CN110262555B - Real-time obstacle avoidance control method for unmanned aerial vehicle in continuous obstacle environment - Google Patents
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Abstract
The invention relates to a real-time obstacle avoidance control method for an unmanned aerial vehicle in a continuous obstacle environment, and belongs to the technical field of unmanned aerial vehicle aerial obstacle avoidance. Firstly, establishing a mathematical model of an unmanned aerial vehicle obstacle avoidance system by using a spatial position relation and a kinematic equation of the unmanned aerial vehicle, and further designing an unmanned aerial vehicle obstacle avoidance guidance law by using the characteristic of rapid convergence of a multi-power sliding mode approach law; meanwhile, uncertainty caused by sensor error, system modeling and the like is considered, and a supercoiled disturbance observer is introduced to estimate and compensate the uncertainty in the system. The method can enable the unmanned aerial vehicle to accurately avoid continuous obstacles in real time, cannot cause excessive maneuvering of the unmanned aerial vehicle, and has strong robustness to system uncertainty caused by sensor errors.
Description
Technical Field
The invention relates to a real-time obstacle avoidance control method for an unmanned aerial vehicle in a continuous obstacle environment, and belongs to the technical field of unmanned aerial vehicle aerial obstacle avoidance.
Technical Field
Recently, small-sized fixed-wing drones have shown great potential in military and civilian fields, and have been widely used in various fields such as reconnaissance, surveillance, target acquisition, etc., because they have faster speed and excellent cruising ability than the rotary-wing drones. However, the current small-sized fixed wing drone cannot rapidly and safely pass through the complex terrain when facing continuous obstacles such as buildings, tunnels and the like. The main difficulty lies in that the size and shape of continuous obstacles are not clear before the unmanned aerial vehicle flies, and the characteristic of high flying speed of the unmanned aerial vehicle provides higher real-time requirement for the design of a control law.
At present, many scholars at home and abroad research the advanced control strategy of obstacle avoidance of the unmanned aerial vehicle, but most of the research focuses on obstacle detection and path planning, and the situations that the kinematics constraint of the fixed-wing unmanned aerial vehicle and the size of an obstacle exceed the detection range of a sensor are not considered.
Aiming at the problems, a novel unmanned aerial vehicle city real-time obstacle avoidance flight control method needs to be researched.
Disclosure of Invention
In order to overcome the defect that the existing unmanned aerial vehicle obstacle avoidance method is insufficient in capability of dealing with real-time sudden continuous obstacles, the invention provides a real-time obstacle avoidance control method for an unmanned aerial vehicle in a continuous obstacle environment.
The invention adopts the following technical scheme for solving the technical problems:
an unmanned aerial vehicle real-time obstacle avoidance control method under a continuous obstacle environment comprises the following steps:
(1) measuring the distance of an obstacle and the surface curvature of the obstacle by using an unmanned aerial vehicle body sensor;
(2) the mathematical relation between the roll angle and the obstacle distance of the unmanned aerial vehicle is set forth by utilizing the geometric position relation between the sensor and the unmanned aerial vehicle;
(3) designing an obstacle avoidance guidance law of the unmanned aerial vehicle by adopting a multi-power sliding mode approach law;
(4) the supercoiling method was introduced to address the noise problem of the sensor.
And (1) the unmanned aerial vehicle body sensor is a sensor with fixed installation positions on two sides of the unmanned aerial vehicle body axis in the X-axis direction.
The sensor is a laser sensor and an image sensor.
And (2) the mathematical relationship between the roll angle of the unmanned aerial vehicle and the obstacle distance is a differential expression of the distance rho between the unmanned aerial vehicle and the obstacle and the installation angle eta of the sensor, and the condition that the installation angle of the sensor is fixed is utilized.
And (4) compensating the noise of the sensor by adopting a supercoiled disturbance observer.
The invention has the following beneficial effects:
(1) the sensor adopted by the invention is an airborne sensor, only measures the obstacle distance and the obstacle surface azimuth angle at a relatively fixed angle, has simple calculation and high processing speed, and meets the real-time obstacle avoidance requirement.
(2) The obstacle avoidance guidance law based on the multi-power sliding mode approach law is adopted, so that the unmanned aerial vehicle can quickly converge at different obstacle avoidance stages, meanwhile, the requirement on rapidity is met, the overshoot and oscillation are reduced, and the unmanned aerial vehicle is prevented from being excessively maneuvered.
(3) The supercoiled disturbance observer adopted by the invention can compensate the uncertain quantity in the system caused by the sensor error, fully considers the error problem of the sensor in practical application, and ensures the stability and rapidity of the obstacle avoidance system.
Drawings
Fig. 1 is a diagram of the position relationship between an unmanned aerial vehicle and an obstacle wall surface.
FIG. 2 is a disturbance observer system block diagram.
Fig. 3 is an obstacle avoidance trajectory diagram of the unmanned aerial vehicle.
Fig. 4(a) is a diagram of drone detection range response comparison; FIG. 4(b) is a comparison graph of yaw angle obstacle avoidance responses; FIG. 4(c) is a roll angle response comparison.
Fig. 5 is a disturbance observer response curve.
FIG. 6(a) is a disturbance-compensated probe range response curve; fig. 6(b) is a disturbance-compensated roll angle response curve.
Fig. 7 is a diagram of unmanned aerial vehicle trajectories in a curved obstacle environment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
The urban real-time obstacle avoidance flight control method for the unmanned aerial vehicle measures the distance and the surface curvature of an obstacle on eta azimuth angles on the left side and the right side of the x-axis direction of the unmanned aerial vehicle through an airborne sensor, and establishes a mathematical model of an unmanned aerial vehicle obstacle avoidance system by combining a transverse and lateral kinematic equation and a spatial position geometric relation of a fixed wing unmanned aerial vehicle. The obstacle avoidance guidance law of the unmanned aerial vehicle is further designed by utilizing the multi-power sliding mode approach law, and under the condition that the error of the sensor is considered, the supercoiled sliding mode observer is designed to estimate and compensate the uncertain quantity caused by the error of the sensor, so that the purposes that the unmanned aerial vehicle can quickly converge to an obstacle-free path and cannot cause excessive maneuvering when facing obstacles such as a continuous wall surface and the like are achieved. The following description of the drawings is used to fully describe the unmanned aerial vehicle city real-time obstacle avoidance flight control method in detail.
As shown in fig. 1, T is a detection intersection point of the unmanned aerial vehicle detection sensor and the wall, and T varies with the position of the unmanned aerial vehicle. Is provided with
Where ρ is the distance between the drone and the obstacle wall detected by the sensor, V is the speed of the drone, ψ is the yaw angle of the drone, g is the gravitational acceleration constant, ψ is the distance between the drone and the obstacle wall, V is the speed of the drone, V is the yaw angle of the drone, g is the yaw angle of the drone, V is the yaw angle of the dronewIs the azimuth angle of the obstacle wall surface, phi is the roll angle of the unmanned plane, VTTo detect the speed of movement of the focal spot along the surface of the obstacle,
for differentiating the detection distance, eta is the angle between the sensor orientation and the X-axis of the drone, determined by the sensor mounting position and angle, and is a constant value, i.e. the derivative of eta
The formula derived from FIG. 1 can be solved
Therefore, a mathematical model of the unmanned aerial vehicle obstacle avoidance system is obtained, and then an obstacle avoidance guidance law is designed.
The relation between the distance change of the obstacle is detected by the roll angle of the unmanned aerial vehicle and the sensor, and the obtained roll angle obstacle avoidance control law is as follows:
in order to enable the unmanned aerial vehicle to quickly converge onAnd (3) designing an obstacle avoidance guidance law of the unmanned aerial vehicle by adopting a multi-power sliding mode approach law according to the ideal distance of the wall surface. Defining the slip form surface as: where is rho-rhod，ρdIs the desired distance between the drone and the obstacle, and then has
Obviously, the system is a continuous time system, and a multi-power sliding mode approximation law is designed and is marked as follows:
wherein,
for the differentiation of the sliding-mode surface s, sgn () represents a sign function, c1,c2,c3,c4α, β, γ are coefficients of the approximation law and have c1＞0,c2＞0,c3＞0,c4> 0, α > 0,0 < β < 1, γ having the value:
then the unmanned plane keeps away the barrier guidance law and does
According to the exponential characteristic, the multi-power approach law divides the dynamic process of the system into four parts: the | s | is more than alpha, 1 is more than | s | and less than or equal to alpha, beta is more than | s | and less than or equal to 1, and | s | and less than or equal to beta. Therefore, proper parameters are selected in different stages, the system can be rapidly converged in different stages, the performance requirement of unmanned aerial vehicle obstacle avoidance on the rapidity of the control law is met, and meanwhile due to the existence of the gamma index item, the roll angle control quantity is gradually reduced along with the convergence of the state quantity distance, so that the system meets the requirement of the rapidity, meanwhile, the overshoot and oscillation are also reduced, and the excessive maneuver of the unmanned aerial vehicle is avoided.
Considering that a certain noise error exists in the obstacle azimuth angle measuring sensor, namely uncertainty exists in the measured data of the sensor, the noise error is assumed to be bounded, namely the output of the sensor is as follows:
where r is the noise of the sensor, σrIs the boundary of the maximum value of the sensor noise,
the measured value of the azimuth angle of the obstacle is actually measured for the sensor.
Will be provided with
Alternative psiwThe following can be obtained:
wherein:
to account for the differentiation of the detection distance between the drone and the obstacle wall, taking into account the sensor error
For the right side of the above formula, for psi + eta-psiwThe derivative, extreme, is shown on the right of the formula, i.e.
Thus a system that takes into account sensor error can be described as
Wherein
Because eta is determined by the installation position of the sensor, r is also determined by the accuracy of the sensor, the speed of the unmanned aerial vehicle is constant in flight, V is also an approximate constant value, and the error of the system is bounded. According to the interference stability and delimitation analysis of the multi-power sliding mode approach law, when the system is in the form of the above formula, if | d (t) | is less than or equal to sigma and sigma is more than 0, the state of the system can be converged to the following regions:
from the above derivation, the convergence of the system is demonstrated. When V is 10m/s, eta is 30 degrees and r is 5 degrees, a proper sliding mode approach law controller parameter is selected, the distance between the unmanned aerial vehicle and the obstacle and the ideal distance can be converged to the range with the error of 2m in limited time, and the obstacle avoidance performance requirement is met.
When unknown disturbance caused by sensor errors exists in the system and the disturbance is bounded, the system can be guaranteed to be converged in a stable boundary of the disturbance by adopting a multi-power sliding mode approximation law, but the property of being capable of being converged in a limited time is not guaranteed any more. Therefore, the invention needs to compensate the unknown disturbance existing in the system, the disturbance observer is designed to compensate the uncertain quantity in the sensor, and the control block diagram of the system is shown in fig. 2.
A system with unknown perturbations to:
designing a supercoiled skateThe model observer estimates an uncertain quantity d, the estimated value is
Is provided with
Defining an auxiliary sliding-mode variable x1Is x1＝ρ-ρnFurther has
ρnIs an estimated value of the detection distance between the unmanned aerial vehicle and the obstacle wall surface,
is ρnDifferentiation of (2).
Designing a superspiral sliding-mode observer as follows:
wherein: alpha is alpha1、β1For a coefficient, μ represents the square term of the synovial variable,
is the differential of μ.
The convergence time of the supercoiled system is less than T (x)0)，T(x0) Is a constant value, x, determined by the initial state0The initial state of state x at system 0 time is shown as
Wherein: τ is a constant related to the system state equation, v (x) represents the differential equation of the system, and t (x) represents the relationship between the convergence time and the system state.
Variable of auxiliary sliding mode
Can converge to 0 in a finite time, i.e. an estimate
The unknown disturbance d of the system can be approached in a limited time.
Simulation (Emulation)
And carrying out simulation verification on the unmanned aerial vehicle obstacle avoidance controller in MATLAB. The unmanned aerial vehicle who chooses for use is small-size fixed wing unmanned aerial vehicle, and the unmanned aerial vehicle parameter sets up to V11 m/s, eta 30, rhod100 m. The approach law parameters of a sliding mode controller in the obstacle avoidance guidance law are as follows: c. C1＝0.008,c2＝0.05,c3＝0.02,c4＝2，α＝2,β＝0.5。
Fig. 3 shows an obstacle avoidance trajectory of the unmanned aerial vehicle facing a straight wall, fig. 4(a) shows a detection distance response curve of the unmanned aerial vehicle obstacle avoidance, wherein a solid line is a parameter response curve of the multi-power sliding mode approach law obstacle avoidance controller designed herein, and a dotted line is an obstacle avoidance guidance law response curve designed by the traditional lyapunov backstepping method, and a distance simulation result between the unmanned aerial vehicle and an obstacle shows that the obstacle avoidance guidance law designed by the multi-power sliding mode approach law has the advantages of high response speed and small overshoot, and meanwhile, by combining the yaw angle of fig. 4(b) and the roll angle response curve of fig. 4(c), it can be found that the obstacle avoidance controller designed herein does not cause excessive maneuvering of the unmanned aerial vehicle and does not have an obvious oscillation process compared with the controller designed by the traditional lyapunov backstepping method.
Measured wall azimuth psi of the obstaclewThe noise with the signal y ═ sin (0.13t) is added as an uncertainty in the system. The parameters of the supercoiled observer were: alpha is alpha1＝4,β 110. The response curve of the supercoiled disturbance observer is shown in fig. 5, after a short oscillation process, the observer can quickly track the uncertain quantity in the upper system and finally converge, and fig. 6(a) shows the response of the detection distance between the unmanned aerial vehicle with and without disturbance compensation and the obstacleThe curve, as seen in combination with the roll angle response curve of fig. 6(b), reduces the oscillation of the system after the observer is added for compensation, and improves the obstacle avoidance quality of the unmanned aerial vehicle. The obstacle avoidance trajectory of the unmanned aerial vehicle facing the continuous canyon obstacle is shown in fig. 7, the unmanned aerial vehicle can automatically fly parallel to the wall surface and always fly near the left and right sides of the central line of the canyon until the unmanned aerial vehicle leaves the canyon corridor.
In conclusion, the real-time obstacle avoidance control strategy adopted by the invention has the characteristics of fast convergence to the parallel flight with the obstacle wall surface when facing the continuous wall surface obstacle, has small overshoot and no obvious oscillation process, keeps the stability of the unmanned aerial vehicle, and realizes the function of avoiding the continuous wall surface obstacle in real time.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A real-time obstacle avoidance control method for an unmanned aerial vehicle in a continuous obstacle environment is characterized by comprising the following steps:
(1) aiming at the characteristic that the unmanned aerial vehicle sensor can not acquire all information of obstacles under the continuous obstacle environment, the distance and the surface curvature of the obstacle are measured by the airborne sensors arranged on the eta azimuth angles on the left side and the right side of the x-axis direction of the unmanned aerial vehicle, and the specific formula is as follows:
where ρ is the distance between the drone and the obstacle wall detected by the sensor, V is the speed of the drone, ψ is the yaw angle of the drone, and g isConstant of gravitational acceleration, #wIs the azimuth angle of the obstacle wall surface, phi is the roll angle of the unmanned plane, VTTo detect the speed of movement of the focal spot along the surface of the obstacle,
for differentiating the detection distance, eta is the angle between the sensor orientation and the X-axis of the drone, determined by the sensor mounting position and angle, and is a constant value, i.e. the derivative of eta
(2) According to the horizontal and lateral kinematics equation of the unmanned aerial vehicle and the geometric position relation between the unmanned aerial vehicle and the continuous obstacles, a mathematical model of the unmanned aerial vehicle obstacle avoidance system is established:
(3) aiming at the unmanned aerial vehicle obstacle avoidance system in the step (2), designing an obstacle avoidance guidance law of the unmanned aerial vehicle by adopting a multi-power sliding mode approach law, and generating a roll angle instruction of the unmanned aerial vehicle; the multi-power sliding mode approximation law comprises three control variables:
wherein s is the control error between the distance between the unmanned aerial vehicle and the obstacle and the expected distance, alpha is more than 1, beta is more than 0 and less than 1,
the alpha, beta and gamma parameters determine the convergence speed and the oscillation degree of the system; the roll angle instruction is as follows:
where phi is the roll angle command for the drone and rho is the distance between the drone and the obstacleThe relative distance between the unmanned aerial vehicle and the laser sensor, V is the speed of the unmanned aerial vehicle, eta is the fixed angle between the laser sensor and the X axis of the body axis of the unmanned aerial vehicle, psi is the real-time azimuth angle of the unmanned aerial vehicle, psiwIs the relative azimuth of the obstacle surface;
(4) and tracking and estimating uncertain quantity in the system caused by the noise of the sensor by adopting the supercoiled sliding-mode observer, and compensating an estimated value of a detection distance between the unmanned aerial vehicle and the obstacle wall surface to ensure that the system is converged within a limited time.
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