GB2567810A - Method and system for determining optimal path for drones - Google Patents
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Abstract
A path for a drone is optimised within a defined area by creating a route-grid which has a plurality of cells associated with the area, obtaining a preference factor for each cell by analysing parameters associated with each cell, defining start and end coordinates for the drone in the route-grid, obtaining several potential routes, and determining a cumulative (total) preference score for each route between the start and end coordinates based on the preference factor of the cells that comprise each route. The cumulative preference scores are then used to select an optimal path. The parameters associated with each cell may be static, perhaps relating to terrain or structures, or may be dynamic, perhaps relating to air traffic or weather, and these parameters may be used to determine the preference score for each cell. The best path for the drone may be selected using a graph traversal algorithm.
Description
METHOD AND SYSTEM FOR DETERMINING OPTIMAL PATH FOR DRONES
TECHNICAL FIELD
The present disclosure relates to a method of determining an optimal path for a drone in a defined area. Moreover, the present disclosure relates to a system for determining an optimal path for a drone in a defined area.
BACKGROUND
In recent years, drones, also known as unmanned aerial vehicles (UAVs), have been widely used in various fields such as aerial photography, surveillance, scientific research, geological surveys, goods delivery and remote sensing. Generally, the drones carry on board a variety of electrical components used to control various aspects of the operation of the drones. At the same time, the drones include one or more sensors for navigational, surveillance or remote sensing purposes.
Typically, the movement of a given drone may be based on a preprogrammed flight path. The pre-programmed flight path is usually based on a shortest and a quickest path available from a given first location to a given second location, without considering various factors associated with the flight path. However, precise knowledge of the various factors associated with a defined area is particularly necessary in order to prepare the flight path of the drones. In an example, the planning of the flight paths requires precise knowledge of a terrain to be traversed, a wind direction, a network coverage pertaining and so forth, of the defined area over which the drone is to be flown. In addition, as the drones become ubiquitous, the chances of a drone inadvertently entering into a restricted air space increases. Examples of restricted airspace include, but are not limited to, airports, airplane flight paths, no-fly zones, buildings/skyscrapers, military reservations, stadiums, private property and other geographic boundaries. Several national and regional agencies continue to develop guidelines and regulations for drone operations of all kinds (civil, commercial, recreational, etc.). However, presently, there are no systems that effectively prevent or otherwise restrict the drones from flying into restricted air space. Moreover, the public is becoming more aware of the growing use of drones for various purposes, wherein the drones could become vulnerable to tampering. For example, the control of a drone might be intercepted or interfered with in-flight such as by intercepting, jamming, and/or imitating (e.g., pirate signals) global positioning or Global Navigation Satellite System (GNSS) signals in order to direct a drone to a surrogate landing zone. Furthermore, drones may require a continuous coverage network connection for communication. However, the drone may lose such network connection due to no network coverage, or dead spots, and may become lost, thereby putting the drone at risk.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with conventional unmanned aerial vehicles.
SUMMARY
The present disclosure seeks to provide an improved method of (for) determining an optimal path for a drone in a defined area.
The present disclosure also seeks to provide an improved system for determining an optimal path for a drone in a defined area.
According to a first aspect, an embodiment of the present disclosure provides a method of (for) determining an optimal path for a drone in a defined area, characterized in that the method comprises:
- creating a route-grid having a plurality of cells associated with the defined area;
- analysing one or more parameters associated with each of the plurality of cells of the route-grid to obtain a preference factor for each of the plurality of cells;
- defining origin and destination coordinates for the drone in the route-grid to obtain a plurality of potential paths therebetween, wherein the pluralities of paths traverse one or more cells;
- determining a cumulative preference score for each of the plurality of potential paths between the origin and the destination coordinates based on the preference factor of one or more cells traversed by the plurality of potential paths; and
- selecting the optimal path from the plurality of potential paths based on the determined cumulative preference scores.
The present disclosure seeks to provide an efficient and reliable method of (for) determining an optimal path for a drone in a defined area; furthermore, the present disclosure is operable to provide an inexpensive, safe and user-friendly system for managing a flight path of a drone and, moreover, for reducing power consumption of the drone.
Optionally, a size of the plurality of cells of the route-grid is based on the defined area.
Optionally, the origin and destination coordinates for the drone are defined by a user of the drone.
More optionally, the one or parameters associated with each of the plurality of cells include static and/or dynamic parameters.
Optionally, the static parameters include at least one of: terrain, civil structures, communication structures.
Optionally, the dynamic parameters include at least one: air traffic, weather.
More optionally, obtaining a preference factor for each of the plurality of cells includes analysing at least one parameter of the static and/or dynamic parameters.
According to a second aspect, an embodiment of the present disclosure provides a system for determining an optimal path for a drone in a defined area, characterized in that the system comprises:
- a user device for generating a request for the optimal path; and
- a server arrangement communicably coupled to the user device, wherein the server is operable to:
- create a route-grid having a plurality of cells, associated with the defined area;
- analyse one or more parameters associated with each of the plurality of cells of the route-grid to obtain a preference factor for each of the plurality of cells;
- define origin and destination coordinates for the drone in the route-grid to obtain a plurality of potential paths therebetween based on the user request, wherein the pluralities of potential paths traverse one or more cells;
- determine a cumulative preference score for each of the plurality of potential paths between the origin and the destination coordinates based on the preference factor of one or more cells traversed by the plurality of potential paths; and
- select the optimal path from the plurality of potential paths based on the determined cumulative preference scores.
Optionally, a user defines the origin and destination coordinates for the drone using the user device.
Optionally, the one or parameters associated with each of the plurality of cells include static and/or dynamic parameters.
More optionally, the system further includes a database arrangement operable to store information associated with the static and/or dynamic parameters.
Yet more optionally, the server arrangement is operable to employ at least one graph traversal algorithm to determine the optimal path for the drone.
It will be appreciated that the features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
DESCRIPTION OF THE DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 is a schematic illustration of a system for determining an optimal path for a drone, in accordance with an embodiment of the present disclosure;
FIG. 2 is an illustration of an exemplary user interface for determining an optimal path for the drone, in accordance with an embodiment of the present disclosure; and
FIG. 3 is an illustration of steps of a method of (for) determining an optimal path for a drone in a defined area, in accordance with an embodiment of the present disclosure
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DESCRIPTION OF EMBODIMENTS
In overview, embodiments of the present disclosure are concerned with methods for determining an optimal path for drones. Moreover, embodiments of the present disclosure are concerned with systems for determining such optimal path for the drones.
Referring to FIG. 1, there is shown an illustration of a system 100 for determining an optimal path for a drone 102, in accordance with an embodiment of the present disclosure. As shown, the system 100 comprises a user device 104 for generating a request for the optimal path for the drone 102. The system 100 further comprises a server arrangement 106 that is communicably coupled to the user device 104. Specifically, the server arrangement 106 is configured, namely operable, to receive the request generated by the user device 104 for the optimal path of the drone 102.
Throughout the present disclosure, the term 'drone' may refer to an unmanned aerial vehicle (or UAV); and specifically, to an aircraft without human pilots and/or passengers on-board. Specifically, the drone may be operated fully or partially autonomously for real world applications (or missions) such as asset inspection, aerial photography, and so forth. Optionally, the drone may be operated using on-board computers or remotely located human operators.
According to an embodiment, the drone 102 may comprise at least one sensor coupled to the server arrangement 106. Specifically, the at least one sensor may be a part of payload of the drone 102. Furthermore, the at least one sensor may be used for missions to be executed by the drone 102. In an embodiment, the at least one sensor may be one of image sensor, proximity sensor, pressure sensor, motion sensors and biosensors. It may be evident that the at least one sensor may further include, but is not limited to, radiation sensors and infrared sensors. For example, if a drone is operated for aerial inspection of a mountainous region, digital cameras comprising image sensors may be attached to the drone and coupled to a server arrangement.
Throughout the present disclosure, the term 'optimal path' may refer to a path that the drone 102 may traverse to reach destination coordinates. Specifically, the drone 102 may have a plurality of potential paths that the drone 102 may traverse to reach the destination coordinates. More specifically, an optimal path may be selected from a plurality of potential paths, wherein, in an embodiment the optimal path may be a quickest path for the drone 102 to reach the destination coordinates. In another embodiment, when traversing the optimal path, a battery consumption of the drone 102 may be reduced. Furthermore, the optimal path may present fewer numbers of obstacles to the drone 102, in comparison with the plurality of potential paths and thus increases safety of the drone 102.
The server arrangement 106 is communicably coupled to the user device
104. In an embodiment, the server arrangement 106 may be hardware, software, firmware, or a combination of these. Optionally, the server arrangement 106 may be communicably coupled to the drone 102 via a wired or a wireless connection. Specifically, the server arrangement 106 may be wirelessly communicably coupled to the drone 102 via a network. Furthermore, examples of the network include, but are not limited to, Internet®, short range radio, and cellular network. Additionally, the server arrangement 106 may control operation of the drone 102.
According to an embodiment, the user device 104 may be hardware, software, firmware, or a combination of these, configured, namely operable, to generate a request for the optimal path for the drone 102 and communicate the request to the server arrangement 106 via a wired or a wireless connection. Examples of the user device include, but are not limited to, a smart-phone, a desktop computer, a laptop computer, and a tablet computer. Furthermore, the user device 104 may be manually controlled by a user of the drone 102.
Referring to FIG. 2, there is shown an exemplary user interface 200 for determining an optimal path for a drone 102 (as shown in FIG. 1), in accordance with an embodiment of the present disclosure. As shown, the user interface 200 comprises a map of a defined area 202. The server arrangement 106 (as shown in FIG. 1) is configured, namely operable, to create a route-grid 204 having a plurality of cells, such as cells Cl to C15, associated with the defined area 202.
In an embodiment, a size of the plurality of the cells, such as the cells Cl to C15, of the route-grid 204 is based on the defined area 202. Specifically, the size of the cells of the route-grid 204 may be based on the parameters associated with the defined area. More specifically, parameters associated with the defined area may include terrain of the defined area, population of the defined area, population density of the defined area, civil structures in the defined area and so forth. In an example, a defined area with highly variable terrain may have a route grid with cells of a smaller size to maintain similar terrain properties of the cells.
The server arrangement 106 is configured, namely operable, to define origin coordinates 206 and destination coordinates 208 for the drone 102 in the route-grid 204 to obtain a plurality of potential paths, such as the paths Pl and P2, therebetween based on the user request. Specifically, the origin coordinates 206 and destination coordinates 208 for the drone may be defined in a defined area 202. Furthermore, the plurality of potential paths between the origin coordinates 206 and destination coordinates 208 may lie in the defined area 202. In an embodiment, the user of the drone 102 may define origin coordinates 206 and destination coordinates 208 for the drone using the user device 104. Specifically, the user of the drone may enter origin coordinates 206 and destination coordinates 208 using a user interface of the user device 104. Optionally, the origin coordinates 206 be automatically defined as the current location of the drone 102.
According to an embodiment, the term 'defined area'may refer to an area determined by the user and/or the server arrangement 106. Specifically, the defined area 202 may include an area surrounding the origin coordinates 206 and destination coordinates 208. More specifically, the defined area 202 may include the plurality of paths that may be implemented between origin coordinates 206 and destination coordinates 208.
The server arrangement 106 is configured, namely operable, to analyse one or more parameters associated with each of the plurality of cells, such as the cells Cl to C15, of the route-grid 204 to obtain a preference factor for each of the plurality of cells. Specifically, one or more parameters associated with each of the plurality of cells may represent conditions of a specific cell which may affect flight of the drone 102. More specifically, such conditions may include air traffic conditions, building structures in the cell and so forth. Furthermore, one or more parameters associated with each of the plurality of cells are analysed by the server arrangement 106 to obtain a preference factor of each of the plurality of cells. Specifically, the preference factor of a specific cell may represent conditions for flight of the drone in the specific cell. In an example, a relatively high preference factor of a cell may represent favourable conditions for flight of drone in the cell. In another example, a relatively low preference factor of a cell may represent unfavourable conditions for flight of drone in the cell. In yet another example, a cell C4 may have an airport 210 located therein. Therefore, in such example, the server arrangement 106 may analyse a location of the airport 210 in the cell C4; thus, analysing the location as restricted airspace for the drone and provides a relatively low preference factor of the cell C4. Subsequently, the cell C4 may be considered unfavourable for flight of drone 102 therein. Moreover, such one or more parameters may be analysed by the server arrangement 106 to obtain the preference factor of each of the plurality of cells, such as the cells Cl to C15 in the route-grid 204. Additionally, the preference factor of each of the plurality of cells may be a numerical value. Furthermore, such numerical value may be positive or negative.
In an embodiment, obtaining a preference factor for each of the plurality of cells Cl to C15 includes analysing at least one parameter of the static and/or dynamic parameters. Specifically, one or more parameters such as weather or terrain may not be taken into consideration while obtaining a preference factor for a specific cell or a specific path or the drone. Optionally, the user of the drone 106 may selectively define the parameters to be analysed to obtain a preference factor of each of the plurality of cells. In an example, the user of the drone 106 may selectively exclude weather conditions from the analysis of one or more parameters to obtain a preference factor for each of the plurality of cells. Such selection may be based upon the robustness and strength of the drone, as the drone may endure adverse weather conditions without significant harm thereto. In another example, the drone may be in need of ready access to a landing site (e.g., due to unpredictable weather). In such an example, the user may selectively choose a safest path for the drone irrespective of the duration of flight for the drone on such path.
Optionally, the one or parameters associated with each of the plurality of cells include static and dynamic parameters. In such an instance, the one or more parameters relate to static (namely, fixed) attributes and dynamic (namely, variable) attributes pertaining to the plurality of cells, which affect flight of the drone within such plurality of cells. In other words, the one or more parameters define conditions that the drone would be subjected to whilst moving within the plurality of cells.
Optionally, the one or more parameters associated with the plurality of cells are mutually similar, namely are mutually the same. Alternatively, optionally, the one or more parameters associated with the plurality of cells are mutually different.
Furthermore, optionally, the static parameters include at least one of: terrain, civil structures, communication structures. It will be appreciated that attributes such as terrain, civil structures and communication structures associated with the plurality of cells, are often static (namely, invariant) with respect to time. In other words, such static parameters may be understood to be consistently (namely, at all times) associated with the plurality of cells, and consequently, analysis of such static parameters is highly relevant for accurately determining the preference factors for each of the plurality of cells. In an example, the terrain associated with the plurality of cells may be a grassy flatland, an uncultivated plain, a plateau, hilly (namely, mountainous), a coastal plain, an oceanic region, a river land region, and so forth. In another example, the civil structures associated with the plurality of cells include roads, buildings, bridges, dams, canals, and so forth. Furthermore, the static parameters may include locations of civil structures and associated heights thereof. Additionally, the civil structures may include airports, and the area associated therewith may be considered as restricted airspace. Therefore, locations of civil structures in a cell may negatively affect a corresponding preference factor of the cell. In yet another example, the communication structures associated with the plurality of cells include cellular network towers, radio masts and towers, and so forth. Therefore, a location of a given communication structure in a given cell may positively affect a preference factor of the given cell and cells nearby such a given cell. Optionally, a distance of each of the plurality of cells in the route-grid 204 from the origin and/or destination coordinates may be analysed to obtain the preference factor of the cell. In an example, a preference factor of a cell farther to the origin and/or destination coordinates may have a lower preference factor in comparison with a cell closer to the origin and/or destination coordinates.
As an example, a static parameter associated with a cell C14 of the route-grid 204 may include an uncultivated plain terrain of the cell C14 and a communication structure within the cell C14. Furthermore, in such an example, a static parameter associated with another cell C6 of the route-grid 204 may include the uncultivated plain terrain of the cell C6. In such an example, preference factors R.1 and R.2 may be obtained for each of the cells C14 and C6 respectively, based upon analysis of the static parameters associated with the cells C14 and C6. For example, the preference factor R.1 for the cell C14 may be higher (namely, more positive) with respect to the preference factor R2 for the cell C6, since a presence of the radio tower within the cell C14 may be useful for the drone in order to maintain strong communication links with its command and control centre.
As another example, a static parameter associated with a cell C2 of a route-grid 204 may include an uncultivated plain terrain of the cell C2 and a plurality of high-rise buildings within the cell C2. Furthermore, in such an example, a static parameter associated with another cell CIO of the route-grid 204 may include the uncultivated plain terrain of the cell CIO and a plurality of low-rise buildings within the cell CIO. In such an example, preference factors QI and Q2 may be obtained for each of the cells C2 and CIO respectively, based upon analysis of the static parameters associated with the cells C2 and CIO. For example, the preference factor QI for the cell C2 may be lower (namely, more negative) with respect to the preference factor Q2 for the cell CIO, since the cell C2 may be comparatively more urbanised with respect to the cell CIO and the cell C2 includes more obstructions to a path of the drone 102 as compared to the cell CIO.
Moreover, optionally, the dynamic parameters include at least one: air traffic, weather. It will be appreciated that attributes such as air traffic and weather are often dynamic (namely, variable) with respect to time. In other words, such dynamic parameters may be understood to be variably associated with the plurality of cells, and consequently, analysis of such dynamic parameters is highly relevant for accurately determining the preference factors for the plurality of cells at a given point of time. In an example, the air traffic associated with the plurality of cells may be clear air traffic, congested air traffic, restricted air traffic, and so forth. In such example, air traffic favourable for the flight of drone 102 such as clear air traffic may positively influence the preference factor of the cell. Furthermore, air traffic unfavourable for the flight of drone 102 such as congested air traffic and/or restricted air space may negatively affect preference factor of the cell. In another example, the weather associated with the plurality of cells may be windy, sunny, clear, rainy, stormy, snowy, and so forth. In such an example, weather conditions favourable for the flight of drone 102 such as sunny, clear weather may positively influence the preference factor of the cell. Furthermore, weather conditions unfavourable for the flight of drone 102 such as windy, stormy, rainy, snowy weather may negatively affect preference factor of the cell.
As an example, a dynamic parameter associated with a cell C13 of a route-grid 204 may include sunny weather. Furthermore, in such an example, dynamic parameters associated with another cell C7 of the route-grid 204 may include sunny and windy weather. In such an example, preference factors Fl and F2 may be obtained for each of the cells C13 and C7 respectively, based upon analysis of the dynamic parameters associated with the cells C13 and C7. For example, the preference factor Fl for the cell C13 may be higher (namely, more positive) with respect to the preference factor F2 for the cell C7, since presence of wind within the cell C7 may adversely affect trajectory of the drone therethrough. Furthermore, preference factor of a cell may inversely affect cost of flight of drone in the cell. In an example, the cost of flight of drone in a cell may be lower in a cell with a high preference factor in comparison with a cell with low preference factor. In another example, the cost of flight of drone in a cell may be higher in a cell with a low preference factor in comparison with a cell with high preference factor.
The server arrangement 106 is operable to determine a cumulative preference score for each of the plurality of paths, such as the paths Pl and P2, between the origin and the destination coordinates 206 and 208 based on the preference factor of one or more cells Cl to C15 traversed by the plurality of paths Pl and P2. Specifically, the cumulative preference score of a specific path may be a cumulative sum of the preference factors of the cell traversed by the specific path. In an example, the cumulative preference score of a path Pl is a cumulative sum of the preference factors of the cells Cl, C2, C3, C4, C5, C6, C7, C8 and C9. In another example, the cumulative preference score of a path P2 is a cumulative sum of the preference factors of the cells Cl, Cll,
C12, C13, C14, C15, C8 and C9. Furthermore, a time of flight of drone in a particular cell may influence a contribution of the preference factor of the particular cell in the cumulative preference score of a path traversing the particular cell. In an example, the contribution of the preference factor of the cell C3 with lower flight time of drone 102 therein to the cumulative preference score of the path Pl may be lower in comparison with the contribution of the preference factor of cell C2 or C4. In another example, the contribution of the preference factor of the cell CIO with higher flight time of drone 102 therein to the cumulative preference score of the path Pl may be higher in comparison with the contribution of the preference factor of cell C14 or C8.
In an implementation, the route-grid 204 may comprise a plurality of paths, such as the paths Pl and P2, in the defined area 202. In such implementation, the paths Pl and P2 start from the origin coordinates 206 and traverse a plurality of cells to reach the destination coordinates 208. Specifically, the path Pl traverses cells Cl, C2, C3, C4, C5, C6, C7, C8 and C9 to reach the destination coordinates 208. Furthermore, the path P2 traverses cells Cl, Cll, C12, C13, C14, C15, C8 and C9 to reach the destination coordinates 208. Moreover, the path Pl represents the shortest path between the origin and the destination coordinates 206 and 208. In an example, the cell C4 comprises an airport 210 therein. Furthermore, in such an example, the area comprised in the cell C6 and C7 may be experiencing heavy rainfall. Therefore, in such example, the shortest path Pl may not be an optimal path for the drone 102. Furthermore, in the implementation, the cells Cll and C14 may have a communication structure 212 and 214 therein or nearby the cell. Therefore, such a location of the communication networks 212 and 214 may positively increase the preference factors of the cells Cll and C14. Furthermore, the cells traversed by the path P2 may provide favourable weather conditions for the flight of the drone 102. Therefore, the cells traversed by the path P2 may have a high preference factor.
Consequently, the cumulative preference score of the path P2 may be high in comparison with the plurality of paths between the origin and the destination coordinates 206 and 208. Therefore, the path P2 may be an optimal path for the drone 102.
The server arrangement 106 is operable to select the optimal path from the plurality of paths based on the determined cumulative preference scores thereof. Specifically, the server arrangement may select a path with highest cumulative preference score from the plurality of paths. Furthermore, the optimal path selected by the server arrangement 106 may provide the most optional combination of all preferences for the flight therethrough.
Optionally, the server arrangement 106 is operable to employ at least one graph traversal algorithm to determine the optimal path for the drone 102. It will be appreciated that such at least one graph traversal algorithm is employed for selecting the optimal path for the drone 102 from the plurality of paths, wherein the optimal path is understood to be one which has the highest cumulative preference score among the plurality of paths. Therefore, a cost associated with traversing the optimal path would be minimum among costs associated with traversing the plurality of paths. In such an instance, the at least one graph traversal algorithm is employed to determine a path having minimum cost of traversal (namely, having maximum cumulative preference score) among the plurality of paths, and select such a path as the optimal path. Examples of the at least one graph traversal algorithm include, but are not limited to, Dijkstra algorithm, A* algorithm, Floyd-Warshall algorithm, and Bellman-Ford algorithm.
In an embodiment, the server arrangement 106 may further include a database arrangement (not shown) operable to store information associated with static and/or dynamic parameters. Specifically, the database arrangement may be hardware, software, firmware, or a combination of these, suitable for storing the information associated with static and/or dynamic parameters. More specifically, with regard to the static parameters, the database arrangement may store information such as location of civil structures (such as location of airports, height of high rises and so forth), terrain profile of uncultivated area and so forth. Furthermore, with regard to dynamic parameters, the database arrangement may store weather patterns, anticipated air traffic and so forth, which may assist real-time analysis of dynamic parameters.
Optionally, the drone 102 may be operable to gather information while in-flight using at least one sensor comprised in the drone. Specifically, such information may include vehicular traffic data, images of terrain profiles, location of civil structures and so forth. Furthermore, such information may be communicated to the server arrangement 106. Additionally, the server arrangement may employ such information for various applications such as terrestrial and land navigation of vehicles, providing information about terrain and so forth.
Referring to FIG. 3, there are illustrated steps of a method 300 for determining an optimal path for a drone in a defined area, in accordance with an embodiment of the present disclosure. At a step 302, a route-grid having a plurality of cells, associated with the defined area, is created. At a step 304, one or more parameters associated with each of the plurality of cells of the route-grid are analysed to obtain a preference factor for each of the plurality of cells. At a step 306, origin and destination coordinates for the drone in the route-grid are defined to obtain a plurality of paths therebetween wherein the pluralities of paths traverse one or more cells. Furthermore, at the step 308, a cumulative preference score for each of the plurality of paths between the origin and the destination coordinates is determined based on the preference factor of one or more cells traversed by the plurality of paths. Moreover, at a step 310, the optimal path from the plurality of paths is selected based on the determined cumulative preference scores.
The steps 302 to 310 are only illustrative and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein. For example, in the method 300, a size of the plurality of cells of the route-grid is based on the defined area. Optionally, in the method 300, the origin and destination coordinates for the drone are defined by a user of the drone. More optionally, in the method 300, the one or parameters associated with each of the plurality of cells include static and/or dynamic parameters. Furthermore, optionally, in the method 300, the static parameters include at least one of: terrain, civil structures, communication structures. Additionally, optionally, in the method 300, the dynamic parameters include at least one: air traffic, weather. Alternatively, optionally, the method 300 includes obtaining a preference factor for each of the plurality of cells, including analysing at least one parameter of the static and/or dynamic parameters. Additionally, optionally, the method 300 includes employing at least one graph traversal algorithm to determine the optimal path for the drone.
The present disclosure provides the system and the method for determining an optimal path for a drone in a defined area. The described method effectively reduces the power consumption of the drone, while inflight. Specifically, the method described herein assists to try to avoid the flight of the drone into a restricted air space. Moreover, the optimal path for the drone is determined based on the precise knowledge of various factors (for example, such as the location of the communication structures, air traffic, visibility to the public and so forth). The described method allows for a reliable network connectivity and thus the communication with navigation systems is uninterrupted. Furthermore, the present disclosure provides obstacle avoidance guidance to facilitate a smooth flight of the drone and ensure safety of the drone. Additionally, the described system enables the drone to record and communicate weather data, geographical data, traffic data from the appropriate locations, roads, fields, etc., while in-flight.
Modifications to embodiments of the invention described in the foregoing are possible without departing from the scope of the invention as defined by the accompanying claims. Expressions such as including, comprising, incorporating, consisting of, have, is used to describe and claim the present invention are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. Numerals included within parentheses in the accompanying claims are intended to assist understanding of the claims and should not be construed in any way to limit subject matter claimed by these claims.
Claims (13)
1. A method of (for) determining an optimal path for a drone in a defined area, characterized in that the method comprises:
- creating a route-grid having a plurality of cells associated with the defined area;
- analysing one or more parameters associated with each of the plurality of cells of the route-grid to obtain a preference factor for each of the plurality of cells;
- defining origin and destination coordinates for the drone in the route-grid to obtain a plurality of potential paths therebetween, wherein the pluralities of potential paths traverse one or more cells;
- determining a cumulative preference score for each of the plurality of potential paths between the origin and the destination coordinates based on the preference factor of one or more cells traversed by the plurality of potential paths; and
- selecting the optimal path from the plurality of potential paths based on the determined cumulative preference scores.
2. A method of claim 1, characterized in that a size of the plurality of cells of the route-grid is based on the defined area.
3. A method of any one of the claims 1 or 2, characterized in that the origin and destination coordinates for the drone are defined by a user of the drone.
4. A method of any one of the claims 1 to 3, characterized in that the one or parameters associated with each of the plurality of cells include static and/or dynamic parameters.
5. A method of claim 4, characterized in that the static parameters include at least one of: terrain, civil structures, communication structures.
6. A method of claim 4, characterized in that the dynamic parameters include at least one: air traffic, weather.
7. A method of any one of the claims 4 to 6, characterized in that obtaining a preference factor for each of the plurality of cells includes analysing at least one parameter of the static and/or dynamic parameters.
8. A method of any one of the preceding claims, characterized in that the method includes employing at least one graph traversal algorithm to determine the optimal path for the drone.
9. A system for determining an optimal path for a drone in a defined area, characterized in that the system comprises:
- a user device for generating a request for the optimal path; and
- a server arrangement communicably coupled to the user device, wherein the server is operable to:
- create a route-grid having a plurality of cells, associated with the defined area;
- analyse one or more parameters associated with each of the plurality of cells of the route-grid to obtain a preference factor for each of the plurality of cells;
- define origin and destination coordinates for the drone in the route-grid to obtain a plurality of potential paths therebetween based on the user request, wherein the pluralities of potential paths traverse one or more cells;
- determine a cumulative preference score for each of the plurality of potential paths between the origin and the destination coordinates based on the preference factor of one or more cells traversed by the plurality of potential paths; and
- select the optimal path from the plurality of potential paths based on the determined cumulative preference scores.
10. A system of claim 9, characterized in that a user defines the origin and destination coordinates for the drone using the user device.
11. A system of any one of the claims 9 or 10, characterized in that the one or parameters associated with each of the plurality of cells include static and/or dynamic parameters.
12. A system of claim 11, characterized in that the system further includes a database arrangement operable to store information associated with the static and/or dynamic parameters.
13. A system of any one of the claims 9 to 12, characterized in that the server arrangement is operable to employ at least one graph traversal algorithm to determine the optimal path for the drone.
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| PCT/EP2018/074808 WO2019053161A1 (en) | 2017-09-13 | 2018-09-13 | Systems and methods for operating systems |
| PCT/EP2018/074802 WO2019053157A1 (en) | 2017-09-13 | 2018-09-13 | Open economic framework and a method of operation |
| PCT/EP2018/078460 WO2019077006A1 (en) | 2017-10-18 | 2018-10-17 | System and method for determining optimal paths for drones |
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| US20200406927A1 (en) * | 2019-06-28 | 2020-12-31 | Robert Bosch Gmbh | Method for testing a vehicle system |
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| CN110852470B (en) * | 2019-09-20 | 2021-04-27 | 合肥工业大学 | Optimization method for traffic patrol task allocation under cooperation of unmanned aerial vehicle and vehicle |
| CN113168776B (en) * | 2019-10-09 | 2023-04-18 | 乐天集团股份有限公司 | Processing system, unmanned aerial vehicle and flight path determination method |
| CN111199312B (en) * | 2019-12-24 | 2023-08-22 | 达闼机器人股份有限公司 | Path planning method, path planning device, storage medium and electronic equipment |
| CN111601355B (en) * | 2020-04-09 | 2024-01-19 | 绍兴市上虞区舜兴电力有限公司 | Optimal path selection method in wireless ultraviolet light cooperation unmanned aerial vehicle formation maintenance topology |
| JP7496436B2 (en) * | 2020-04-26 | 2024-06-06 | 北京三快在線科技有限公司 | Flight route determination |
| CN112148028B (en) * | 2020-08-28 | 2022-06-14 | 合肥工业大学 | Environment monitoring method and system based on unmanned aerial vehicle shooting image |
| CN114253300B (en) * | 2021-12-03 | 2023-04-07 | 国网智能科技股份有限公司 | Unmanned aerial vehicle inspection system and method for gridding machine nest |
| CN117111629B (en) * | 2023-07-26 | 2024-05-28 | 中国人民解放军陆军工程大学 | Multi-unmanned aerial vehicle fixed time optimal control method based on self-adaptive dynamic programming |
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| WO2019077006A1 (en) | 2019-04-25 |
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