Method for controlling the trajectory of an aircraft

Abstract

One aspect of the invention relates to a method for controlling an aircraft (A) comprising at least one camera and a guidance system configured to indicate a trajectory to the aircraft (A), the method being implemented by computer and being characterized in that it comprises the following steps: Acquisition of a distance (D) between the aircraft (A) and an obstacle (O) on the trajectory (Ta) of the aircraft (A) and a time before collision of the aircraft (A) with the obstacle (O) from images of the obstacle (O) obtained by the camera; Estimation of at least a radial velocity (Vr) of the obstacle (O) using an extended Kalman filter taking as input said distance (D) and said time before collision of the aircraft (A) with the obstacle (O); Modification of the trajectory of the aircraft (A) by the guidance system.based on the estimated radial velocity (Vr) of the obstacle (O) and the distance (D) between the aircraft (A) and the obstacle (O) in order to avoid a collision between the aircraft (A) and the obstacle (O). Figure to be published with the abbreviation: Figure 2,

Description

Title of the invention: Method for controlling the trajectory of an aircraft TECHNICAL FIELD OF THE INVENTION

[0001] The technical field of the invention is that of the navigation of flying objects and in particular aircraft.

[0002] The present invention relates to a method for controlling the trajectory of an aircraft, and in particular a method for controlling the trajectory by radial velocity estimation based on an extended Kalman filter with input measurements of the distance between the aircraft and the obstacle and the "time to collision" between the aircraft and the obstacle. TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0003] In aircraft navigation, the "detect and avoid" function is performed based on distance measurements between the aircraft and an obstacle to be avoided on the aircraft's trajectory.

[0004] Such a function makes it possible to perceive or detect (“detect”) a future air traffic conflict that could lead to a collision and to take appropriate measures to avoid the occurrence of said conflict (“avoid”).

[0005] Active systems exist, such as extremely high frequency radars (in English, "Millimeter-Wave radar" or "MMW radar") or laser radars (in English, "laser radar" or "LADAR"). These active systems are bulky and expensive, which is undesirable for installation on light aircraft such as drones or on aircraft for which weight reduction is a priority, such as light aircraft.

[0006] Passive obstacle detection systems have therefore been developed, using, for example, EO / IR (Electro-Optical / Infrared) sensors. Among the passive systems, some provide the distance between the aircraft and the obstacle, while others provide the time to collision (TTC) between the aircraft and the obstacle. The time to collision, also abbreviated as TTC, is also known, depending on the context, as time to go (TTG) and time to arrival (TTA).

[0007] The "time to collision" is defined as the time remaining before a collision between the aircraft and the obstacle occurs if their two trajectories and speeds are not modified.

[0008] The distance can, for example, be obtained by: • An estimate using artificial intelligence through image analysis, such as Described in FR3094081A1 "Passive time-to-collision estimation method for an aircraft or any guided flying object, associated navigation method," in which the time to collision is estimated by analyzing images from an onboard camera. The analysis is performed by a neural network trained to estimate the time to collision from the size of the obstacle in pixels. The distance can be estimated for each image. A drawback of this technique is that the resulting time to collision and distance between the aircraft and the obstacle are imprecise. • Passive telemetry, also called “passive ranging.” For example, an “azimuth tracking” method can be used, as presented in [“Utilization of modified polar coordinates for bearings-only tracking” - VJ AIDALA, SE HAMMEL, IEEE, 2007, pp. 741–752, doi: 10.1109 / 9780470544198.ch74]. Azimuth tracking reconstructs the obstacle's trajectory from azimuth measurements taken by the aircraft. • the use of RPEKF filter banks (from English) Range-parameterized extended Kalman filter (RPEKF) initialized on a priori distances, as presented in [“Recursive Bayesian Estimation - Bearings-only Applications” - R. KARLSSON, F. GUSTAFSSON, IEEE, November 2005, D01:10.1049 / ip-rsn:20045073]. RPEKF can give good results if the distance is observable, since the obstacle is only tracked angularly, and if the metadata is of very high quality, because the goal is to obtain three-dimensional information from only two angular values ​​and by observing the obstacle's trajectory projected onto the image plane of the optronic sensor.

[0009] Radial velocity accuracy is an important system requirement for integrating an aircraft into air traffic. To achieve good radial velocity accuracy—that is, sufficient radial velocity accuracy for effective "detect and avoid" functionality in air traffic—it is necessary to be able to extract the distance and time to collision with sufficient accuracy. The time to collision is generally calculated by observing either: • The variation in the signal emitted by the obstacle, with the signal generally increasing as the target approaches (example in infrared bands), • the variation in the apparent size of the obstacle between images, for example in number of pixels, which increases when the target gets closer.

[0010] Knowing the time before collision, it is possible to deduce the radial velocity, but with such precision that it requires increasing the performance in distance to have a good quality of radial velocity.

[0011] The radial velocity error requirement is the most critical because if it is determined via distance estimation, the distance error must then be reduced to levels that may be difficult to achieve for passive EO / IR technologies.

[0012] There is therefore a need to obtain, in an aircraft implementing a passive measurement system, a better estimation of the radial speed of an obstacle than the state of the art. Summary of the invention

[0013] The invention offers a solution to the problems mentioned above, by allowing a better estimation of radial velocity without requiring better quality distance measurements.

[0014] One aspect of the invention thus relates to a method for controlling an aircraft comprising at least one camera and a guidance system configured to indicate a trajectory to the aircraft, the method being implemented by computer and being characterized in that it comprises the following steps: • Acquisition of the distance between the aircraft and an obstacle on the aircraft's trajectory and the time before collision of the aircraft with the obstacle from images of the obstacle obtained by the camera, • Estimation of at least one radial velocity of the obstacle using an extended Kalman filter taking as input said distance and said time before collision of the aircraft with the obstacle, • Modification of the aircraft's trajectory by the guidance system, based on the estimated radial speed of the obstacle and the distance between the aircraft and the obstacle, in order to avoid a collision between the aircraft and the obstacle.

[0015] The present invention directly uses the time to collision of the aircraft with the obstacle, as well as the distance between the obstacle and the aircraft, to obtain, via an extended Kalman filter (abbreviated KEF, or "EKF"), a more accurate radial velocity. This method reduces the distance constraint otherwise required if the radial velocity were obtained by another means, as in the prior art. Furthermore, the invention makes it possible to use an optronic system, making it more suitable for performing a "detect and avoid" function by obtaining a radial velocity with good performance, that is, an estimated radial velocity closer to the actual radial velocity of the obstacle than in the prior art.

[0016] In addition to the characteristics just mentioned in the preceding paragraph, the method according to one aspect of the invention may have one or more characteristics Additional risks from among the following, considered individually or according to all technically possible combinations: • The extended Kalman filter includes a measurement vector and a state vector, the measurement vector including the distance between the aircraft and the obstacle and the inverse of the time before collision of the aircraft with the obstacle, the state vector including the distance between the aircraft and the obstacle and the radial velocity of the obstacle relative to the aircraft. • The distance between the aircraft and the obstacle is obtained by a machine learning process based on image analysis, the images having been obtained by a camera on board the aircraft. • The time before collision of the aircraft with the obstacle is obtained by a machine learning process based on an analysis of images, the images having been obtained by a camera on board the aircraft. • the radial velocity is obtained by using a plurality of extended Kalman filters, each extended Kalman filter of the plurality of extended Kalman filters modeling one kinematic regime of the obstacle among a plurality of kinematic regimes of the obstacle, the kinematic regime modeled by each extended Kalman filter of the plurality of extended Kalman filters being different from the kinematic regime modeled by the other extended Kalman filters of the plurality of extended Kalman filters. • each extended Kalman filter of the plurality of extended Kalman filters includes a state vector representing the kinematic regime modeled by the extended Kalman filter weighted by a probability of realization of the kinematic regime, the radial velocity being contained in a state vector being the barycenter of the state vectors of the extended Kalman filters of the plurality of extended Kalman filters.

[0017] Another aspect of the invention relates to an aircraft configured to implement the aircraft control method according to the invention, the aircraft comprising at least one camera and a guidance system configured to indicate a trajectory to the aircraft.

[0018] Another aspect of the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, lead the latter to implement the method of controlling an aircraft according to the invention.

[0019] Another aspect of the invention relates to a computer-readable recording medium comprising instructions which, when executed by a computer, lead the computer to implement the method of controlling an aircraft according to the invention.

[0020] The invention and its various applications will be better understood upon reading the description that follows and examination of the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES

[0021] The figures are presented for illustrative purposes only and are in no way limiting of the invention. • Figure 1 shows a schematic representation of a system comprising an obstacle and an aircraft configured to estimate radial velocity according to the invention, • Figure [Fig. 2] shows a schematic representation of a multi-model extended Kalman filter of one embodiment of the invention, • Figure 3 shows a schematic representation of a control process of an aircraft according to the invention, • Figures 4A to 4C show graphical representations of results for an extended Kalman filter modeling a non-maneuvering kinematic regime of the obstacle O with only the distance D included in the measurement vector of the extended Kalman filter, • Figures 5A to 5C show graphical representations of results for an extended Kalman filter modeling a non-maneuvering kinematic regime of the obstacle O with the distance D and the time to collision TAC included in the measurement vector of the extended Kalman filter, • Figures 6A to 6D show graphical representations of results for a multi-model extended Kalman filter with the distance D and the time to collision TAC included in the measurement vector of the extended Kalman filter. DETAILED DESCRIPTION

[0022] Unless otherwise specified, the same element appearing on different figures has a unique reference.

[0023] Figure 1 shows a schematic representation of a system comprising an obstacle and an aircraft configured to implement the method according to the invention. The obstacle O shown in Figure 1 follows a trajectory T0. The aircraft A shown in Figure 1 follows a trajectory Ta. Preferably, the trajectory Ta is determined by a guidance system onboard the aircraft A; that is, the guidance system determines a trajectory based on configuration information and / or a trajectory determination method, for example, based on a starting point, an arrival point, and parameters. Such parameters may be tasks to be performed, stages, objectives, an average speed between departure and arrival, or a trajectory duration. The trajectory Ta of the aircraft A may include periods of inactivity.

[0024] An aircraft is a flying transport device capable of moving through the air.The term "transport" refers to a device capable of carrying at least one human being or at least one object, such as a camera, a package, a processor, or any other object. Aircraft A may be an unmanned aircraft, such as a drone, including a medium-sized drone, or a piloted aircraft, such as, but not limited to, a glider, a motorized ultralight glider (also known as an "ULM"), a helicopter, or a light aircraft, including a single-engine or twin-engine aircraft. Aircraft A includes a processor, such as one in an onboard computer, configured to estimate the radial velocity Vr of obstacle O, which will be described later, and / or a method for controlling aircraft A based on the estimated radial velocity Vr of obstacle O, which will also be described later.The method according to the invention is implemented by the processor by executing instructions stored in memory, either remote or contained within aircraft A. The execution of instructions by the processor leads the processor to implement the method of controlling aircraft A according to the invention.

[0025] An obstacle O of aircraft A is an object located or intended to be located on the trajectory Ta of aircraft A, that is to say, the trajectory To of the obstacle O intersects the trajectory Ta of the aircraft at a collision point C at some time t. When aircraft A is not moving, that is to say, when it is stationary, an obstacle O is an object whose trajectory To passes through aircraft A. An object that never intersects aircraft A may be considered an obstacle O by aircraft A if there is a risk that the object will collide with aircraft A. Aircraft A may be configured to consider that every intruder, that is to say, every object entering its field of vision, presents a risk of colliding with aircraft A and is therefore an obstacle O.

[0026] The invention aims to estimate the radial velocity Vr of the obstacle O relative to the aircraft A. The estimation is performed by computer, preferably a computer mounted on the aircraft A. Thus, the invention relates to a computer-implemented method. With the distance D and the radial velocity Vr, it is possible to avoid a collision between the aircraft A and the obstacle O by modifying the trajectory Ta of the aircraft A using the guidance system.

[0027] The radial velocity Vr is the velocity of the obstacle O projected onto an observation axis of the aircraft A, for example, onto axis 1 shown in dashed lines in [Fig. 1]. The aircraft A includes at least one camera, which therefore has the function of acquiring images. The camera is configured to acquire a plurality of images of the obstacle O. The camera preferably allows the acquisition of images at a plurality of different wavelengths, for example, in the visible and / or infrared range, preferably in the near-infrared and / or far-infrared, in order to allow the radial velocity Vr to be estimated according to the invention regardless of the visibility conditions, and to allow nighttime detection of obstacle O.

[0028] The distance D is the distance between aircraft A and obstacle O. The distance D can be obtained, as described in the prior art, via a machine learning process implemented by aircraft A analyzing the images obtained by the camera. The machine learning process may, for example, implement a neural network. The distance D is then provided by the machine learning process at several times, whether regular or not.

[0029] The time to collision (TTC) is the time before collision between aircraft A and obstacle O, each following their respective trajectories Ta and To. The TAC can be obtained by image analysis, for example by the variation in apparent size in pixels of obstacle O between images of a sequence of images of obstacle O obtained by the camera.

[0030] To estimate the radial velocity Vr, the invention proposes the use of an extended Kalman filter, taking as input the distance D between aircraft A and obstacle O and the time before collision of aircraft A with obstacle O. The extended Kalman filter makes it possible to filter the distance and the radial velocity Vr, that is to say, the estimated distance De and the estimated radial velocity Vr are smoothed and less noisy than in the prior art. Thus, the distance D at the input of the extended Kalman filter can be imprecise while obtaining an estimated radial velocity Vr with good performance, that is to say, more precise than in the prior art. The extended Kalman filter makes it possible to differentiate from the filtering space Y_[D]. “[Vr. Since the time to collision can be obtained as follows: TAC = -31 manage a measurement space Z = There is a non-linearity between the measurement space and the filtering space. The use of a An extended Kalman filter allows us to handle this non-linearity.

[0031] The extended Kalman filter includes a measure vector in the measure space and a state vector in the filtering space. Preferably, the measure vector includes the distance D between aircraft A and obstacle O and the inverse of Time before collision (TAC) of aircraft A with obstacle O. The inverse of the time before TAC collision avoids filter convergence problems when the obstacle O has a radial velocity Vr of zero. An obstacle O can have a radial velocity Vr of zero. for example when the obstacle O is stationary, for example a hot air balloon. The The state vector Y_D includes the distance D between aircraft A and obstacle O and the X "VrJ radial velocity Vr of obstacle O relative to aircraft A.

[0032] A Kalman filter operates in two phases: a prediction phase and a

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[0046] estimation phase. The prediction phase uses the state vector at a previous state, for example at time k-1, and seeks to predict the value of the state vector. to a current state, for example at current time k. The estimation phase uses the measurement vector z observed at current time k to correct the predicted state xk at current time k in order to obtain a more accurate prediction. The operation of the extended Kalman filter according to the invention will now be described. Prediction phase: The prediction phase begins with the calculation of the state vector transitioning from time k to time k+1, for example with an image obtained by the camera at time k and a second image obtained by the camera at time k+1, the state vector is then calculated as follows: Xk+Vk = F*Xk / k With 1 dT] for constant velocity movement of obstacle O, with 0 1 J dT is the time difference between two instants (for example, at 10Hz, dT = 0.1 seconds). With X^ = [ D 1 the estimated state vector at time k and Xk+i / k = [ D 1 : vector LVrJ LVrJ predicted state at time k+1 knowing the estimated state at current time k. The predicted covariance matrix corresponds to this predicted state vector Xk+l / k: with the covariance matrix estimated in [D 1, Pk+i / k the covariance matrix 1 VrJ predicted in [ D 1, F1 the transpose of F, Q the modeling matrix of the ci-LVrj regime Nematic of obstacle O. For example, Q can be expressed as follows: dT* 4 dF dT' dT2 with o the increment in acceleration expressed in m / s² of the kinematic regime of obstacle O. Estimate In the estimation phase, because the measurement vector is D 1, the Jacobian H L TAC must be calculated as follows: r rz 1 F 1 0 1 TJ .. Cl A _ H ~\dx\~ ........1........1 L ■ TAC*D ~D. With the distance D being zero in case of collision between aircraft A and obstacle O.

[0047] Next, the state vector at time k+1 following the current time k, comprising the value of radial velocity Vr and estimated distance De, is calculated as follows:

[0048] Xfc+1 / k+i = + Kk+l* (Zk+lh (Xk+yk))

[0049] And the covariance matrix associated with the state vector at time k+1:

[0050] Pi+W+1 = (I - Æ'ul*H)*Pk+1 / k

[0051] With. / x —[X flV ( : \1t and Xk+i / Je vector D 1. Vx^-L k+i / k\ LVr]

[0052] With Kk+[ the Kalman gain, calculated taking into account the Jacobian H and the measurement noise ZX <Tn) et। \ D / TAC^tâc ~ TAC2)

[0053] The Kalman gain Kk+i is obtained by first calculating the matrix S, as follows s=h*pM!cht+m

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[0067] with M the noise matrix measured in 0 0 rf / TAC. where is the noise covariance in distance D, and <jj)\iTAC est la covariance de bruit in —L_ and (Jutât = 17 tac TAC <ITAC — The Kalman payoff is then calculated according to: km = Pw*ht*s-1 The extended Kalman filter operates with an initial state vector X0 / 0, that is -¾ TACtt. at an initial instant 0. According to the invention, the initial state vector X0 / 0 is initialized as follows: Xq / 0- With Do an initial distance corresponding to the first distance obtained by image analysis, and TAC0 an initial time before collision corresponding to the first time before collision obtained by image analysis. The covariance matrix associated with the initial state vector is: with : AJ And TAC0 7 TAC4

[0068] The time before collision TAC and the distance D being considered as two uncorrelated measurements obtained at the same instant.

[0069] Taking into account the time before collision TAC from the initialization of the filter leads to a better convergence of the Kalman filter.

[0070] The invention also relates to an extended Kalman filter for handling cases in which only a time to collision (TTC) measurement has been performed for the current time k. In such a case, the extended Kalman filter uses the measurement of the most recent distance D to which it has access, for example because the distance D measurements are stored in a memory, for example the distance D measurement made at the previous instant k-1, stored in the state vector.

[0071] In one embodiment, the radial velocity estimation Vr of the obstacle O relative to the aircraft A is performed using several extended Kalman filters, which can also be considered as a multi-model extended Kalman filter. In this embodiment, a plurality of extended Kalman filters each model a different kinematic regime of the obstacle O. By "an extended Kalman filter models a kinematic regime" we mean that the extended Kalman filter takes into account, in the kinematic regime modeling matrix Q, a particular kinematic regime of the obstacle O. For example, as shown in [Fig.[2] if the plurality of extended Kalman filters includes three extended Kalman filters, a first extended Kalman filter Fl can model via the matrix Q1 a kinematic regime corresponding to an obstacle O not maneuvering relative to the aircraft A, i.e. an obstacle O stationary relative to the aircraft A, a second extended Kalman filter F2 can model via the matrix Q2 a kinematic regime corresponding to an obstacle O maneuvering in distance relative to the aircraft A, and a third extended Kalman filter F3 can model via the matrix Q3 a kinematic regime corresponding to an obstacle O highly maneuvering in distance relative to the aircraft A, i.e. an obstacle O having many movements.

[0072] In such a case, that is to say in the multi-model embodiment, each filter The extended Kalman curve takes the same measurement vector as input. That's the case. of the three Fl to F3 filters of [Fig. 2]. Each Fl to F3 filter comprises a Q1 to Q3 kinematic regime modeling matrix, each matrix Q1 to Q3 modeling a kinematic regime of obstacle O different from the other kinematic regimes. Each Fl to F3 filter, at each instant, outputs a state vector XI to X3 for the current instant. Each state vector XI to X3 includes an estimate of the radial velocity Vrl to Vr3 of obstacle O relative to aircraft A, modeled according to the corresponding kinematic regime in matrices Q1 to Q3. The resulting state vector X of the multi-model filter is then the centroid of the vectors The states XI to X3 are weighted by a probability of obstacle O achieving the kinematic regime. The probability associated with each vector XI to X3 is denoted a, b, and c, respectively. The probabilities a to c are the probabilities that obstacle O performs maneuvers within the kinematic regime represented by the matrix Q1 to Q3, respectively.

[0073] The estimated state vector Xk / k is then the barycenter of the state vectors Xik / k of the Fl to F3 filters weighted by their probability of occurrence Pri:

[0074] Xk / k = £3 . £— j- nf K. I

[0075] The associated covariance matrix is:

[0076] Pk / k = ^3 ■ ^3: \ >? Pr i+ Xx J * Pr, + X Q^- Pr '

[0077] Pri at time k+1 following the current time k is updated at each measurement as follows, using the probability of realization of the same kinematic regime at the current time k:

[0078] Pri(k+1) “ Pri(k) * with d2 the normalized innovation of the filter corresponding to the kinematic regime where d2 = [(Zk+1 - h(Xk+l / k))T*Sl*((Zk+l - h(Xk+l / k)).

[0079] In the multi-model embodiment, the invention allows for consideration of the different displacement capacities of obstacle O. The various kinematic regimes can be predicted by configuration, for example, stored in a file. The probabilities of realization of the kinematic regimes can also be stored in a file. Finally, the kinematic regimes can be linked to a type of obstacle O. The type of obstacle O can be detected by image analysis, and the various kinematic regimes associated with this type of obstacle O can then be retrieved and used in a multi-model Kalman filter, allowing for improved accuracy of the radial velocity Vr.

[0080] Another aspect of the invention relates to a method 1 for controlling aircraft A shown in [Fig. 3]. Method 1 comprises three steps: • A first step 11 involves acquiring a distance D between aircraft A and obstacle O and a time to collision (TAC) of aircraft A with obstacle O from images of obstacle O obtained by the camera. This step 11 is performed by the camera of aircraft A. • A second step 12 of estimating the radial velocity Vr of obstacle O using an extended Kalman filter taking as input the distance D between aircraft A and obstacle O and the time before collision of aircraft A with obstacle O, as presented previously. This estimation is, for example implemented by a processor included in aircraft A, from the distance D and the time before collision TAC acquired in step 11, provided as input to the extended Kalman filter, single model or multi-model. • A third step 13 of modification of the trajectory Ta of aircraft A by the guidance system of aircraft A, as a function of the value of radial speed Vr estimated of obstacle O and the estimated distance De between aircraft A and obstacle O in order to avoid a collision of aircraft A with obstacle O. The radial speed Vr and the estimated distance De are the information contained in the state vector at the output of the extended Kalman filter at each instant.

[0081] To illustrate the gains obtained by the invention in terms of performance over distance D and radial velocity Vr, the following table presents a comparison of the invention with a Kalman filter taking only distance D as input. Cases 1 and 2 show the results obtained without bias in distance D with a distance error of Cases 3 and 4 show the results obtained by the invention with a 10% bias in distance D and a 20% error in distance D (+ / - 10% compared to the true distance), respectively, by the invention and by a Kalman filter with distance D as the sole input.

[0082] [Tables 1]

[0083] Table 1 shows radial velocity errors Vr at the outlet of the Kalman filter extended much lower for cases 1 and 3 compared to cases 2 and 4 respectively.

[0084] Figures 4A, 4B and 4C, 5A, 5B and 5C and 6A, 6B, 6C and 6D show graphs representing metrics relating to the performance of the invention at time. Each metric is graphically represented by instant, i.e., for example, by image, with instants 0 to 200 corresponding to images 0 to 200 captured by the camera. Alternatively, an instant can be an instant at which a distance is obtained at the output of the extended Kalman filter.

[0085] Figures [Fig.4A], [Fig.4B] and [Fig.4C] show respectively the estimated distance De in meters, the errors in estimated distance De in meters and the errors in radial velocity Vr in meters per second per instant for an extended Kalman filter modeling a non-maneuvering kinematic regime of the obstacle O with the distance D alone included in the measurement vector of the extended Kalman filter.

[0086] Figures [Fig.5A], [Fig.5B] and [Fig.5C] show respectively the estimated distance De in meters, the errors in estimated distance De in meters and the errors in radial velocity Vr in meters per second per instant for an extended Kalman filter modeling a non-maneuvering kinematic regime of the obstacle O with the distance D and the time before collision TAC included in the measurement vector of the extended Kalman filter.

[0087] Figs. 6A, 6B, 6C and 6D respectively show the estimated distance De in meters, the probability of realization of each model, the errors in estimated distance De in meters and the errors in radial velocity Vr in meters per second per instant for a multi-model extended Kalman filter with the distance D and the time before collision TAC included in the measurement vector of the extended Kalman filter.

[0088] The estimated distance De in figures 4A, 5A and 6A is represented by a continuous line and the true distance is represented by star-shaped points.

[0089] The probability of realization Pr represented in [Fig.6B] is represented by points in the form of a pentagon for a modeling of the highly maneuverable kinematic regime TM, by points in the form of a circle for a modeling of the maneuverable kinematic regime M and by points in the form of a star for a modeling of the non-maneuverable kinematic regime NM.

Claims

Claims

1. Method (1) for controlling an aircraft (A) comprising at least one camera and a guidance system configured to indicate a trajectory (Ta) to the aircraft (A), the method (1) being implemented by computer and being characterized in that it comprises the following steps: - Acquisition (11) of a distance (D) between the aircraft (A) and an obstacle (0) on the trajectory (Ta) of the aircraft (A) and of a time before collision of the aircraft (A) with the obstacle (0) from images of the obstacle (0) obtained by the camera, - Estimation (12) of at least one radial velocity (Vr) of the obstacle (0) using an extended Kalman filter taking as input said distance (D) and said time before collision of the aircraft (A) with the obstacle (0), - Modification (13) of the trajectory (Ta) of the aircraft (A) by the guidance system,as a function of the estimated radial velocity value (Vr) of the obstacle (0) and the distance (D) between the aircraft (A) and the obstacle (0) so as to avoid a collision of the aircraft (A) with the obstacle (0).,

2. Method (1) according to the preceding claim according to which the extended Kalman filter comprises a measurement vector and a state vector, the measurement vector comprising the distance (D) between the aircraft (A) and the obstacle (0) and the inverse of the time before collision of the aircraft (A) with the obstacle (0) and the state vector comprising the distance (D) between the aircraft (A) and the obstacle (0) and the radial velocity (Vr) of the obstacle (0) relative to the aircraft (A).

3. Method (1) according to any one of the preceding claims, wherein the distance (D) of the aircraft (A) with the obstacle (0) is obtained by a machine learning method based on image analysis, the images having been obtained by a camera on board the aircraft (A).

4. Method (1) according to any one of the preceding claims, wherein the time before collision of the aircraft (A) with the obstacle (0) is obtained by a machine learning method based on image analysis, the images having been obtained by a camera on board the aircraft (A).

5. Method (1) according to any one of the preceding claims according to which the radial velocity (Vr) is obtained by using a plurality of extended Kalman filters, each extended Kalman filter of the plurality of extended Kalman filters modeling a kinematic regime of the obstacle (0) relative to the aircraft (A) among a plurality of kinematic regimes of the obstacle (0) relative to the aircraft (A), the kinematic regime modeled by each extended Kalman filter of the plurality of extended Kalman filters being different from the kinematic regime modeled by the other extended Kalman filters of the plurality of extended Kalman filters.

6. Method (1) according to claims 2 and 5 according to which each extended Kalman filter of the plurality of extended Kalman filters comprises a state vector representative of the kinematic regime modeled by the extended Kalman filter weighted by a probability of realization of the kinematic regime, the radial velocity (Vr) being included in a state vector being the barycenter of the state vectors of the extended Kalman filters of the plurality of extended Kalman filters.

7. Aircraft (A) configured to implement the method according to any one of the preceding claims, the aircraft (A) comprising at least one camera and a guidance system configured to indicate a trajectory to the aircraft (A).

8. Computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the control method (1) according to any one of claims 1 to 6.

9. A computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to implement the control method (1) according to any one of claims 1 to 6.

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