CN113625762A - Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system - Google Patents
Unmanned aerial vehicle obstacle avoidance method and system, and unmanned aerial vehicle cluster obstacle avoidance method and system Download PDFInfo
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Abstract
The invention provides an unmanned aerial vehicle obstacle avoidance method and system and an unmanned aerial vehicle cluster obstacle avoidance method and system, and the method mainly comprises the following steps: acquiring position information of a dynamic barrier; based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle; determining a collision time interval between the drone and the dynamic barrier based on the flight trajectory; and determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area. According to the method, modeling is not needed, and the flight track of the dynamic obstacle can be inversely predicted only according to the position information and the quasi-linear variable parameter model, so that the purposes of accurately predicting the moving track of the dynamic obstacle and accurately avoiding the obstacle are achieved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle dynamic obstacle avoidance, in particular to an unmanned aerial vehicle obstacle avoidance method and system and an unmanned aerial vehicle cluster obstacle avoidance method and system.
Background
At present, unmanned aerial vehicle all shows wide application prospect in a plurality of application fields such as infrastructure detection, underground mineral deposit are surveyed, accident search and rescue, survey and drawing and accurate agriculture. In a general scene, the flight mission of the unmanned aerial vehicle does not consider the influence of a dynamic obstacle, but the actual situations are just opposite, and a flight accident is often caused by the dynamic obstacle, so that in a complex environment, a plurality of aircrafts are required to form a cooperative flight to deal with an unknown aircraft together, and the formation of the unmanned aerial vehicle needs to determine the moving track of the dynamic obstacle.
In the technical field of unmanned aerial vehicle dynamic obstacle avoidance, a nonlinear prediction model is mainly adopted to predict the track of a moving obstacle, but the method is limited to the nonlinear prediction model and is not suitable for predicting unknown obstacles and dynamic obstacles with unknown tracks.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle obstacle avoidance method and system and an unmanned aerial vehicle cluster obstacle avoidance method and system, which can accurately predict the moving track of a dynamic obstacle and achieve the aim of accurately avoiding the obstacle.
In order to achieve the purpose, the invention provides the following scheme:
an obstacle avoidance method for an unmanned aerial vehicle comprises the following steps:
acquiring position information of a dynamic barrier;
based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle;
determining a collision time interval between the drone and the dynamic barrier based on the flight trajectory;
and determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
Optionally, the acquiring the position information of the dynamic obstacle specifically includes:
and acquiring the position information of the dynamic obstacle based on the laser radar and the binocular camera.
Optionally, the inversely predicting the flight trajectory of the dynamic obstacle based on the position information and the quasi-linear parameter varying model specifically includes:
determining a state equation met by the flight path of the dynamic obstacle based on the quasi-linear parameter varying model;
calculating a dynamic obstacle acceleration and a dynamic obstacle velocity based on the position information; the position information at least comprises continuously acquired 5 position point information;
calculating system parameters and thrust acceleration of the dynamic obstacle according to the state equation, the acceleration of the dynamic obstacle and the speed of the dynamic obstacle;
and according to the system parameters and the thrust acceleration of the dynamic obstacle, carrying out inversion prediction on the flight track of the dynamic obstacle.
Optionally, determining, based on the flight trajectory, a collision time interval between the unmanned aerial vehicle and the dynamic obstacle specifically includes:
calculating the dynamic distance between the unmanned aerial vehicle and the dynamic obstacle in any global dimension based on the flight trajectory;
under the calibration dimension, determining the time interval of which the dynamic distance is smaller than the distance of the collision area as a local time interval; the calibration dimension is any dimension;
and performing union operation on all the local time intervals to form a collision time interval.
Optionally, determining whether the unmanned aerial vehicle enters the collision area based on the collision time interval specifically includes:
judging whether the local time intervals under different dimensions have intersection or not;
if so, the unmanned aerial vehicle enters a collision area;
if not, the unmanned aerial vehicle does not enter the collision area.
Optionally, when the unmanned aerial vehicle enters the collision area, a corresponding obstacle avoidance scheme is selected according to a relative position between the unmanned aerial vehicle and the dynamic obstacle, and the method specifically includes:
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the horizontal forward direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust in the positive direction of the Z axis for the unmanned aerial vehicle;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the left direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust of the unmanned aerial vehicle in the X-axis negative direction;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the right direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the Y-axis negative direction putting thrust for the unmanned aerial vehicle;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the horizontal forward and backward direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust in the original direction for the unmanned aerial vehicle.
Optionally, the obstacle avoidance scheme selected in the terrestrial coordinate system is a smooth track moving scheme with acceleration in the first half and deceleration in the second half.
An unmanned aerial vehicle keeps away barrier system includes:
the position information acquisition module is used for acquiring the position information of the dynamic barrier;
the inversion prediction module is used for performing inversion prediction on the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model;
a collision time interval determination module for determining a collision time interval between the unmanned aerial vehicle and the dynamic obstacle based on the flight trajectory;
and the obstacle avoidance scheme selection module is used for determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
An unmanned aerial vehicle cluster obstacle avoidance method comprises the following steps:
acquiring position information of a dynamic barrier;
based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle;
determining a collision time interval between a piloted unmanned aerial vehicle and the dynamic barrier based on the flight trajectory;
determining a dynamic distance between the calibrated unmanned aerial vehicle and a dynamic obstacle; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier;
and determining whether the piloted unmanned aerial vehicle enters a collision area or not based on the collision time interval, selecting a corresponding obstacle avoidance scheme according to the relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision area, and determining the duration time of thrust increase in the obstacle avoidance scheme according to the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle.
An unmanned aerial vehicle cluster obstacle avoidance system, comprising:
the position information acquisition module is used for acquiring the position information of the dynamic barrier;
the inversion prediction module is used for performing inversion prediction on the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model;
a collision time interval determination module for determining a collision time interval between the piloted unmanned aerial vehicle and the dynamic barrier based on the flight trajectory;
the dynamic distance determining module is used for determining the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic barrier; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier;
an obstacle avoidance scheme selection module, configured to determine whether the piloted unmanned aerial vehicle enters a collision region based on the collision time interval, select a corresponding obstacle avoidance scheme according to a relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision region, and determine a duration time of thrust increase in the obstacle avoidance scheme according to a dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
in order to achieve the purpose, the invention provides the following scheme:
the invention provides an unmanned aerial vehicle obstacle avoidance method and system and an unmanned aerial vehicle cluster obstacle avoidance method and system, and the method mainly comprises the following steps: acquiring position information of a dynamic barrier; based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle; determining a collision time interval between the drone and the dynamic barrier based on the flight trajectory; and determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area. According to the method, modeling is not needed, and the flight track of the dynamic obstacle can be inversely predicted only according to the position information and the quasi-linear variable parameter model, so that the purposes of accurately predicting the moving track of the dynamic obstacle and accurately avoiding the obstacle are achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle obstacle avoidance method of the present invention;
FIG. 2 is a schematic view of the operating principle of the binocular camera of the present invention;
FIG. 3 is a schematic diagram of the laser radar of the present invention;
FIG. 4 is a schematic view of the present invention determining a collision zone and a safety zone based on flight trajectory;
fig. 5 is a schematic view of a flight path of the obstacle avoidance scheme 1) of the present invention;
fig. 6 is a schematic view of flight trajectories of the obstacle avoidance scheme 2) and the obstacle avoidance scheme 3) of the present invention;
fig. 7 is a schematic structural diagram of an unmanned aerial vehicle obstacle avoidance system of the present invention;
fig. 8 is a control timing diagram of the unmanned aerial vehicle obstacle avoidance system of the present invention;
fig. 9 is a schematic flow chart of the unmanned aerial vehicle cluster obstacle avoidance method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an unmanned aerial vehicle obstacle avoidance method and system and an unmanned aerial vehicle cluster obstacle avoidance method and system, which can accurately predict the moving track of a dynamic obstacle and achieve the aim of accurately avoiding the obstacle.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The core idea of the invention is as follows: firstly, positioning position data of a dynamic barrier based on a laser radar and a binocular camera together; secondly, applying a Newton's second law of classical mechanics and an aerodynamic principle, considering external factors (wind speed) and dynamic barrier internal power, providing a quasi-linear variable parameter model to calculate important parameters of the dynamic barrier, and then inverting and predicting the flight path of the dynamic barrier; and then determining a collision area based on the flight track, and when the unmanned aerial vehicle enters the collision area, optimizing and iteratively adjusting thrust by using the flight track, and smoothly controlling the unmanned aerial vehicle to dynamically avoid obstacles.
Example one
Referring to fig. 1, the obstacle avoidance method for the unmanned aerial vehicle provided in this embodiment specifically includes the following steps.
Step 101: position information of the dynamic obstacle is acquired.
The embodiment is based on the fact that the position information of the dynamic barrier is obtained by the laser radar and the binocular camera together; the position information at least comprises continuously collected 5 position point information.
Because the laser radar has the advantages of close positioning, limited detection range, high speed and the like, and the binocular camera has the characteristic of long-distance image positioning, the laser radar is adopted to work in a close range, and the binocular camera is adopted to work in a long range, so that the purposes of complementary advantages and accurate acquisition of dynamic obstacle position information are achieved.
Fig. 2 illustrates the working principle of a binocular camera, wherein f denotes the focal length of the camera; xl and xr respectively represent pixel points of the left camera and the right camera; the parallax d is xl-xr.
The depth is as follows: z ═ f × b/(xl-xr) ═ f × b/d; x-xl z/f or b + xr z/f, with the Y axis perpendicular to the plane, Y-yl z/f or yr z/f.
Fig. 3 illustrates the working principle of the laser radar, wherein the rotating mechanism rotates to emit laser, records the azimuth and calculates the receiving and sending time difference through a timer to position the dynamic obstacle.
Step 102: and inversely predicting the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model.
The sum of the speed v and the wind speed w of the dynamic obstacle when the relative speed of the dynamic obstacle in the air is no wind, and the acceleration a of the dynamic obstacle is determined by the speed v, the wind speed w and the thrust acceleration a of the dynamic obstaclefAre determined jointly.
The state equation satisfied by the flight trajectory of the dynamic obstacle predicted based on the quasi-linear variable parameter model in the embodiment is shown in formula (1). The quasi-linear parameter-varying model is constructed based on the classical Newton's second law, aerodynamics, external factors and dynamic barrier intrinsic dynamics.
The matrix A represents the quasi-variable parameters of the state equation and changes along with the change of the relative speed; matrix B represents a control matrix, vector
Representing a state vector, and a vector u representing an input control quantity;
system parameter
w represents the wind speed in nature, mobsRepresenting the mass of the dynamic obstacle, CdDenotes a damping coefficient, ρ denotes an air density, S denotes a wind resistance area of the dynamic obstacle, and v denotes a moving speed of the dynamic obstacle.
The next state of the moving object (dynamic obstacle) is deduced from the previous state, afIs the acceleration of the moving object itself (referred to as endogenous motive force). The air resistance is related to the speed of the moving object and also to the external wind speed.
Air resistance acceleration:
here, the velocity V represents a relative velocity: v + Δ t · a + w.
Equations (1) and (2) are fused here in order to construct an equation of state describing the motion of the moving object. A represents a state parameter (the control system is called a system matrix), and equation (0) is generally a linear equation, where the matrix a is constantly changing, so that the quasi-linear state system is called to study nonlinearity (obstacle moving track nonlinearity).
Newton law of motion, general formula:
xk
based on this, the system parameter Q and the thrust acceleration afIs determined by iterative differencing according to equation (3).
Wherein x iskPosition point, x, representing the kth dynamic obstaclek+1Position estimation point, x, representing the k +1 th dynamic obstaclek+2Position estimation point, x, representing the k +2 th dynamic obstaclek+3A position estimation point representing the (k + 3) th dynamic obstacle, and δ representing a dynamic obstacle position estimation point xk+1Position point x relative to dynamic obstaclekPosition estimation point x of dynamic obstaclek+3The weight coefficient of (a) is,
good-display dynamic obstacle position estimation point xk+2Position point x relative to dynamic obstaclekPosition estimation point x of dynamic obstaclek+3The weight coefficient of (a) is,
and δ and
0.3 and 0.6, respectively, and f represents the resultant force excluding the frictional damping. Rk represents the difference between displacement increments, here primarily in the mathematical sense. x is the number ofkDenotes the 1 st sample point x1Corresponds to a1，v1；xk+1Denotes the 2 nd sample point x2Corresponds to a2，v2；xk+2Represents the 3 rd sample point x3Corresponds to a3，v3；δ，
Weight coefficients for estimation, mathematical meaning.
First, a is obtained from the expressions (4c), (4d) and (4e)1，a2，a3(ii) a Secondly, a is mixed1，a2，a3Substituting the formula (4b) to obtain f; then substituting f into formula (4a) to obtainGo out v1，v2，v3. Finally, the thrust acceleration a is calculated according to the f and the formula (2)fWill accelerate the speed a1，a2，a3Velocity v1，v2，v3And thrust acceleration afThe system parameter Q is obtained by substituting the formula (1), and the flight path of the dynamic barrier can be predicted and predicted in an inversion mode.
In summary, the specific implementation process of step 102 is as follows: determining a state equation met by the flight path of the dynamic obstacle based on the quasi-linear parameter varying model; calculating a dynamic obstacle acceleration and a dynamic obstacle velocity based on the position information; the position information at least comprises continuously acquired 5 position point information; calculating system parameters and thrust acceleration of the dynamic obstacle according to the state equation, the acceleration of the dynamic obstacle and the speed of the dynamic obstacle; and according to the system parameters and the thrust acceleration of the dynamic obstacle, carrying out inversion prediction on the flight track of the dynamic obstacle.
Step 103: determining a collision time interval between the drone and the dynamic barrier based on the flight trajectory.
S.t.i＝1，2，...，G；
J≥Dzone；
S.t.i＝1，2，...，L；
J′≥Dsafe；
The collision zone comprises collision distances of three dimensions (x, y and z axes). J' (theta) represents the relative distance between the unmanned aerial vehicle and the dynamic obstacle in the local section，BG、UGRepresenting a set of global state points, BL、ULRepresenting a set of local state points, DzoneRepresenting a global safe collision zone distance parameter, DsafeA local safety distance parameter is indicated.
Step 103 specifically comprises:
calculating the dynamic distance between the unmanned aerial vehicle and the dynamic obstacle in any global dimension based on the flight trajectory; under the calibration dimension, determining the time interval of which the dynamic distance is smaller than the distance of the collision area as a local time interval; the calibration dimension is any dimension; and performing union operation on all the local time intervals to form a collision time interval. Fig. 4 illustrates collision zone determination and safe distance constraint based on flight trajectory.
Step 104: and determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
Based on the collision time interval, it is first determined whether there is a collision warning. If the unmanned aerial vehicle does not intersect with the dynamic barrier, no collision risk is judged. If in the collision interval, whether the three-dimensional space distance between the unmanned aerial vehicle and the dynamic barrier is smaller than the safe distance or not is further judged, and whether collision occurs or not is judged.
Wherein, the determining whether the unmanned aerial vehicle enters the collision area based on the collision time interval specifically includes: judging whether the local time intervals under different dimensions have intersection or not; if so, the unmanned aerial vehicle enters a collision area; if not, the unmanned aerial vehicle does not enter the collision area.
In the routine flight task process of the unmanned aerial vehicle, when a dynamic barrier appears at the front part, a binocular camera and a laser radar on the unmanned aerial vehicle acquire the position information of the dynamic barrier and predict the flight track of the dynamic barrier, once the unmanned aerial vehicle enters a collision area through prediction, an unmanned aerial vehicle control system further calculates whether to enter a dangerous area, if so, the optimal flight path needs to be selected, the moving track is adjusted, and the dynamic barrier is smoothly avoided. Unmanned aerial vehicle gets into the collision zone and dynamic distance is less than safe distance, and the scheme that unmanned aerial vehicle kept away the barrier dispatch safely divide into four:
1) when the unmanned aerial vehicle is in the positive preceding direction of level and when within 45 degrees interval scope for the flight path of dynamic barrier, the obstacle avoidance scheme of selecting under the earth coordinate system is that unmanned aerial vehicle increases the thrust in Z axle positive direction, exerts certain acceleration in Z axle positive direction promptly.
2) When the flight path of the unmanned aerial vehicle relative to the dynamic obstacle is in the left direction and the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust of the unmanned aerial vehicle in the X-axis negative direction, namely, applies a certain acceleration in the X-axis negative direction.
3) When the unmanned aerial vehicle is in the range of +/-45 degrees relative to the right direction of the flight path of the dynamic obstacle, the obstacle avoidance scheme selected under the terrestrial coordinate system is used for increasing the thrust in the Y-axis negative direction of the unmanned aerial vehicle, namely applying a certain acceleration in the Y-axis negative direction.
4) When the flight path of the unmanned aerial vehicle relative to the dynamic obstacle is in the horizontal forward and backward direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust in the original direction for the unmanned aerial vehicle, namely, applies a certain acceleration in the original direction.
The calculation process of the obstacle avoidance scheme 4) is as follows:
determining the flight speed and flight time of the unmanned aerial vehicle, calculating the dynamic distance, and accelerating the front half of the unmanned aerial vehicle and decelerating the rear half of the unmanned aerial vehicle.
All follow and ensure unmanned aerial vehicle safety and go out, do not advise unmanned aerial vehicle from the front lower place of dynamic barrier flight path and pass through with higher speed, easily take place the collision accident. Because the flight path of the dynamic obstacle has errors, and the flight path of the dynamic obstacle may change suddenly, the uncertainty factor may cause the explosive event.
Smoothing the track:
to keeping away barrier scheme 1), unmanned aerial vehicle increases thrust in Z axle positive direction (vertical upwards). Considering that the unmanned aerial vehicle meets the dynamic barrier in the horizontal direction, suppose that the meeting time t in the X-axis direction1Y-axis direction meeting time t2And t is1≤t2. At t1The dynamic barrier height at the moment is h1At this moment, the flying height of the unmanned aerial vehicle should be h1+dsafe(dsafeRepresenting safe distance), defining the flight position of the unmanned aerial vehicle at the moment as a safe obstacle avoidance target position, and continuing | | t1-t2And the I time avoids collision between the unmanned aerial vehicle and a dynamic obstacle due to early recovery of the original flight state. Similarly, obstacle avoidance scheme 2), and obstacle avoidance scheme 3). At t1Time t2In order to realize smooth transition, the first half-way acceleration and the second half-way deceleration are adopted; the obstacle avoidance scheme 1) can be referred to as fig. 5, and the obstacle avoidance scheme 2) and the obstacle avoidance scheme 3) can be referred to as fig. 6.
For obstacle avoidance scheme 4), iteration is performed, and d is increased in the X and Y directions in the horizontal direction respectively when the relative distance between the unmanned aerial vehicle and the dynamic obstacle is smaller than the safe distancesafeAnd ends up leaving the collision zone. The unmanned aerial vehicle dynamically avoids dynamic obstacles, and the unmanned aerial vehicle and the dynamic obstacles are required to be staggered at the same time, so that collision does not occur.
In summary, the obstacle avoidance schemes selected in the terrestrial coordinate system are all smooth track moving schemes with acceleration in the first half and deceleration in the second half.
Example two
This embodiment provides an unmanned aerial vehicle keeps away barrier system, as shown in fig. 7, include:
a position information obtaining module 701, configured to obtain position information of the dynamic obstacle.
And an inversion prediction module 702, configured to inversely predict the flight trajectory of the dynamic obstacle based on the position information and the quasi-linear parameter varying model.
A collision time interval determination module 703, configured to determine, based on the flight trajectory, a collision time interval between the drone and the dynamic obstacle.
And an obstacle avoidance scheme selecting module 704, configured to determine whether the unmanned aerial vehicle enters a collision area based on the collision time interval, and select a corresponding obstacle avoidance scheme according to a relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
The system control timing chart is shown in fig. 8.
EXAMPLE III
When unmanned aerial vehicle cluster distributed execution task, keep mutual independence between the unmanned aerial vehicle, in order to ensure that the flight task is accomplished, require unmanned aerial vehicle formation flight to keep the uniformity simultaneously. The unmanned aerial vehicle cluster elects the piloting unmanned aerial vehicle, and other unmanned aerial vehicles are taken as following unmanned aerial vehicles, so that once the piloting unmanned aerial vehicle explodes the aircraft unluckily, a new piloting unmanned aerial vehicle is deduced by an optimization algorithm.
When unmanned aerial vehicle cluster formation carries out the flight task, follow unmanned aerial vehicle and follow pilot unmanned aerial vehicle, because often have communication delay or external disturbance, and destroy cluster formation, follow unmanned aerial vehicle's actual flight orbit and regulation route and have great error, the advantage lies in known unmanned aerial vehicle's control system, need not the reconsitution system state, pilot unmanned aerial vehicle only need send target position information and control input, even if there is information delay or interference in the flight process, do not influence at all and follow unmanned aerial vehicle and keep cluster formation.
When a sensor carried by a piloting unmanned aerial vehicle senses that a dynamic barrier exists in a flight space, the position information of the dynamic barrier is dynamically captured, a predictor is responsible for predicting the flight track of the dynamic barrier and judging whether a collision risk exists or not, an optimizer is responsible for selecting an optimal flight path, namely an obstacle avoidance scheme, a controller is responsible for braking and simultaneously transmitting the next flight path to a following unmanned aerial vehicle, and under the special condition, the piloting unmanned aerial vehicle can reserve a safe flight area for the following unmanned aerial vehicle.
The following unmanned aerial vehicle receives the power output signal of the piloting unmanned aerial vehicle, keeps the flight synchronization with the piloting unmanned aerial vehicle, and simultaneously sends the next flight target position to the following unmanned aerial vehicle. This operation advantage lies in following unmanned aerial vehicle and also possess certain autonomic ability at the passive receipt flight instruction simultaneously, helps keeping the flight queue, promotes the performance of cluster safe flight.
The obstacle avoidance method provided by the embodiment one considers a single unmanned aerial vehicle, and the unmanned aerial vehicle cluster obstacle avoidance method provided by the embodiment is the same as the single unmanned aerial vehicle obstacle avoidance scheme, except that the relative position of the unmanned aerial vehicle close to the dynamic obstacle and the dynamic obstacle is considered when the obstacle is avoided.
Referring to fig. 9, the unmanned aerial vehicle cluster obstacle avoidance method provided in this embodiment includes the following steps:
step 901: position information of the dynamic obstacle is acquired.
Step 902: and inversely predicting the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model.
Step 903: and determining a collision time interval between the piloted unmanned aerial vehicle and the dynamic barrier based on the flight track.
Step 904: determining a dynamic distance between the calibrated unmanned aerial vehicle and a dynamic obstacle; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier.
Step 905: and determining whether the piloted unmanned aerial vehicle enters a collision area or not based on the collision time interval, selecting a corresponding obstacle avoidance scheme according to the relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision area, and determining the duration time of thrust increase in the obstacle avoidance scheme according to the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle.
As shown in fig. 6, the three drones select which obstacle avoidance scheme is determined by the piloting drone uav 1.
Obstacle avoidance scheme 1: considering that the piloting unmanned aerial vehicle uav1 arrives at the safe obstacle avoidance target position, after the horizontal flight duration time is intersected by the directions of the x axis and the y axis of the following unmanned aerial vehicle uav2 and the following unmanned aerial vehicle uav3, the cluster recovers the previous flight state, and the collision danger caused by the fact that the unmanned aerial vehicle cluster flies downwards too early (recovers the original flight state) is avoided.
Similarly, the obstacle avoidance scheme 2 considers the position relationship of the following unmanned aerial vehicle uav3 relative to the dynamic obstacle, and the obstacle avoidance scheme 3 considers the position relationship of the following unmanned aerial vehicle uav2 and the dynamic obstacle. The obstacle avoidance scheme 4 considers the relative positional relationship of the following drone uav2 and the following drone uav3 with the dynamic obstacle.
Example four
This embodiment provides an unmanned aerial vehicle cluster keeps away barrier system, includes:
and the position information acquisition module is used for acquiring the position information of the dynamic barrier.
And the inversion prediction module is used for performing inversion prediction on the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model.
And the collision time interval determining module is used for determining a collision time interval between the piloting unmanned aerial vehicle and the dynamic barrier based on the flight track.
The dynamic distance determining module is used for determining the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic barrier; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier.
And the obstacle avoidance scheme selection module is used for determining whether the piloted unmanned aerial vehicle enters a collision area or not based on the collision time interval, selecting a corresponding obstacle avoidance scheme according to the relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision area, and determining the duration time of thrust increase in the obstacle avoidance scheme according to the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle.
Compared with the prior art, the invention has the following technical effects:
1. integrate laser radar and two mesh cameras and keep away among the barrier system to unmanned aerial vehicle developments, fused laser radar and visual positioning data information, the two advantage is complementary, improves the positioning accuracy.
2. The method adopts a basic scientific theory and starts from practice to solve the problem of the moving track of the dynamic barrier. And (3) predicting the moving track of the dynamic barrier by utilizing the classical Newton's second law, aerodynamics and the force analysis of a moving object. The method calculates the basic attribute parameters of the dynamic barrier and inverts the track through a basic theoretical formula, and does not need to carry out a large amount of iteration and construct various dynamic barrier models.
The latest unmanned aerial vehicle dynamic obstacle avoidance literature (first-zone SCI) adopts a high-speed tracking camera to capture a dynamic obstacle motion track in a working chamber, a library with several model tracks is constructed, a high-speed camera is still required to capture an initial state of a moving obstacle in an experiment, and then the mode identification judges which special track belongs to the track model library. The existing dynamic obstacle avoidance technology is too limited, auxiliary positioning is needed, and the number of tracks in a model track library is limited. According to the method, the future motion state of the barrier can be well predicted by calculating the motion essence of the dynamic barrier through analyzing the motion state of the dynamic barrier without modeling in advance.
3. And through coarse and fine particle size collision prediction, the prediction efficiency is improved, and a collision zone and a safe flight distance are defined. And (5) going deep step by step, firstly judging whether the automobile enters a collision area, and then judging whether the automobile touches a safe distance. If the previous step does not occur, the next step is not entered. Firstly, judging whether collision is possible in the three-dimensional xyz axis direction when the time and the space are the same, roughly judging, and if a collision area exists, iteratively calculating the relative distance between the unmanned aerial vehicle and the obstacle in the collision area, and further judging. The fine judgment iteration interval is not a global but a local collision area, so that the calculation amount is reduced, and the fine judgment needs iteration to obtain the optimal smooth flight path. Experiments prove that the combination of the thickness and the granularity judges whether the collision efficiency is higher than that of the whole iteration only through the fine granularity.
4. The selection and optimization of the obstacle avoidance path can enable the unmanned aerial vehicle to fly smoothly, and the electric quantity of the unmanned aerial vehicle is saved. The obstacle avoidance flight distance and the corresponding flight time are calculated firstly, the first half-way uniform acceleration and the second half-way uniform deceleration are regulated, and a smooth track can be seen from the starting point to the end point. The traditional obstacle avoidance only carries out blind acceleration and deceleration towards a certain direction, does not have precise and specific flight path, and usually flies a distance more for safety.
5. The pilot unmanned aerial vehicle calculates and plans at first and keeps away the barrier orbit, sends the nearest barrier target point of keeping away and follows unmanned aerial vehicle to the real-time thrust output of pilot unmanned aerial vehicle motor also sends and follows unmanned aerial vehicle, plays real-time collaborative flight, in case because external noise interference, thrust can't in time transmit to follow unmanned aerial vehicle, and at this moment, follow unmanned aerial vehicle still can make autonomic reaction according to the barrier target point of keeping away who receives. Conventional clusters do not have this flight capability.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. An unmanned aerial vehicle obstacle avoidance method is characterized by comprising the following steps:
acquiring position information of a dynamic barrier;
based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle;
determining a collision time interval between the drone and the dynamic barrier based on the flight trajectory;
and determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
2. The unmanned aerial vehicle obstacle avoidance method according to claim 1, wherein the acquiring of the position information of the dynamic obstacle specifically includes:
and acquiring the position information of the dynamic obstacle based on the laser radar and the binocular camera.
3. The unmanned aerial vehicle obstacle avoidance method according to claim 1, wherein the inversion prediction of the flight trajectory of the dynamic obstacle based on the position information and the pseudo-linear parameter varying model specifically comprises:
determining a state equation met by the flight path of the dynamic obstacle based on the quasi-linear parameter varying model;
calculating a dynamic obstacle acceleration and a dynamic obstacle velocity based on the position information; the position information at least comprises continuously acquired 5 position point information;
calculating system parameters and thrust acceleration of the dynamic obstacle according to the state equation, the acceleration of the dynamic obstacle and the speed of the dynamic obstacle;
and according to the system parameters and the thrust acceleration of the dynamic obstacle, carrying out inversion prediction on the flight track of the dynamic obstacle.
4. The obstacle avoidance method for the unmanned aerial vehicle according to claim 1, wherein the determining a collision time interval between the unmanned aerial vehicle and the dynamic obstacle based on the flight trajectory specifically comprises:
calculating the dynamic distance between the unmanned aerial vehicle and the dynamic obstacle in any global dimension based on the flight trajectory;
under the calibration dimension, determining the time interval of which the dynamic distance is smaller than the distance of the collision area as a local time interval; the calibration dimension is any dimension;
and performing union operation on all the local time intervals to form a collision time interval.
5. The obstacle avoidance method for the unmanned aerial vehicle according to claim 4, wherein the determining whether the unmanned aerial vehicle enters the collision area based on the collision time interval specifically includes:
judging whether the local time intervals under different dimensions have intersection or not;
if so, the unmanned aerial vehicle enters a collision area;
if not, the unmanned aerial vehicle does not enter the collision area.
6. The obstacle avoidance method for the unmanned aerial vehicle according to claim 4, wherein when the unmanned aerial vehicle enters the collision area, a corresponding obstacle avoidance scheme is selected according to a relative position between the unmanned aerial vehicle and the dynamic obstacle, specifically comprising:
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the horizontal forward direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust in the positive direction of the Z axis for the unmanned aerial vehicle;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the left direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust of the unmanned aerial vehicle in the X-axis negative direction;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the right direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the Y-axis negative direction putting thrust for the unmanned aerial vehicle;
when the flight path of the unmanned aerial vehicle relative to the dynamic barrier is in the horizontal forward and backward direction and within the range of +/-45 degrees, the obstacle avoidance scheme selected under the terrestrial coordinate system increases the thrust in the original direction for the unmanned aerial vehicle.
7. The unmanned aerial vehicle obstacle avoidance method according to claim 6, wherein the obstacle avoidance scheme selected in the terrestrial coordinate system is a smooth track moving scheme with acceleration in the first half and deceleration in the second half.
8. The utility model provides an unmanned aerial vehicle keeps away barrier system which characterized in that includes:
the position information acquisition module is used for acquiring the position information of the dynamic barrier;
the inversion prediction module is used for performing inversion prediction on the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model;
a collision time interval determination module for determining a collision time interval between the unmanned aerial vehicle and the dynamic obstacle based on the flight trajectory;
and the obstacle avoidance scheme selection module is used for determining whether the unmanned aerial vehicle enters a collision area or not based on the collision time interval, and selecting a corresponding obstacle avoidance scheme according to the relative position between the unmanned aerial vehicle and the dynamic obstacle when the unmanned aerial vehicle enters the collision area.
9. An unmanned aerial vehicle cluster obstacle avoidance method is characterized by comprising the following steps:
acquiring position information of a dynamic barrier;
based on the position information and a quasi-linear parameter varying model, carrying out inversion prediction on the flight track of the dynamic obstacle;
determining a collision time interval between a piloted unmanned aerial vehicle and the dynamic barrier based on the flight trajectory;
determining a dynamic distance between the calibrated unmanned aerial vehicle and a dynamic obstacle; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier;
and determining whether the piloted unmanned aerial vehicle enters a collision area or not based on the collision time interval, selecting a corresponding obstacle avoidance scheme according to the relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision area, and determining the duration time of thrust increase in the obstacle avoidance scheme according to the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle.
10. The utility model provides an unmanned aerial vehicle cluster keeps away barrier system which characterized in that includes:
the position information acquisition module is used for acquiring the position information of the dynamic barrier;
the inversion prediction module is used for performing inversion prediction on the flight track of the dynamic obstacle based on the position information and the quasi-linear parameter varying model;
a collision time interval determination module for determining a collision time interval between the piloted unmanned aerial vehicle and the dynamic barrier based on the flight trajectory;
the dynamic distance determining module is used for determining the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic barrier; the calibration unmanned aerial vehicle is the unmanned aerial vehicle closest to the dynamic barrier;
and the obstacle avoidance scheme selection module is used for determining whether the piloted unmanned aerial vehicle enters a collision area or not based on the collision time interval, selecting a corresponding obstacle avoidance scheme according to the relative position between the piloted unmanned aerial vehicle and the dynamic obstacle when the piloted unmanned aerial vehicle enters the collision area, and determining the duration time of thrust increase in the obstacle avoidance scheme according to the dynamic distance between the calibrated unmanned aerial vehicle and the dynamic obstacle.
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