EP3994678A1

EP3994678A1 - Method for determining the path of an unmanned aerial device and other associated methods

Info

Publication number: EP3994678A1
Application number: EP20747472.7A
Authority: EP
Inventors: Pierre PELE
Original assignee: Uavia
Current assignee: Uavia
Priority date: 2019-07-01
Filing date: 2020-07-01
Publication date: 2022-05-11
Also published as: KR20220027218A; JP2022539232A; CN114207545A; FR3098336A1; WO2021001768A1; US20220270495A1; IL289500A; CA3144577A1; FR3098336B1

Abstract

A modelling method using digital processing of a three-dimensional environment to establish pathways for unmanned aerial devices which are optimised according to different priorities, the method being characterised in that it comprises the following digital processing steps: (a) providing a three-dimensional model of volumes (PEXi) wherein flight is prohibited, (b) subdividing the model into individual elements (PVk), (c) determining a centre (Pk) for each individual element, (d) establishing and memorising a graph, the nodes (Pk, Ik) of which are formed by at least one portion of the centres, and the branches of which are weighted by the distances between the nodes and by at least one weighting associated with a given priority. A method is also proposed for determining, using an unmanned aerial device, a path between two points in a three-dimensional space modelled by such a graph, and steering methods using such a determination.

Description

Title: Method for determining the path of an unmanned aircraft and other associated methods

Field of the invention

The present invention relates generally to unmanned aerial devices or drones, and in particular to the determination of routes for drones in constrained environments.

State of the art

Surveillance drones are more and more widely used for surveillance, in particular the surveillance of structures, sensitive sites, etc.

Solutions are also known in the literature for making drones describe imposed routes.

In the field of manned air flight, a flight plan is a series of waypoints without vertical dimensions (see for example EP1614086A2) chosen in advance by the user while respecting environmental and material constraints.

This document describes techniques for following the theoretical trajectory, taking as input a list of coordinates of waypoints and the data from various sensors (Lidar, Laser, etc.) which are processed to dynamically modify said trajectory.

The current state of the art does not propose a technique allowing the automatic establishment of a flight program under environmental and material constraints on the one hand and under higher level constraints determined by the user.

Thus in the current state of the art, it is the user of a UAV who is responsible for building the list of waypoints allowing the machine to reach the destination. The user must build this path avoiding obstacles, taking into account the uncertainty of the UAV's positioning, checking that the energy required for the UAV to travel the path is available, etc.

Still in the current state of the art, a safety pilot must be present during the execution of the automatic mission of the UAV, so as to be able to take control in the event of a problem. He will then be responsible for making the right decisions regarding the trajectories allowing the UAV to reach the safe zone.

Summary of the invention

The present invention proposes to improve the generation of automatic flight programs by limiting the need for human intervention during the flight, with great flexibility in determining the flight path.

According to a first aspect, a method is proposed for modeling by digital processing of a three-dimensional environment with a view to establishing travel paths for unmanned aerial devices optimized according to different priorities, characterized in that it comprises the steps following digital processing:

(a) provide a three-dimensional model of volumes (PEXi) in which theft is prohibited,

(b) subdivide the model into individual elements (PVk),

(c) determine a center (Pk) for each individual element,

(d) establishing and storing a graph whose nodes (Pk, Ik) are formed by at least part of said centers, and whose branches are weighted by the distances between the nodes and by at least one weighting associated with a given priority.

This method advantageously but optionally comprises the following additional characteristics, taken individually or in any combination that a person skilled in the art will understand as being technically compatible:

^* the priorities include at least two priorities among an absolute distance priority, a travel time priority, an energy consumption priority, a risk priority.

^* at least one of the weightings depends on a constraint affecting all the branches. ^* said constraint comprises a constraint vector affecting all the branches.

^* the stress vector is a wind vector, each branch having a pair of weights associated respectively with the direction of travel and each weight being separately subjected to the wind vector.

^* said weighting is such that different weights are assigned to the same branch according to the direction of travel, so as to generate privileged directions of travel.

^* said weighting is based on a mapping defining different levels of constraints as a function of the location in a flight space.

^* the stress levels are included in a group comprising a maximum authorized speed constraint and a risk constraint.

^* the constraint is able to take a value such that the corresponding zone becomes a no-fly zone.

^* step (a) includes providing a three-dimensional model comprising volumes (PEXi) in which theft is physically impossible, and reprocessing this model with static safety margin data.

^* step (a) includes the subdivision of the three-dimensional model into horizontal slices (Txy), the projection of the volumes on a horizontal plane being the same throughout the thickness of each slice, and the implementation of a subdivision into individual elements in each horizontal plane.

^* the subdivision is carried out by triangulation.

^* the triangulation is a Delaunay triangulation.

^* step (d) comprises the establishment of branches of the graph between nodes located in adjacent horizontal planes by a distance minimization approach.

According to a second aspect, a method is proposed for determining by an unmanned aerial device a path between two points of a three-dimensional space modeled by a graph obtained by the modeling method as defined above, characterized in that it comprises the following steps:

- determination of a priority for the route,

- taking into account or establishing a given graph corresponding to the determined priority, and

- development of the path, on board the device, by calculating the best path in said given graph.

^* The branches of the graph of the weighting step is implemented by receiving at a distance from a starting graph unweighted branches, and weight on board of the apparatus, said legs depending on the priority.

^* the method comprises, during the flight, a step of updating the weights of the branches of at least part of the graph and a step of recalculating the best path in the graph.

^* the update of the weights of the branches of the graph is carried out according to a change of priority.

^* The branches of the weight update of at least a portion of the graph is performed based on the reception weight change data for weighting corresponding to the precedence effect.

^* the step of updating the weights of the branches of the graph comprises the generation of prohibited branches according to a prohibited zone appearing dynamically.

^* the prohibited zone is determined by remote communication of the machine with other equipment whose position determines the prohibited zone.

^* the other equipment is another unmanned aerial device.

^* the forbidden zone is a forbidden altitude stop.

^* the other equipment is associated with a temporary intervention on the site. ^* the calculation of the best path is carried out according to an agility constraint of the device.

According to a third aspect, there is proposed a method of piloting an unmanned aircraft, comprising the following steps:

- determination of a path by the determination method defined above,

- application of at least one trajectory slackening factor,

- determination of an allowable path deviation as a function of the release factor, and

- application of a path correction instruction only when a measured real path deviation exceeds the permissible path deviation.

Advantageously but optionally, the release factor is determined from at least one piece of data representative of one of the following information: current accuracy of a GPS unit on board the device, wind, response of the device to the commands of the device. control, device size, device type.

According to a fourth aspect, there is proposed a method of piloting an unmanned aircraft, comprising the following steps:

- determination of a path by the determination method defined above,

- measurement of a dynamic characteristic of the machine during flight,

- dynamic determination of a new path as a function of the evolution of said dynamic characteristic.

^* said dynamic characteristic comprises at least one characteristic among the energy available on board and a behavior anomaly. ^* the graph comprises nodes designating stations or landing zones, and the step of dynamically determining the new path takes into account the positions of the nodes of stations or landing zones.

^* the dynamic determination step of the new path also takes into account the statuses (free, occupied) of the nodes of stations or landing zones.

^* the method includes a modification of the priority in the event of behavior anomaly.

An unmanned aerial device is also proposed, characterized in that it comprises digital processing and wireless communication circuits suitable for implementing all or part of any of the above methods, as well as a computer program, capable of being loaded on board an unmanned aircraft, characterized in that it comprises instructions capable of implementing all or part of any of the above methods.

Brief description of the drawings

Other aspects, objects and advantages of the present invention will become more apparent on reading the following detailed description of preferred embodiments thereof, given by way of non-limiting example and made with reference to the accompanying drawings, in which :

- Figure 1 is a top plan view of a simplified site on which a UAV must operate,

- Figure 2 is an elevational view of the simplified site of Figure 1,

- Figure 3 is a perspective view of the simplified site of Figures 1 and 2,

- Figure 4 is a view similar to Figure 1, showing safety zones surrounding areas of prohibited theft,

- Figure 5 is a view similar to Figure 2, showing safety zones surrounding areas of prohibited theft,

- Figure 6 is a plan view at a first altitude, illustrating a possible spatial decomposition of the simplified site at this altitude, - Figure 7 is a plan view at a second altitude, illustrating a possible spatial decomposition of the simplified site at this altitude,

- Figure 8 is a plan view at a third altitude, illustrating a possible spatial decomposition of the simplified site at this altitude,

- Figure 9 is a plan view at a fourth altitude, illustrating a possible spatial decomposition of the simplified site at this altitude,

- Figure 10 illustrates a theoretical path via points of the spatial decomposition of Figure 6,

- Figure 11 illustrates a corrected path established from the points of Figure 10, and

- Figure 12 illustrates the overall architecture of a drone system capable of implementing the invention.

Detailed description of preferred embodiments

Introduction

In the following we will denote by drone (or UAV for "Unmanned Aerial Vehicle" in Anglo-Saxon terminology) an unmanned aerial vehicle and remotely and / or self-piloted, preferably equipped with a rotary wing, wing drones (s) bearing (s) also being covered.

Various aspects of the calculation and tracking of dynamic and secure paths will be described here in a complex and potentially dangerous three-dimensional flight space. An architecture capable of implementing these functions will then be described, in particular in terms of the distribution of tasks linked to these functions between the flying vehicles and the ground.

The system to which the invention applies comprises one or more drones capable of flying in a given space, as well as one or more ground charging stations. The invention focuses in particular on finding a path under constraints in this space, respecting the calculated path as well as re-evaluating the path and the destination.

More particularly, the present invention aims to allow a drone to move with the greatest security in a three-dimensional space, part of the topology of which is known in advance. This knowledge makes it possible to establish a representation of the flight space, to take into account both dynamic changes in this space and changes in the state of the aircraft such as the battery level or the appearance of anomalies behavior, and also to take into account the priorities given on the fly by the user or automatically according to a given context (shortest path, the most energy efficient, etc.).

According to one characteristic, a processing unit constructed from mission data comprising in particular the coordinates of a starting point and the coordinates of a point to be reached, a list of waypoints optimized from the point of view of a a number of criteria, including flight safety.

This list of waypoints is recalculated over time each time the topology of the terrain changes, new information becomes available or information previously available is no longer available.

This process aims to take into account in real time and automatically effects as varied as a drop in the quality of the network in a certain area of space, an obligation to travel in one direction in a certain area, the coordinates an available charging station, the presence of other drones in the vicinity of the trajectory, etc.

The three-dimensional flight space provided as input data is a finite volume that may contain volumes or theft is prohibited. In order to prevent positioning uncertainties, static safety margins are taken into account: the volume of the flight space is reduced by reducing the spatial extension of its outer limits and by increasing the spatial extension of the limits of prohibited volumes it contains.

The authorized flight space, defined as the exclusion of the volumes representing the unauthorized flight zones from the total volume, is then subdivided into a set of elements all contained in the authorized flight space. For each of these elements, a characteristic point is chosen. A graph is built by connecting the points between closest neighbors. Advantageously, a weighting is associated with each of the branches and depends on the constraints imposed on the system and are described below. This weighting can be oriented, namely that two different points can be associated with a branch, according to the direction in which it must be traversed. When the destination is indicated by the user, the graph is traversed to find the optimal path according to the constraints.

Once the path has been calculated, that is to say once the flight plan has been established, the path is followed by the UAV. In order to ensure compliance with the calculated trajectory, a volume containing the trajectory is calculated based on environmental conditions, flight parameters (speed, acceleration, etc.). This volume results from the application of a non-isotropic release factor of the trajectory and corresponds to a mandatory flight volume for the UAV following said trajectory. The release factor is calculated periodically and the mandatory flight volume is modified accordingly to take into account dynamic changes in the conditions for modifying said release factor. The weighting associated with the branches of the graph is periodically recalculated and the path between the current position and the destination minimizing the “cost” of the journey according to one or more criteria is recalculated. The notion of travel cost is determined by a high level priority chosen by the user: priority to the shortest travel time, to the highest average travel speed, to increased safety. When the new path crosses the volume associated with the previous path, then the release factor is recalculated and the mandatory flight volume recalculated accordingly. The behavior of the UAV is monitored and when an anomaly is detected a modification of the destination can take place, and the UAV then proceeds to the pre-defined safety zone, maximizing the safety of the flight.

Representation of flight space

We will now describe in detail, with reference to Figures 1 to 11, a path search method and construction and tracking of the path. Referring firstly to Figures 1 to 3, the representation of the flight space E can be performed in three dimensions by considering it as a polyhedron called "encompassing" PEG (typically a cylinder with vertical directrix s' pressing on the limits of a site, here a fence CL) entirely containing a set of other so-called “excluded” polyhedra, here for the sake of simplification of the rectangular parallelepipeds PEX1, PEX2 and PEX3, the volume of which is prohibited from flight. These polyhedra can represent, for example, buildings, industrial installations, tanks, parking or work areas. In order to take into account both the uncertainty of the position measuring instruments and the errors introduced by the three-dimensional model itself, static safety margins for both the enclosing polyhedron and for the excluded polyhedra are calculated using determining a predefined increase in the size of the excluded polyhedra and a predefined decrease in the size of the enclosing polyhedron. The distance of increase or decrease can be chosen in different ways using for example the known error of the positioning system. It can be different in the two horizontal dimensions and the vertical dimension. It is typically of the order of 5 m.

Thus the references PEG ’and PEX1’, PEX2 ’and PEX3’ designate in Figures 4 and 5 these polyhedra "expanded" after correction.

Referring now to Figures 5 and 6 to 9, the three-dimensional model of the flight space is here seen as the superposition of horizontal layers at different altitudes. Horizontal slices of fixed altitudes are created between horizontal planes located at the minimum and maximum altitudes of each of the polyhedra contained in the flight space. Here a plane PO corresponds to the common minimum altitude of the three excluded polyhedra PEX1, PEX2, PEX3, while planes P1 to P3 correspond to the maximum altitudes in the increasing direction of the excluded polyhedra, namely PEX3, PEX2 and PEX1. The intersection between each plane and each polyhedron itself forms a polygon. Here the polyhedra are of constant horizontal section over their entire height. In the case of polyhedra of variable section, the projection of the polyhedron in the planes at the level of its widest section for the slice considered is determined by calculation.

The design of the three-dimensional model can also include a plane P4 (see Figure 5) determined as a function of a maximum flight altitude of the UAVs, this plane being carried over to an almost infinite altitude in the absence of such a limitation.

The flight space is modeled by a 2.5-dimensional space consisting of a set of slices here T01, T12, T23 and T34 of constant horizontal sections, which are respectively delimited by the pairs of planes P0-P1, .. ., P3-P4, whose boundaries are externally those of the corrected enclosing polyhedron PEG 'and internally those of the corrected excluded polyhedra PEX1' to PEX3 'which have intersections with these slices, each of these slices defining over its entire height a zone of authorized flight.

Figures 6 to 9 respectively illustrate the sections of the four slices based on the model of Figures 1 to 5.

It can be seen in Figure 9 that the higher altitude slice T34 does not contain any excluded polygons.

In this flight space, one or more UAVs are required to evolve and are in interaction with one or more charging stations located in accessible areas of the flight zone. Emergency landing zones can also be taken into account in defining the flight space. They correspond to areas that may or may not contain a charging station and are selected areas in which a UAV can land safely. A charging station must necessarily be located in an emergency landing zone: in the event of a problem while landing a UAV in the station, the UAV has a fallback solution that is quickly and safely accessible. In the current description, an emergency landing zone can be located above an obstacle, but cannot be located at the same level.

The creation of the site template to browse also includes the positioning, performed using an appropriate user interface, of recharging stations for UAVs, and where appropriate emergency landing zones separate from the recharging station zones.

Advantageously, this positioning is carried out taking into account the safety margins of the prohibited areas, so as to prevent a UAV from having to enter such a prohibited area during an emergency landing. Subdivision of the flight space into individual elements and construction of a representation of the flight space in the form of a graph

At this stage, the horizontal plane of the authorized flight zone in each of the aforementioned sections is subdivided by a processing system into a set of individual elements or blocks PVk thus constituting a paving of the authorized flight zone in this section. Paving can be done in different ways. Advantageously, a Delaunay triangulation or one of its variants is used for this tiling (see in particular https://fr.wikipedia.org/wiki/Triangulation_de_Delaunay), the tiles thus all being triangular in shape.

Advantageously, a constrained Delaunay triangulation is used (see for example Christophe Lemaire. Delaunay triangulation and multidimensional trees. Image synthesis and virtual reality [cs.GR]. Ecole Nationale Supérieure des Mines de Saint-Etienne; Université JeanMonnet - Saint-Etienne, 1997. French. NNT: 1997STET4021; tel-00850521, chapter 1 .5).

An advantage of Delaunay's triangulation is that it is not very demanding on the computational resources required to perform the triangulation, so that the subdivision into blocks can be done on board the UAV.

In addition, the triangulation under constraint makes it possible to impose that the result of the triangulation respects a particular shape in places because the individual elements of the model can intersect (case for example of a prohibited zone of flight in the middle of an authorized flight zone).

Once the triangulation has been carried out, the processing unit determines for each block the coordinates of a characteristic point Pk. One possible choice is to take the center of mass of the block. Indeed, by definition, the center of mass of a triangular paving stone resulting from a Delaunay triangulation is necessarily located inside this paving stone, and therefore in the authorized flight zone of the horizontal slice Txy considered.

The processing unit then constructs a graph whose nodes are each of these characteristic points Pk. Each node has for properties a node identifier Ik and its three coordinates Xk, Yk, Zk in an orthonormal three-dimensional space. The branches of the graph include branches connecting the closest nodes located in the same slice, and on the other hand branches connecting the closest nodes in two neighboring slices. The nodes constituted as the closest in the same slice are advantageously the characteristic points of adjacent triangles by one of their sides (simplicity of construction). The nodes closest to two adjacent slices are the nodes whose calculated mutual distance is the shortest, it being specified that a node of a given slice can have one or more branches connecting it to one or more nodes of the branch immediately higher (when it exists), and one or more branches connecting it to one or more nodes of the immediately lower branch (when it exists). Typically, the processing unit does not branch between slice nodes that are not immediately adjacent, but there may be exceptions for particular site configurations. Moreover, in all cases, a branch can only be generated between two nodes if it does not create an intersection with the interior and exterior borders of the slices considered, and the processing unit verifies this condition by applying simple geometric rules. at each generation of branch.

Figures 6 to 9 illustrate the Delaunay triangulations and the associated characteristic points in each of the slices of the simplified model used so far.

Once the graph has been established (or during its construction), the processing unit assigns to each of these branches a base weight which is proportional to the length of the branch, determined from the coordinates of the two nodes it connects.

In a preferred embodiment, this basis weight can be affected by a travel direction correction coefficient, to promote travel in one direction over another, by decreasing the basis weight and increasing it in the direction of travel. opposite, possibly up to making it sufficiently large so that no route in this direction can be proposed during the search for a better route by the processing unit (see below).

For branches connecting nodes at different altitudes, the basis weight can be corrected by an altitude factor determined based on the difference in altitude between the two nodes. The value of this corrective factor can be chosen heuristically, and is positive in the upward direction and negative in the downward direction. In this way, a change in altitude is favored in the direction of descent, and disadvantaged in the direction of ascent.

Other factors of dynamic variation in the weight of the branches will be described below.

Path search and update

The processing unit on board the drone is able to receive as input data the coordinates of a desired destination on the site, this destination being either entered by a user and communicated to the drone by the available means of communication, or determined. automatically according to other treatments. Depending on its current position and the destination data, the processing unit on board the UAV relies on the graph defined as described above, loaded into the memory of each UAV when it is installed on site, to construct the path allowing to reach the said destination, and execute the control commands allowing to follow the path.

The determination of the path is broken down into two parts: - the first consists of finding a path globally throughout the flight space;

- the second consists of the construction of a trajectory allowing to follow the path found in the previous step.

When the destination is received by the drone, the processing unit traverses the paving determined as described above to identify the triangular block encompassing the destination, in the altitude range immediately below the altitude of the destination. This search can be done by browsing a table listing all of the geometric characteristics of the tiling, as determined by the Delaunay triangulation.

If such a block is found in the table, then the destination is indeed contained in the authorized flight zone. In other words, destinations outside the authorized flight space are unreachable by construction.

Once the destination has been validated, the processing unit brings into play a graph traversing process, of a type known per se, to find the shortest path in the graph while minimizing the sum of the weights of the branches to be traversed. This process can be based for example on a known algorithm such as A ^* or Dikjstra (see for example https://dzone.com/articles/from-dijkstra-to-a-star-a-part-2-the-a -star-a-algo).

FIG. 10 illustrates a basic path CHB obtained, which is a broken line whose intermediate points or passage points are the characteristic points of the graph for which the sum of the weights is minimized.

The main object of this CHB basic path, in complex environments, is to determine the optimal route between the prohibited zones with respect to this weight minimization.

From this basic path, the processing unit builds an effective CHE path, an example of which is shown in Figure 11, by performing a number of operations on the basic path, in particular:

- elimination of certain waypoints using alignment tests (if three waypoints PPn-1, PPn and PPn + 1 are, at a approximation close, on the same line, then the intermediate point of passage PPn is eliminated);

- elimination of certain crossing points by calculating a straight line connecting crossing points PPn-1 and PPn + 1 located on either side of a crossing point PPn, by determining whether this straight line intersects one or more dilated prohibited zones , and elimination of the waypoint Pn if this test is negative;

- refinement of the path freed from certain unnecessary intermediate points by an approach of reducing the sum of the weights; this process involves for example a dichotomous approach: if we consider a section of path made up of three waypoints PPn-1, PPn and PPn + 1, the point PPn is replaced by a point PPn 'of the segment PPn- 1 -PPn such that the weight associated with the PPn'-PPn + 1 branch is less than the weight associated with the PPn-PPn + 1 branch, this search for the point PPn 'being carried out by dichotomy; an effective path CHE is thus generated, the total weight of the branches of which is minimized.

At the end of these steps, the processing unit uses data from the effective CHE path to build a volume or flight corridor that the UAV must comply with. This volume is constructed by taking into account a relaxation factor around the CHE path.

This release factor is determined from a maximum span data of the UAV, increased by a factor which can be either uniform and depend on the nature of the site, or variable according to the location of the CHE path. and in particular of its distance to prohibited flight zones (after expansion), that is again the sum of a uniform factor and a variable factor.

In a basic embodiment, taking this slack factor into account in the computation of the mandatory flight lane involves the computation of a set of truncated cones placed end to end around the CHE path, the radius of the base of each truncated cone being equal to the relaxation factor. Gradually the volume of flight is built around the path to follow CHE. This flight corridor can either be calculated after establishment of the CHE path, for the whole thereof, or dynamically calculated during the flight of the UAV. It will be recalculated each time the ILAV determines a new CHE path after varying the weights of the branches of the graph.

Periodically, the UAV compares its current actual position with geometric data from the flight lane. When this comparison detects an exit from the flight lane (in particular as a function of external factors such as a strong wind, a temporary problem with GPS location, etc.), a corrective flight instruction is applied to the autopilot according to the 'measured position deviation.

It should be noted that other factors may be useful in the static or dynamic determination of the authorized flight lane, and in particular:

- IlAV agility factors (type of wing, minimum speeds - in the case of a fixed load-bearing wing - and maximum, maximum acceleration, etc.),

- characteristics of on-board sensors (Lidar, Laser, etc.), these factors influencing in particular the ability of the UAV to dynamically detect and avoid collisions. In general, a flight corridor is expected to be narrower as these skills are weak.

Furthermore, once the size of the flight corridor has been established, the UAV, within this corridor, can be expected to adopt a trajectory which is different according to these parameters or other parameters, whether dynamic or static. Thus the determination of the trajectory can be influenced by the value of various parameters having the effect of favoring the shortest possible trajectory, or one allowing the execution time to be reduced as much as possible, or even the one remaining as far as possible from the obstacles.

According to another characteristic, it is possible to provide that an exit from the flight lane causes, rather than a corrective action on the autopilot aimed at allowing the UAV to find its lane, a new calculation of the path then of the flight lane. associate. In practice, when a mission order comprising destination data is received by the UAV, the on-board processing unit launches the first global path search. Then, during the flight, communication channels between the UAV and other equipment (ground equipment, sensors, other UAVs, etc.) allow the processing unit to update the weights of the branches of the graph. .

At the same time, either at a determined frequency (for example once per second), or each time a weight is modified, the processing unit of the UAV performs a new path search between its current position and the indicated destination at the beginning.

It is also possible, during the flight, for the UAV to receive or determine a new destination, and in this case a new path between its current position and the new destination is calculated, and updated as described above.

Once the path is found, the flight lane is calculated and stored so as to be accessible by a local path planner.

Where appropriate, in the case where the on-board processing unit has information on the autonomy of the battery (s) of the UAV, this information is compared with the sum of the weights of the path CHE, to determine whether the UAV has sufficient autonomy to reach the destination, with an appropriate margin of error.

In the event that flight is possible, the local flight path planner, at a determined frequency and for example 50 times per second, applies flight instructions to the autopilot so that the UAV moves in the corridor. As described above, this planner also tests, preferably at the same frequency, any lane exits and applies the appropriate corrective instructions to the autopilot.

It will also be noted that this trajectory planner can take into account, either statically or dynamically, a maximum authorized speed in the corridor.

Adjustment of the weights of the branches of the graph In the preceding description, the basis weights associated with the branches of the graph representing the flight space are calculated as being proportional to the distance between the nodes that the branches connect.

Each UAV capable of flying on a site contains in its memory the data of this graph, with the basic weights, and as we have seen the on-board processing unit will determine the flight lane to follow to reach a given destination. .

At the same time, the UAV's means of communication with the ground, or even with other UAVs flying on the same site, or even with information sources on site (sensors, etc.) or external (weather data , etc.) allow the UAV to collect data that may affect the weight values.

Mathematically, these data can be of the scalar field type or of the vector field type.

A scalar field corresponds, for example, to a variable such as a score for the quality of the communication network between the UAVs and the ground, temperature, humidity, etc.

These data are scalar in the sense that they are not oriented and affect all weights in the graph in the same way.

For example, a particularly low temperature can lead to increasing the basis weights by a given multiplying factor, to take into account that the autonomy of the UAV at low temperature is reduced due to a loss of efficiency batteries.

On the other hand, the wind can be represented in the form of a vector field, with each point or region of the flight space being associated with a vector whose orientation represents its direction, and whose norm represents its force. Receiving a vector field (or a vector applicable to the current location of the drone) makes it possible to recompute the weights of the branches of the graph by a scalar product function, the branch also being considered as a vector whose orientation corresponds to its direction and whose norm represents the basis weight. In the case where the value of the wind vector along a given branch changes depending on the position in the branch, the processing unit determines an average of the vector products at different points of the branch.

Note that the granularity of a vector field likely to affect the weights can vary widely. For example in the case of wind, we can use a single wind vector for the entire site, accessible from a connected anemometer or an external weather source, or different wind vectors depending on the areas of the site. site, whether the "local" wind is measured by sensors or determined for example by simulation.

Note that the component of the weight of a branch resulting from this calculation is oriented: the force of a wind that is not perpendicular to a branch decreases the base weight in one direction (upwind path), and increases the base weight in the other (wind route with).

A module for updating the weights of the branches of the graph modifies the weights preferably each time new data resulting from external constraints are available. To minimize the risk of error, any new path calculation request during a weight update operation is made on the basis of the current graph before the update, a copy of which is kept for this purpose.

Modification of the path according to a priority given to the flight - different types of weight

When establishing a mission, either by a user or automatically, the mission data can advantageously include a type of priority to reach the destination set by the mission.

For example, four types of priorities can be provided, namely:

- minimization of the absolute distance to be covered,

- minimization of travel time,

- minimization of energy consumption, - risk minimization, with possible sub-categories depending on the type of risk (on people, on goods, etc.).

In general, the current value of the weight of a branch for a given direction of travel is obtained by combining the basic weight (length of the branch) with the various corrections made by one or more scalar fields and / or by one or several vector fields, as described above.

Priority management implies being able to give each branch a different nature or weight value.

In the case where the priority is to minimize the absolute distance, the path search is performed on the weighted graph with the basis weights or the basis weights corrected for example with a wind vector.

To be able to take into account a priority of the travel time minimization type, it is possible to correct the distance weight (base weight, corrected or not) assigned to each branch a coefficient linked to the maximum speed authorized in this branch.

Advantageously, this correction is carried out by including in the data of the site to be modeled a mapping of authorized speeds (depending in particular on the type of equipment nearby or overflown, a risk related to people, etc.). Then, once the structure of the graph has been established, the processing unit assigns information on the maximum authorized speed to each branch depending on the location of this branch in the speed map. From the base weight (length of the branch) and this maximum speed information, the processing unit calculates a minimum travel time weight (that obtained for the maximum authorized speed), by multiplying the base weight if necessary corrected by a scalar or vector field by a coefficient that is all the smaller as the authorized speed is high, and vice versa.

If the mission includes this priority of minimizing travel time, the search for the best path is no longer based on weights representative of the distance, but as a function of these travel time weights.

Another mapping that the system can advantageously use is a mapping defining areas having different levels of risk. This risk mapping makes it possible, for example, to take into account the presence of personnel or personnel circulation areas, the dangerousness of the various installations, etc. In the same way as for the mapping of authorized speeds, the processing unit alters the weight of each branch according to the risk score of the zone in which the branch is located, thus, ultimately, to disadvantage people. paths crossing high risk areas compared to paths crossing lower risk areas.

In case the priority of the mission is to minimize energy consumption, one possible approach is to determine a density of waypoints. In this regard, the greater the number of waypoints, the more frequent the changes of direction and speed of the UAV will be, which is an important factor for the consumption of energy.

The path can then be determined no longer by searching for the shortest path in distance or time, but by determining a set of possible paths all having a sum of weight in time or in distance less than a threshold, and at select the path that has the minimum number of waypoints.

Finally, if the priority is flight safety, a risk factor derived from its proximity to equipment constituting a prohibited zone can be assigned to each branch. The greater the proximity, the greater the risk factor. Once the structure of the graph has been obtained, this "risk" weight is determined by calculating the distance of each branch generated with the closest prohibited zone, and by assigning to the distance weight (base weight after possible correction by scalar field or vector) a multiplying coefficient all the more important as this distance is short (the coefficient being typically equal to 1 for all the branches whose distance from a prohibited zone is greater than a determined threshold).

The best path from the point of view of flight safety is then the one which minimizes the sum of the risk weights.

To further refine this priority management, it is possible to combine the basic weights (possibly corrected by scalar and / or vector fields) with the aforementioned speed, energy consumption and risk factors in different measures, in a manner to adapt the importance of each alteration to the priority of the mission.

For example, we can set an order of priorities (for example safety then speed then energy) and consequently modulate the impact of the corresponding weight correction factors.

We will now describe an example of calculating the weight of a branch of the graph.

The general formula for calculating this weight is given by:

wAB; j = SOMMEi (Yi; jGi (A, B))

or

• A and B are nodes of the graph that can be connected by a straight line without intersection with the interior of a prohibited zone (and where appropriate without contact with its edge),

• j is a priority factor,

• Gi (A, B) are functions representing a contribution to the calculation of the weight,

• Yi; j is a factor associated with a contribution.

In a concrete example, let us consider three contributions to the calculation of the weight, namely three functions G1, G2 and G3;

• G1 (A, B) represents the distance between points A and B,

• G2 (A, B) represents the average quality of the GPS positioning signal between points A and B,

• G3 (A, B) represents the inclusion of risk areas.

The mathematical formulation of these three functions can respond to different approaches that it is not necessary to detail here. Now consider two priorities:

• j = 1: Shortest distance traveled,

• j = 2: Risk zones taken into account.

For each of these two priorities, the system chooses the contribution of the three functions G1, G2, G3 to the weight of the branch by modifying the value of the corresponding parameter Yi; j.

Thus, in the above case where priority is given only to the smallest distance traveled (j = 1), we can use:

- Yi = 1 = 1

- Yi = 2.3 = 0

In the case where priority is given only to taking into account the risk areas (j = 2), we can use:

- Yi = 1, 2 = 0

- Yi = 3 = 1

Of course, coefficients Yi; j with values other than 0 and 1, ensuring combined consideration of different priorities, can be used.

Modification of the flight space according to an imposed direction of movement

At any time the user or an external factor can impose a zone, in particular a zone of passage between two prohibited zones, in which a certain direction of circulation is made compulsory.

In this case, the weights associated with the branches of the graph extending at least partially in this zone are modified so as to leave intact the weights associated with the branches in the direction which respects this direction of circulation, and to make infinite or almost infinite the weights in the opposite direction (from the point of view of the mathematics of the graph, give them a very high value).

It should be noted here that as a general rule, a UAV should be able to return to its starting area. However, depending on the imposed direction of flight topology, it is possible that the one-way criterion does not allow it. To ensure that the UAV can return to its starting point, even in a context of meaning single traffic, the existence of a high but not infinite weight for the route in the prohibited direction nevertheless allows the UAV to travel a one-way zone in the prohibited direction when there is no other choice.

Modification of the flight plan in response to the dynamic characteristics of the UAV

When the UAV is powered on, an available flight time estimation module is launched and determines this flight time based in particular on the state of charge of the battery, in-flight consumption measurements taken. recently, the ambient temperature, etc.

When the UAV is on a mission, the processing unit calculates at a given rate, for example once per second, a so-called “emergency” path between its current position and the position of the closest available charging station (or other landing zone). As long as the time required to travel this path is less than the estimated time remaining indicated by the aforementioned module, the UAV continues its mission.

When the estimate of the available flight time becomes equal to the flight time to reach the nearest charging station (possibly within a safety margin), then the UAV processing unit causes a mission abandonment in replacing the path currently traveled within the framework of this mission by the emergency path calculated from the current position and the closest landing position, so as to return towards it and land.

According to another approach, the emergency path is imposed in response to technical anomalies observed by the UAV during the mission. Thus, autopilots are generally able to provide various data concerning the state of health of the drone, such as the precision of the position determination circuit (so-called EKF circuit for "Extended Kalman Filter"), the level of vibration, etc.

As soon as the UAV is powered on, an anomaly detection module connected to the EKF circuit and to a vibration sensor (typically part of its inertial unit) is activated. For all the types of data analyzed, this module estimates whether the values received are within the range of acceptable values or not. One possible implementation consists of calculating a simple average over a given time window for each type of data received, and comparing it with a stored range of acceptable values. If an average value is outside this range, the fallback path is automatically calculated, loaded and tracked. Modification of the flight space: prohibited altitudes, presence of other UAVs

Knowing that several UAVs can fly on the same site, it is expected according to this functionality that the presence of other UAVs in flight on the site is taken into account in the establishment of the path or the dynamic modification of it.

This functionality is advantageously implemented in addition to anti-collision devices which can be fitted to UAVs, such as Laser or Lidar, the efficiency of which implies direct visibility of the obstacle and which, moreover, can require significant digital processing resources. .

More precisely, rather than recalculating the structure of the graph by considering a drone as a mobile prohibited flight zone, a solution can consist in receiving at the level of a UAV the current position of another UAV in flight in the vicinity, to identify the branches of the graph located at a distance less than a threshold of this position, and to attribute to the weights of the branches thus identified a very high multiplying factor, so that a path recalculated after updating the weights avoids the branches in question.

This aspect makes it possible to significantly enhance flight safety when a fleet of UAVs is able to operate on the same site.

Architecture

An architecture has been illustrated in FIG. 12 making it possible to implement the various aspects described in the above.

A first processing unit 100 receives model data from the site and the associated maps. From this data, it performs the expansion of the prohibited flight zones, determines the authorized flight zones at different altitudes, carries out the subdivisions for example by Delaunay triangulations at each of the altitudes, generates the points of the graph from the coordinates of the individual blocks, and interconnects these points of one hand in each horizontal plane corresponding to an altitude and on the other hand between adjacent horizontal planes.

For each branch of this graph, its length is calculated from the coordinates of the points it joins, to thus determine its basis weight.

The data of this graph is transmitted by the communication channels adapted to each of the UAVs 200a, 200b, 200c, etc. likely to circulate on the site, where they are stored.

Each time the site environment changes (appearance or disappearance of a no-fly zone, for example), an updated graph is determined and transmitted to each UAV.

A mission is generally initiated by transmitting mission data from a ground station 300, separate from or part of the processing unit 100, to a given UAV, here 200a.

The processing unit 210 on board this UAV receives the mission data, typically comprising:

- the coordinates of a destination,

- a priority for the flight, or several ordered priorities,

- other parameters of the mission, and in particular instructions for shooting while traveling or hovering, etc.

The processing unit 210 on board the UAV also receives, either before the start of the mission or periodically including during the flight, scalar and / or vector data likely to affect the basic weights of the branches. .

Depending on the priority data and the scalar and / or vector data, as well as any data affecting the directions of movement, the processing unit 210 calculates the effective weights of the different branches, and determines the base path CHB according to the data of the graph with its effective weights, the current coordinates of the UAV (starting point) and the destination data received.

The processing unit 210 then determines the actual path CHE.

The ability of the UAV to perform the mission, depending on its autonomy, is then measured.

In the event of sufficient autonomy, the mission can then start, and during the flight, the on-board processing unit monitors any exits from the corridor and applies the necessary corrective actions to the autopilot, receives dynamic data liable to affect the weight of the branches of the graph, recalculates the path as necessary, recalculates the feasibility of the mission in operation of the updated autonomy, and monitors any anomalies on board, likely to cause the replacement of the current path of the mission by an emergency path.

Of course, the present invention is in no way limited to the foregoing description and many variations are possible.

In particular :

- flight data can be collected and gathered for access through a learning process to determine, when the constraints are similar to previously encountered constraints, to determine the path to be traveled by experience rather than by calculation;

- mission data may include not only destination data, but also mandatory crossing point data, in particular for planned surveillance;

the various processing operations described in the foregoing as being carried out on the ground or in an on-board manner can be carried out in different processing architectures; in particular, the creation and updating of the graph from the site model can be carried out, if the computing power is suitable, on board each of the UAVs.

Claims

1. A method of modeling by digital processing of a three-dimensional environment with a view to establishing pathways for unmanned aerial devices optimized according to different priorities, characterized in that it comprises the following digital processing steps:

(b) subdivide the model into individual elements (PVk),

(c) determine a center (Pk) for each individual element,

2. The method of claim 1, wherein the priorities include at least two priorities from among an absolute distance priority, a travel time priority, a power consumption priority, a risk priority.

3. Method according to one of claims 1 and 2, wherein at least one of the weights depends on a constraint affecting all branches.

4. The method of claim 3, wherein said stress comprises a stress vector affecting all the branches.

5. The method of claim 4, wherein the stress vector is a wind vector, each branch having a pair of weights associated respectively with the direction of travel and each weight being separately subject to the wind vector.

6. Method according to one of claims 1 and 2, wherein said weighting is such that different weights are assigned to the same branch according to the direction of travel, so as to generate preferred directions of travel.

7. Method according to one of claims 1 and 2, wherein said weighting is based on a mapping defining different stress levels depending on the location in a flight space.

8. The method of claim 7, wherein the stress levels are included in a group comprising a maximum authorized speed constraint and a risk constraint.

9. The method of claim 8, wherein the constraint is able to take a value such that the corresponding zone becomes a no-fly zone.

10. Method according to one of claims 1 to 9, wherein step (a) comprises providing a three-dimensional model comprising volumes (PEXi) in which theft is physically impossible, and reprocessing this model with static safety margin data.

1 1. The method of claim 10, wherein step (a) comprises subdividing the three-dimensional model into horizontal slices (Txy), the projection of the volumes on a horizontal plane being the same throughout the thickness of each slice, and setting implementation of a subdivision into individual elements in each horizontal plane.

12. The method of claim 11, wherein the subdivision is performed by triangulation.

13. The method of claim 12, wherein the triangulation is a Delaunay triangulation.

14. The method according to one of claims 1 1 to 13, wherein step (d) comprises establishing branches of the graph between nodes located in adjacent horizontal planes by a distance minimization approach.

15. A method for determining by an unmanned aerial device a path between two points of a three-dimensional space modeled by a graph obtained by the method of one of claims 1 to 14, characterized in that it comprises the steps following:

- determination of a priority for the route,

16. The method of claim 15, wherein the step of weighting the branches of the graph is implemented by remote reception of a starting graph with unweighted branches, and weighting, on board the device, of said branches. depending on the priority.

17. Method according to one of claims 15 and 16, which comprises, during the flight, a step of updating the weights of the branches of at least part of the graph and a step of recalculating the best path in the graph. .

18. The method of claim 17, wherein the updating of the weights of the branches of the graph is performed as a function of a change in priority.

19. The method of claim 17 or 18, wherein the updating of the weights of the branches of at least part of the graph is performed based on the receipt of weighting data modified for the weight corresponding to the priority in effect.

20. The method according to one of claims 15 to 19, wherein the step of updating the weights of the branches of the graph comprises the generation of forbidden branches as a function of a forbidden zone appearing dynamically.

21. The method of claim 20, wherein the prohibited zone is determined by remote communication of the machine with other equipment whose position determines the prohibited zone.

22. The method of claim 21, wherein the other equipment is another unmanned aircraft.

23. The method of claim 22, wherein the prohibited zone is a prohibited altitude stop.

24. The method of claim 23, wherein the other equipment is associated with a temporary intervention on the site.

25. Method according to one of claims 15 to 24, in which the calculation of the best path is carried out according to an agility constraint of the apparatus.

26. A method of piloting an unmanned aircraft, comprising the following steps:

- determination of a path by the method according to one of claims 15 to 25, - application of at least one trajectory relaxation factor,

27. The method of claim 26, wherein the release factor is determined from at least one piece of data representative of one of the following information: current accuracy of a GPS unit on board the device, wind, response of the device with the piloting controls, dimensions of the device, type of device.

28. A method of piloting an unmanned aircraft, comprising the following steps:

- determination of a path by the method according to one of claims 15 to 25,

- measurement of a dynamic characteristic of the machine during flight,

29. The method of claim 28, wherein said dynamic characteristic comprises at least one characteristic of the energy available on board and a behavior anomaly.

30. The method of claim 28 or 29, wherein the graph comprises nodes designating stations or landing zones, and wherein the step of dynamically determining the new path takes into account the positions of the nodes of stations or zones of landing. 'landing.

31. The method of claim 30, wherein the step of dynamically determining the new path also takes into account the statuses (free, occupied) of the nodes of stations or landing zones.

32. The method of claim 29, which comprises a modification of the priority in the event of an abnormal behavior.

33. Unmanned aerial device, characterized in that it comprises digital processing and wireless communication circuits suitable for implementing all or part of the method according to one of claims 1 to 32.

34. Computer program, suitable for being loaded on board an unmanned aircraft, characterized in that it comprises instructions suitable for implementing all or part of the method according to one of claims 1 to 32.