x1And if the random point is not greater than U (x1), successfully escaping from the local minimum value, and if the local minimum value is not escaped, returning to reselect the random point.
Fig. 2 is a diagram of the simulation of the unmanned aerial vehicle path according to the scheme of the invention.
Claims (6)
1. An unmanned aerial vehicle path planning method based on an artificial potential field method and an annealing algorithm is characterized by comprising the following steps:
(1) setting a starting point, a terminal point, an obstacle and a position parameter;
(2) respectively calculating the attraction of the target point to the unmanned aerial vehicle, the repulsion of the obstacle to the unmanned aerial vehicle and the attraction of the air route to the unmanned aerial vehicle according to the parameters, and finally calculating the resultant force borne by the unmanned aerial vehicle;
(3) judging whether the unmanned aerial vehicle sinks into the local minimum, if not, continuing flying to the target point, and if so, entering the next step;
(4) when the artificial potential field method is trapped in a local minimum, randomly selecting a circle point x with x as the center of a circle at the current point x1Separately calculating a point x and a point x1Potential field of (U) (x)1)；
(5) If U (x) is satisfied1) Less than or equal to U (x), receiving the point x1As the next point; if U (x) is satisfied1) If U (x) is greater, the probability is calculated
Δ＝U(x1) -U (x), and accepts with a probability PThis point is taken as the next point, while T decreases in a certain manner, T (T) ═ α T (T-1), 0.85 < α < 1;
(6) repeating the steps (3) to (5) until the local minimum value is escaped.
2. The unmanned aerial vehicle path planning method based on the artificial potential field method and the annealing algorithm according to claim 1, wherein in step (2), the unmanned aerial vehicle is considered as a point, the point moves in a virtual field formed by superposing a gravitational field of the target point on the unmanned aerial vehicle and a repulsive field of an obstacle on the unmanned aerial vehicle, the moving direction of the unmanned aerial vehicle is a direction in which a potential field function descends, and the mathematical model is as follows:
U(q)＝Uatt(q)+Urep(q)
wherein q is the coordinate of the unmanned aerial vehicle, Uatt(q) is the gravitational field, Urep(q) is the repulsive force field, from which it can be derived that the resultant force experienced by the drone in the field is:
the formula of the gravitational field is:
wherein, KattThe gravity gain constant, from which the magnitude of gravity can be calculated:
3. the unmanned aerial vehicle path planning method based on the artificial potential field method and the annealing algorithm as claimed in claim 1, wherein in step (2), the calculation formula of the repulsive field is as follows:
from this, the formula for the calculation of the repulsive force is:
4. the unmanned aerial vehicle path planning method based on artificial potential field method and annealing algorithm as claimed in claim 1, wherein in step (2), gravity is added on artificial potential field method, namely real-time vertical gravity between the unmanned aerial vehicle and the target route, and the function of the gravity field is:
where σ is the gravitational gain coefficient, ρ2(q,qline) The square of the current linear distance between the unmanned plane and the flight path foot is obtained, and the gravity function is as follows:
5. the unmanned aerial vehicle path planning method based on the artificial potential field method and the annealing algorithm as claimed in claim 1, wherein in step (2), the total force applied to the unmanned aerial vehicle is:
Fsum(q)＝Fatt(a)+Freg(q)+Frep(q)
at this moment, the unmanned aerial vehicle has relatively strengthened path following capability, the direction of the force can be calculated by calculating the resultant force applied to the unmanned aerial vehicle, and then the next optimal target point can be obtained, wherein the calculation formula of the optimal target point is as follows:
Xk+1＝Xkz±1*cos(posangle(θ))
Yk+1＝Yk±1*sin(posangle(θ))
wherein, 1 is the step length of calculation each time, and theta is the direction angle of the resultant force that current unmanned aerial vehicle receives.
6. The unmanned aerial vehicle path planning method based on the artificial potential field method and the annealing algorithm as claimed in claim 1, wherein the specific processes in steps (3), (4) and (5) are as follows: firstly setting x-S (S is a local minimum or starting point), secondly selecting an annealing strategy, setting T-T0When T isfT and no escape from the local minimum, a random point x is generated1X + Δ x, which is a random point near point x, one step from point x, then calculate U (x)1) I.e. point x1Set delta-U (x) as potential energy of1) -u (x), if Δ ≦ 0, x ═ x1If Δ > 0, with probability
Δ＝U(x1) -u (x) making a decision, setting a random probability a, when P > a, x ═ x1And if the random point is not greater than U (x1), successfully escaping from the local minimum value, and if the local minimum value is not escaped, returning to reselect the random point.
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| CN108121338A (en) * | 2016-11-30 | 2018-06-05 | 中国科学院沈阳自动化研究所 | A kind of flight path closed loop control method of USV |
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