Swarm of drones with collaborative positioning via ultra-wideband communication and associated positioning method

Abstract

A COLLABORATIVELY POSITIONED ULTRA-WIDEBAND COMMUNICATION DRONE SWARM AND ASSOCIATED POSITIONING METHOD. A drone swarm (100) comprising a plurality of drones (10), including aerial drones, capable of coordinated flight, in which each drone (10) includes an inertial measurement unit (IMU) (31) for estimating the drone's attitude, an ultra-wideband communication module (32) (UWB) configured to measure a relative position when communicating with another UWB, and a central computer (33) configured to perform fusion of the data from the IMU and the UWB. No drone (10) uses a satellite geolocation module, such as a GPS receiver; and each drone (10) is configured to communicate, via its UWB, with one or more neighboring drones (10) to ensure its relative positioning within the swarm. Figure for the summary: Figure 2

Description

Title of the invention: Test of drones with collaborative positioning by ultra-wideband communication and associated positioning method technical field

[0001] The present invention belongs to the field of swarm robotics, in particular drone swarms. It relates more particularly to a swarm of drones whose drones position themselves collaboratively by ultra-wideband communication, better known by its English acronym UWB (Ultra Wide Band), as well as a positioning method implementing such a swarm of drones.

[0002] The present invention finds a direct, but not exclusive, application in drone air shows and in coordinated drone flights for technical inspection or defense missions. State of the art

[0003] Swarm drone flying is a rapidly growing discipline which has opened up many technical challenges, particularly with regard to the precise positioning of drones in the swarm.

[0004] The precise positioning of drones in a swarm proves particularly crucial in the field of drone shows due to the high requirements of precision and synchronization, necessary for a good execution of aerial figures.

[0005] Indeed, during a drone show in the form of a sequence of artistic figures, each drone in the swarm must follow, with great precision, a predefined trajectory from the beginning to the end of the show, so that the overall result corresponds as closely as possible to the sequences and transition movements previously simulated and validated.

[0006] Following a given trajectory requires that the drone be able, in real time, to locate itself and position itself correctly accordingly.

[0007] Technically, the positioning of the drones in a swarm is achieved by estimating the state of each drone. This estimation can be performed individually in each drone using its onboard computer.

[0008] Estimating the state of a drone can be efficiently achieved by a conventional fusion of data from different sensors of the drone, in particular an inertial measurement unit (IMU) and an absolute position sensor, such as a GPS sensor, and / or a relative position (distance) sensor, such as a UWB sensor.

[0009] French patent FR3100895, in the name of the applicant company, relates to a method for the automatic positioning of a plurality of drones operating in a swarm, each drone comprising an IMU, a GPS module, a UWB module and a central computer implementing a Kalman filter, said method comprising for each drone: a step of measuring the position and attitude of the drone by the IMU and by the GPS module; a step of measuring distances by means of the UWB module and fixed UWB antennas (anchors), allowing a determination of the position of the drone; a step of fusing the data from all or part of the sensors of the drone with the distance and position measurements obtained in the previous step, in the central computer by means of the Kalman filter; and a step of estimating the state of said drone.

[0010] More particularly, in this solution, positioning by UWB technology is based on localizing the drones of the swarm by a distance measurement (ranging), using localization algorithms based on the TWR (Two Way Ranging) technique, for example, or preferably on its improved version SDS-TWR (Sym-metrical Double Sided Two Way Ranging).

[0011] Localization can also consist of a direct calculation of the propagation time of the UWB signal between the target and the anchors without synchronization of their antennas, this time being known as the Time of Arrivai (ToA).

[0012] This latter method makes it possible to reduce pulse traffic and therefore to increase the number of targets that can be located simultaneously as well as the localization frequency, and thus presents an effective solution for the localization of drones in a swarm.

[0013] In the same vein, document KR102459019 describes a UWB-based positioning method for a drone swarm. More specifically, appropriate positioning for the swarm's flight is ensured in real time by calculating relative positions using the TWR technique according to a master-slave model.

[0014] However, the use of GPS and / or anchors remains necessary in these solutions which apply to outdoor and / or indoor flights.

[0015] Such a swarm cannot therefore move accurately in an environment without anchors and without access to GPS. Such an environment could, for example, be the interior of an abandoned building that requires inspection or exploration.

[0016] It is known that UWB technology makes it possible to measure a relative position with respect to another object: a mobile drone or a fixed anchor. It thus allows the positioning of drones during indoor flight, unlike GPS, which only works properly outdoors due to the nature of its signal.

[0017] Furthermore, the use of fixed anchors, or ground stations, can induce a lack of robustness due to the quality of the link between the drones and the ground stations.

[0018] There are many methods for estimating the states of a swarm of independent robots more generally, in which Kalman filters are mainly used, as mentioned in the solution described above, and in particular the filter of extended Kalman (EKF).

[0019] Data fusion methods for estimating the state of a drone in a swarm, although varied, systematically include GPS measurements in the fused data in order to ensure a certain accuracy in the estimated positions.

[0020] For example, in "RUBIN ZOU. Cooperative positioning of U AV swarms by fusing IMU / UWB / GPS with Federal Kalman Filter. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY, 10, 09 2019", a fusion method, called the Federal Kalman Filter (FKF), is described. This method uses a double Kalman filter to fuse the data from the three measurements: IMU, UWB, and GPS, in order to estimate the state of each drone in the swarm. Each drone carries its own FKF. A first Kalman filter is used to fuse the IMU and GPS measurements, and then a second Kalman filter is used to fuse the output of the first filter with the UWB measurements.

[0021] Finally, it should be noted that GPS operation is significantly less reliable than UWB, that using IMUs alone leads to a significant divergence in covariance, which is completely incompatible with swarm flight, and that adding GPS to the IMU significantly improves drone navigation by eliminating covariance divergence, but GPS measurements are not always available (jamming, multipath, indoor). Summary of the invention

[0022] The present invention aims to overcome all or part of the disadvantages of the prior art described above by proposing a positioning solution for a swarm of drones, making it possible to do without GPS, or similar, and anchors, and therefore to adapt without technical constraints to any type of environment.

[0023] More fundamentally, by exploiting the fact that a swarm of drones offers advantages over individual drones, such as cooperation, redundancy and the ability to perform complex tasks in a distributed and collaborative manner, the present invention proposes a collaborative positioning solution through a distributed UWB system.

[0024] One advantage of the invention is therefore to eliminate the need for GPS or to significantly limit its use (for example, a single GPS in a drone of the swarm). This latter case remains possible, but is not of great interest within the scope of the invention.

[0025] Another advantage of the invention is to limit the drift of the swarm and to ensure a good compromise between drift and geometric distortion of aerial figures.

[0026] To this end, the present invention relates to a swarm of drones comprising a plurality of drones, particularly aerial drones, capable of performing coordinated flight, in which each drone includes an inertial measurement unit, called an IMU, allowing estimation The drone's attitude, an ultra-wideband communication module (UWB) configured to measure relative positioning when communicating with another UWB, and an onboard central computer configured to perform data fusion between the IMU and the UWB. This drone swarm is remarkable in that no drone uses a satellite geolocation module, such as a GPS receiver, and in that each drone is configured to communicate, via its UWB module, with one or more neighboring drones to ensure its relative positioning within the swarm.

[0027] Advantageously, the estimation is decentralized in the drone swarm: each drone in the swarm includes its own Kalman filter.

[0028] UWB makes it possible to reduce geometric distortion through an appropriate adjustment of a self-covariance parameter of the UWB module.

[0029] According to one embodiment, each measurement made by the UWB module of a drone in the swarm has a standard deviation determined based on covariance information from a neighboring drone used to establish said measurement, so as to disseminate the information throughout the swarm.

[0030] Advantageously, the covariance information is transmitted from drone to drone by UWB communication using UWB modules.

[0031] According to one aspect of the invention, each drone includes a Kalman filter implemented in its central computer.

[0032] According to one embodiment, the drone swarm further comprises at least one stationary drone serving as an anchor for the other drones.

[0033] The present invention also relates to a method for the collaborative positioning of drones in a swarm, implementing a drone swarm as described, comprising: • a preliminary numerical simulation step to select a covariance for tuning a drone positioning model; and • a step to execute the positioning model in each drone.

[0034] More particularly, the simulation step makes it possible to estimate an aerial figure of the drones as a function of a setpoint aerial figure and to choose the covariance as a function of an estimation accuracy and performance indicators among a distortion, a translational drift and a rotational drift of the figure estimated with respect to the setpoint figure.

[0035] According to one embodiment, the drone swarm uses at least one anchor equipped with a UWB transmitter.

[0036] The present invention also relates to: a computer program product downloadable from a communication network and / or stored on a microprocessor-readable medium and / or executable by a microprocessor, characterized in that it includes program code instructions for the execution of such a collaborative positioning process, and a terminal-readable, non-transient storage medium storing a computer program comprising a set of instructions executable by a computer or processor to implement said process.

[0037] The fundamental concepts of the invention having been set out above in their most elementary form, other details and features will become clearer from reading the following description and with regard to the attached drawings, giving by way of non-limiting example an embodiment of a swarm of drones with collaborative positioning and its associated positioning method, in accordance with the principles of the invention. Presentation of the drawings

[0038] The figures are given for illustrative purposes only to facilitate a better understanding of the invention without limiting its scope. The various elements may be represented schematically and are not necessarily to scale. Throughout the figures, identical or equivalent elements are identified by the same numerical reference.

[0039] It is thus illustrated in: [Fig.1]: a schematic view of a swarm of drones according to the invention; [Fig.2]: a diagram of two drones from the swarm exchanging UWB signals, showing the main onboard systems of a drone; [Fig.3]: an aerial figure of the swarm called a "square" on a 2D plane; [Fig.4]: another aerial figure of the swarm called "ellipses"; [Fig.5]: another aerial figure of the swarm called a "flower"; [Fig.6]: a first result of a simulation of the "square" figure, showing a significant drift by rotation of the trajectories; [Fig.7]: a graph of the position error along the x and y axes, corresponding to the simulation of [Fig.6]; [Fig.8]: the evolution of performance indicators as a function of UWB covariance; [Fig.9]: a second result of a simulation of the "square" figure with a set UWB covariance, showing a reduction in drift compared to the first result of [Fig.6]; [Fig. 10]: a graph of the position error along the x and y axes, corresponding to the simulation of [Fig. 9]; [Fig. 11]: the evolution of performance indicators as a function of the number of anchors (a) and as a function of the number of fixed drones (b); [Fig. 12]: The evolution of performance indicators as a function of covariance UWB in the case of the "ellipse" (a) and "flower" (b) figures. Detailed description of implementation methods

[0040] It should be noted that certain technical elements well known to those skilled in the art are recalled here to avoid any insufficiency or ambiguity in the understanding of the present invention.

[0041] In the embodiment described below, reference is made to a swarm of drones, intended primarily for performing aerial drone shows and conducting coordinated flights as part of military missions. This non-limiting example is given for a better understanding of the invention and does not preclude the use of the principles of the invention in other coordinated groups of robots (land-based or marine) for carrying out other types of missions.

[0042] In this description, the terms "drone" and "swarm" refer, by extension, respectively, to an aerial drone, or unmanned aerial vehicle (UAV), and a group of independent drones capable of flying simultaneously and in a coordinated manner.

[0043] Fig. 1 schematically represents a swarm of drones 100 comprising n independent drones 10 (n being greater than 2 and typically on the order of several hundred or a few thousand), each evolving along a well-defined trajectory, so that the swarm 100 performs an aerial figure as part of a drone show or any coordinated flight (technical inspection, maintenance, surveillance, combat, etc.).

[0044] In the swarm 100, each drone 10 is capable of communicating wirelessly, via UWB, with the other drones in the swarm to determine its relative position. Indeed, each drone 10 in the swarm 100 is configured to receive the positions of the other drones and send its own position to the other drones via a UWB module comprising a UWB signal transmitter-receiver.

[0045] More specifically, each drone 10 is capable of communicating by UWB at least with one or more drones, called neighbors, located in its neighborhood delimited by the range of the UWB signals exchanged.

[0046] Thus, a UWB hop-by-hop communication takes place even if the swarm 100 has an extent (largest dimension) greater than the range of the UWB signal used.

[0047] For the purpose of estimating its state and positioning in the swarm 100, each drone 10 includes a UWB module, an inertial measurement unit, called an IMU, and an on-board central computer, together enabling the measurement of the drone's movement and obtaining relative position information from neighboring drones.

[0048] Optionally, at least one drone 10 of the swarm 100 may include a GPS-type geolocation module, the use of which in the context of the invention is not necessary, but remains compatible if needed.

[0049] Fig. 2 schematically represents two drones 10 of the swarm 100, communicating via UWB.

[0050] With reference to this figure, each drone 10 comprises a main body 20 in which are embedded the IMU 31, the UWB module 32 and the central computer 33.

[0051] The drones 10 naturally include motors 40, in particular propeller motors in quadrotor configuration, for their propulsion as well as all the necessary utilities for their autonomous flight.

[0052] Thus constituted, the drones 10 of the swarm 100 are capable of achieving precise collaborative positioning thanks to hop-by-hop UWB communication and a state estimation model, which will be presented below. Simulation results validating the operation of the invention will be presented and discussed with reference to simple 2D aerial figures to avoid making the description too cumbersome. The validated estimation model is, of course, applicable to any 3D aerial figure.

[0053] Figures 3 and 4 show examples of simple aerial figures (a square and concentric ellipses) on a gridded simulation plane. The small crosses represent the drones on their trajectories (here, ten drones); and the thick dots at the corners of each graph represent anchors equipped with UWB transmitters, which can be used within the scope of the invention as explained later.

[0054] Fig. 5 represents a more complex aerial figure, called a "flower", which is representative of the different movements made by drones regardless of their trajectory (straight and curvilinear translations, and rotations), and which therefore allows the estimation model to be tested on several aspects of performance.

[0055] An initial estimation model is described below.

[0056] Each drone in the swarm is located by its x and y coordinates in the plane. The state vector of each drone is thus defined by:

[0057] Discretization according to a time step A? then allows the behavior of the drone to be expressed by the following system of equations: ^ = / (¾¾) with Z S = ft (HAS M 7.) R)

[0058] R depends on the measurements considered, namely IMU measurements and UWB measurements.

[0059] Initially, we consider a "stone" type prediction model, in which the predicted state is equal to the state at the previous instant. This translates to: / (¾^)=^.+^.

[0060] We therefore obtain:

[0061] In each drone, the UWB module, operating as a receiver, gives the distance (relative position) with respect to another UWB module, operating as a transmitter, with a standard deviation avwB.

[0062] For a drone i that has received a relative position measurement with respect to a drone j, we have: x{, = Il Xi-Xi II j + MfwB : ( a R™B ) ; with =0-2

[0063] It should be noted that for a drone j of the swarm, R^WB depends directly on the associated covariance pi which appears as an uncertainty on the position of the drone j which is not fixed, unlike a UWB anchor.

[0064] The observation matrix is ​​expressed as: And: ll^ll2 /

[0065] The prediction and correction equations of the extended Kalman filter (EKF) are used in the following.

[0066] The prediction is performed according to the following two equations: = f ( X h 0) P t ^ = F t P t / t F T k + G k HQ T t

[0067] The correction itself is made based on the available measurements. For example, for two UWB measurements (with the Ji and J2 drones), we have:

[0068] The correction, or update, equations are therefore as follows.

[0069] Innovation, which corresponds to the difference between the measurement and the prediction: v k+i “ z k+l -H k (X k+ ^, 0)

[0070] The covariance of innovation: $k+\ ~ ^k+lPk+^k+l + ^k

[0071] The optimal Kalman gain: &k+\ ~

[0072] State update: ^+11^+1 = + Kk+ïvk+î

[0073] Covariance update: Pk+^k+i = - K k+l H k+l )P k+i ^

[0074] The standard deviation auwB of a UWB measurement is determined based on the covariance information of the neighboring drone used to establish the measurement. The neighboring drone can be an "interlocutor" drone or an anchor. This ensures the dissemination of information, particularly accuracy, throughout the entire swarm. The transmission of covariance information is carried out via UWB, with UWB being used in this case as a means of communication.

[0075] According to an exemplary embodiment, the standard deviation j) for a measurement between two drones i and j is calculated by summing the standard deviation of the UWB sensor auwB with the semi-major axis of the covariance ellipse of the reference drone and the semi-major axis of the covariance ellipse of the drone taking the measurement.

[0076] The semi-major axis is calculated by taking the square root of the maximum eigenvalue of the covariance matrix of the drone under consideration. If the drone is an anchor, this maximum eigenvalue is 0. Otherwise, its expression at time k is:

[0077] Where eig(A) represents the set of eigenvalues ​​of A.

[0078] The taking into account of the drone measurements in the initial model above is described below.

[0079] It should be noted that in the absence of IMU, the drones in the swarm are not able to follow a given trajectory and simply maintain the same relative distances they have with each other.

[0080] The IMU provides two acceleration measurements, along the x and y axes. The measurement vector of the IMU can therefore be denoted:

[0081] Since the IMU provides velocity measurements in addition to the drone's positions (geometric coordinates), the state vector of each drone becomes:

[0082] The drone evolution model with the IMU becomes: with v has.

[0083]

[0084] Furthermore, we have: ZA H Û\ Discretizing the above evolution model yields: Xk+Vx,k-At + ^yk ' 41 / \ Either : A+1-2 k^k xk + axk ' + vxk ■ \ Vyk + ayk ' 41 / + Vyk ■ 41 / / With: HAS / Ft = ^(Xs,0) = 'f = |(XfcÜ) = 0 A / 0 Ar / 0 \And: Ar 0

[0085]

[0086] The model then applies the prediction equations with the new function f and the new matrices F and G. Regarding the correction step, adding the IMU only involves modifying the size of certain matrices. More specifically, the observation matrix corresponding to the UWB measurement becomes: UWBl- & L ' UWB With :

[0087]

[0088]

[0089] \ / ' 2 The matrix remains unchanged. The correction equations are finally applied with the new functions h and the new observation matrices. Thus, a collaborative positioning algorithm based on the obtained model is implemented in the central computer of each drone in the swarm, and allows to execute a positioning process implementing a swarm of drones according to the invention. Results

[0090] The simulations and tests to validate the described positioning model were carried out with ten drones, on a 2D plane of 20m x 20m and over a period of 20 seconds, for obvious reasons of simplification.

[0091] The variable parameters are the covariance, the number of anchors, the predefined trajectory of the drones (the aerial figure of the swarm).

[0092] The analysis of the results uses three performance indicators, which are geometric and quantifiable, and which allow for the evaluation of the consistency of the estimated aerial pattern of the swarm with respect to its actual pattern. These indicators are: • Distortion D: a value in meters measuring the conformity of the estimated (obtained) pattern with respect to the actual pattern. For example, if the actual pattern is a square of a certain size, an estimate with low distortion will produce an estimated pattern representing a square of the same dimensions. The estimated pattern can nevertheless be shifted or oriented without increasing the distortion. • The translation T: value in meters measuring the difference in distance between the center of gravity of the estimated figure and the center of gravity of the real figure. • Rotation R: value in degrees measuring the rotation of the estimated figure relative to the actual figure.

[0093] The choice of the covariance ^uwb must guarantee a good compromise between the estimation accuracy, which must be high, and these three indicators, which must remain low. This choice can be made manually or automatically during a positioning process adjustment step called "noise tuning".

[0094] The noise tuning step can also take into account the number of anchors and the nature of the trajectories of the drones in the swarm.

[0095] It should be noted that the model uses only UWB modules and IMUs.

[0096] Figure 6 shows the result obtained for the "square" figure. The solid and dashed lines correspond respectively to the exact and estimated trajectories of the drones. The ellipses represent the UWB covariances of the drones, which are all equal according to the illustrated example.

[0097] The "square" figure drifts slowly in translation and rotation. This is explained by the absence of an absolute position sensor (GPS) which would allow drones to correct their absolute position according to the predefined trajectory.

[0098] Fig. 7 represents error graphs on the x and y position of an arbitrarily chosen drone in the swarm.

[0099] The error in the x position remains within the 3cr confidence interval, while the error in the y position tends to diverge and move out of the confidence interval after a certain time (16s). This corresponds to the drift observed in [Fig. 6], which is indeed greater along the y-axis (drift of the trajectories towards the down).

[0100] This first result was obtained with an initial covariance auwB = 1.5.

[0101] This result can be improved by noise tuning, in order to reduce the drift of the aerial figure.

[0102] Figure 8 represents the performance indicators of the "square" figure as a function of aim.

[0103] The distortion increases by oscillating around the value 0.2. Thus, a low auwe value should be chosen to limit the distortion. However, a low auwB value implies a greater rotation of the aerial figure.

[0104] A compromise must therefore be made between these two parameters. Depending on the nature and shape of the aerial pattern, rotation or distortion may prove more or less problematic. For example, for a spherical pattern, rotation will not have an impact on the overall position of the swarm.

[0105] In view of the above analysis, taking °uwb = 2, we obtain the result of [Fig.9] and the error graphs of [Fig.10].

[0106] Figure 9 clearly shows that the drift has been reduced overall, notably due to a decrease in the swarm's rotation. Figure 10 confirms that the error in y is significantly reduced compared to the previous result and remains well within the confidence interval.

[0107] Moreover, [Fig. 10] simply shows a slow translation of the swarm along x, of the same order as that obtained with a GPS (according to tests carried out by the inventors).

[0108] Fig. 11 shows the influence of the number of anchors or stationary drones on performance.

[0109] It turns out that the use of a single anchor significantly reduces translational drift, while the use of two anchors eliminates rotational drift.

[0110] Thus, by combining these results with the previous results, very good results can be obtained by choosing a value of &uwb large enough to limit the rotational drift and an anchor to limit the translational drift.

[0111] In the case of the absence of anchors, the correction provided by a stationary drone is less significant.

[0112] Figure 12 shows that these conclusions also hold true for the other trajectories (ellipses and flower). In particular, it shows that the translational drift remains the same. However, the rotational drift is significantly less for the "ellipses" figure in (a) than for the "flower" figure in (b). Consequently, a lower value of auwB for "ellipses" than for "flower" can be appropriately chosen.

[0113] Thus, eliminating the need for GPS while retaining the IMU and UWB is very advantageous when the swarm interacts with one or more anchors, since translational drift is eliminated with the anchor, and rotational drift is eliminated with a suitable choice of auWB. However, in the absence of anchors (for example, in an unknown environment), translational drift of the swarm is unavoidable. Nevertheless, the distortion and rotation of the swarm can be limited by carefully selecting the value of auWB according to the simulation parameters.

[0114] Finally, the addition of stationary drones can slightly improve distortion and rotational drift, but the effects remain slight.

[0115] In summary, UWB is a technology that uses signals capable of providing precise information on the distance and time of flight of radio waves, which makes it possible to determine the relative position between different drones with high precision.

[0116] In the context of the present invention, UWB technology is used to enable hop-by-hop communication and precise relative positioning between the drones in the swarm. Each drone in the swarm is equipped with a UWB module, generally in the form of an electronic chip or a module integrated into the computer. The same applies to the IMU.

[0117] When the drones are in flight, they communicate with each other using UWB signals. Each drone measures the flight time of the UWB signals from the other drones in the swarm. By knowing the propagation speed of the UWB signals in the environment, each drone can calculate its relative distance to the other drones. By combining this distance information with the IMU measurements, the drones can determine their relative position within the swarm with high accuracy.

[0118] UWB technology also enables real-time updates of the relative positions of drones, facilitating swarm coordination and control. For example, drones can adjust their position and trajectory based on location information provided by UWB technology, thus maintaining accurate formation and avoiding collisions.

[0119] In addition to its application in the field of light drone shows, the present invention offers promising opportunities in other areas such as technical inspection and defense. In the context of light drone shows, the UWB relative positioning system makes it possible to perform breathtaking aerial choreography, where each drone is in perfect harmony with the others, thus creating a striking and immersive visual spectacle. The artistic and creative possibilities are endless, offering spectators a unique and memorable experience.

[0120] With regard to technical inspection activities, the drone swarm can be used to inspect complex infrastructure, such as buildings or industrial installations, by providing a comprehensive and detailed view of the environment, and by quickly identifying areas requiring intervention or repair.

[0121] It is apparent from the present description that certain non-essential elements can be modified, replaced or deleted without departing from the scope of the invention as defined by the following claims.

Claims

Claims

1. Swarm of drones (100) comprising a plurality of drones (10), in particular aerial ones, capable of performing a coordinated flight, in which each drone (10) comprises an inertial measurement unit (31), called IMU, making it possible to estimate the attitude of the drone, an ultra-wideband communication module (32), called UWB module, configured to measure a relative position when it communicates with another UWB module, and a central computer (33) configured to perform a fusion of the data from the IMU and the UWB module, said swarm being characterized in that no drone (10) uses a satellite geolocation module, such as a GPS receiver; and in that each drone (10) is configured to communicate, via its UWB module, with one or more neighboring drones (10), so as to ensure its relative positioning in the swarm.

2. Swarm of drones according to claim 1, in which each measurement carried out by the UWB module of a drone (10) has a standard deviation determined as a function of covariance information from a neighboring drone used to establish said measurement, so as to disseminate the covariance information throughout the swarm.

3. A drone swarm according to claim 2, wherein the covariance information is transmitted from drone to drone by ultra-wideband communication using the UWB modules.

4. A swarm of drones according to any preceding claim, wherein each drone (10) comprises a Kalman filter implemented in its central computer (33).

5. A swarm of drones according to any preceding claim, further comprising at least one stationary drone serving as an anchor for the other drones.

6. Method for collaborative positioning of drones, implementing a swarm of drones (100) according to any one of claims 1 to 5, comprising: • a prior step of digital simulation making it possible to choose a covariance to adjust a positioning model of the drones; and • a step of executing the positioning model in each drone (10).

7. Positioning method according to claim 6, in which the simulation step makes it possible to estimate an aerial figure of the drones as a function of a set aerial figure and to choose the covariance as a function of an estimation precision and performance indicators among a distortion, a translational drift and a rotational drift of the estimated figure relative to the set figure.

8. A positioning method according to claim 6 or 7, wherein the swarm of drones (100) uses at least one anchor provided with an ultra-wideband transmitter.

9. Computer program product downloadable from a communication network and / or stored on a medium readable by a microprocessor and / or executable by a microprocessor, characterized in that it comprises program code instructions for the execution of a collaborative positioning method according to one of claims 6 to R

10. d O. Terminal-readable and non-transitory storage medium storing a computer program comprising a set of instructions executable by a computer or a processor to implement a collaborative positioning method according to one of claims 6 to 8.

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