WO2023001397A1

WO2023001397A1 - Methods and apparatus for determining a uav route

Info

Publication number: WO2023001397A1
Application number: PCT/EP2021/078927
Authority: WO
Inventors: Alfonso De Jesus Perez Martinez; Miguel Angel MUÑOZ DE LA TORRE ALONSO; Rodrigo Alvarez Dominguez
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2021-07-20
Filing date: 2021-10-19
Publication date: 2023-01-26

Abstract

A method of unmanned aircraft system traffic management, UTM, for unmanned aerial vehicle, UAV, route planning, the method comprising: receiving as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage; receiving as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV; determining a UAV route based on the route planning data; and initiating transmission, to a UAV, of the determined UAV route.

Description

METHODS AND APPARATUS FOR DETERMINING A UAV ROUTE

Technical Field

Embodiments of the present disclosure relate to methods and apparatus of unmanned aircraft traffic management (UTM) for unmanned aerial vehicle (UAV) route planning, and in particular methods and apparatus for determining a UAV route based on Key Performance Indicator (KPI) information of a wireless communication network.

Background

An unmanned aerial vehicle (UAV), commonly known as a drone, may be defined as an aircraft without a human pilot on board. UAVs may be a component of an unmanned aircraft system (UAS), which includes: at least one UAV, a ground-based controller, and a system of communications between the two (e.g. UTM). The flight of UAVs may operate with various degrees of autonomy; either under remote control by a human operator or autonomously by on-board computers referred to as an autopilot.

Compared to crewed aircraft, UAVs were originally used for missions deemed inappropriate for humans. While drones originated mostly in military applications, their use is rapidly finding many more applications including aerial photography, product deliveries, agriculture, policing and surveillance, infrastructure inspections, science, and drone racing.

UTM is an air traffic management ecosystem under development for autonomously controlled operations of UASs by the federal aviation administration (FAA), the national aeronautics and space administration (NASA), other federal partner agencies, and industry. They are collaboratively exploring concepts of operation, data exchange requirements, and a supporting framework to enable multiple UAS operations beyond visual line-of-sight at altitudes under 400ft above ground level in airspace, where FAA air traffic services are not provided.

Interest in using cellular connectivity to support unmanned aerial systems (UAS) is strong, and the 3GPP ecosystem offers excellent benefits for UAS operation. Ubiquitous coverage, high reliability and quality of service (QoS), robust security, and seamless mobility are critical factors in supporting UAS command and control functions. In parallel, regulators are investigating safety & performance standards and registration & licensing programs to develop a well-functioning private and civil UAS ecosystem which can safely coexist with commercial air traffic, public and private infrastructure, and the general population.

Figure 1 is a schematic diagram illustrating a 5G architecture as defined by 3GPP. The 5G architecture illustrated in Figure 1 may be divided into a control plane (which is responsible for signalling) and a user plane (which is responsible for handling user data). The control plane may comprise: a network slice selection function (NSSF), a network exposure function (NEF), a network function repository function (NRF), a policy charging function (PCF), unified data management (UDM), an application function (AF), an access and mobility management function (AMF), an authentication server function (AUSF), and a session management function (SMF). The user plane may comprise a user equipment (UE), a radio access network (RAN), and a user plane function (UPF). In certain embodiments, the 5G architecture may not be divided into control plane functions and user plane functions. For example, a UE may have control plane logic (e.g. signalling between the UE and the AMF, as illustrated in Figure 1) as well as user plane logic. Furthermore, the RAN may have control plane logic (e.g. interfacing between the RAN and the AMF, as illustrated in Figure 1) as well as user plane logic.

Elements of the 5G architecture most relevant to supporting UAS will now be discussed.

AF

The AF may interact with the 3GPP core network and allows external parties to use an exposure application programming interface (API) which is offered by an operator of the 3GPP network.

NEF

The NEF may support different functionality and specifically supports the exposure of different APIs within and outside the 3GPP network.

UDR

The UDR may store data which is grouped into distinct collections of subscription- related information, for example: • subscription data;

• policy data;

• structured data for exposure; and

• application data.

PCF

The PCF supports a unified policy framework to govern the network behaviour. Specifically, the PCF provides policy and charging control (PCC) rules to the policy and charging enforcement function (PCEF), i.e. the SMF/UPF which enforces policy and charging decisions according to provisioned PCC rules.

The 3GPP system can provide control plane and user plane communication services for UAS. Examples of services which can be offered to the UAS ecosystem include: data services for command and control, telematics, UAS-generated data, remote identification and authorisation, enforcement, and regulation of UAS operation.

Figure 2 is a schematic diagram of an example UAV architecture in a 3GPP network, as defined in: “TS 23.754 v17.1.0 Study on supporting Unmanned Aerial Systems (UAS) connectivity, Identification and tracking” by the 3rd Generation Partnership Project (3GPP), available at: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx7sp ecificationld=3575 as of 22 June 2021.

The UAV architecture of Figure 2 may comprise two UASs (i.e. UAS1 and UAS2) and each UAS may comprise at least one UAV. Both UASs may also comprise an unmanned aerial vehicle controller (UAVC) configured to control the operation of the respective UAVs. The UAVC may be connected to the internet, rather than the 3GPP network, as illustrated by the non-networked UAVC of UAS2. The UAV architecture may further comprise an external UTM operated by UAS service suppliers (USS).

Figure 2 illustrates example communication connections that may be established in the UAV architecture, as follows:

UAV1: may interface a UAV and UAVC with the 3GPP network (i.e. 3GPP PLMN-a and/or 3GPP PLMN-b). • UAV2: may interface a third party authorised entity (TPAE) with the 3GPP network (i.e. 3GPP PLMN-a).

• UAV3: illustrates 3GPP user plane connectivity.

• UAV4: may interface the TPAE with a UAV over the 3GPP network (i.e. 3GPP PLMN-a).

• UAV5: may interface a UAV with a non-networked UAVC via the internet.

• UAV6: may interface the 3GPP system (i.e. 3GPP PLMN-a and/or i.e. 3GPP PLMN-b) with an external USS/UTM.

• UAV7: may interface a UAV with the TPAE.

• UAV8: may interface a UAV directly with the non-networked UAVC.

• UAV9: may interface a UAV or a networked UAVC with the external USS/UTM.

• U2U (UAV-to-UAV): may provide UAV to UAV communications. A key performance indicator (KPI) is a measurable value that indicates how effectively a network is serving a user. Certain KPIs of a 5G system which provide services in UAV applications are provided in Table 1, below. Table 1 is based on the table included in: TS 22.125 v17.3.0 Unmanned Aerial System (UAS) support in 3GPP; Stage 1” by the 3rd Generation Partnership Project (3GPP), available at: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx7sp ecificationld=3545 as of 22 June 21. The KPIs included in Table 1 are data rate, end- to-end latency, altitude above ground level (AGL) and service area.

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Table 1

In Table 1, UAV originated QoS refers to the QoS of uplink data (e.g. from a UAV to the network side). UAV terminated QoS refers to the QoS of downlink data (e.g. from the network side to a UAV). In certain UAV applications, different uplink and downlink QoS may be required at the same time.

The type of data transmitted by the 5G system may comprise data collected by hardware devices installed on UAVs, such as cameras which collect, for example, pictures, videos and data files. Data transmitted by the 5G system may also comprise software calculation or statistical data, e.g. UAV management data. Service control data transmitted by the 5G system may be based on application triggers, such as switch, rotation and promotion & demotion control of equipment on a UAV.

Some UAV applications may require the 5G system to simultaneously provide services to multiple users on the ground in the same area. In typical UAV systems, a UAV operator requires the mobile network operator (MNO) to provide radio coverage in geographical areas in which a UAV will fly. When planning UAV flight paths (e.g. route planning between source point A and destination point B), physical obstacles and forbidden areas (e.g. airports) are taken into consideration. However, network throughput, coverage and latency characteristics in geographical areas through which the UAV will fly (i.e. the route to be planned) are not considered. Ignoring network properties may lead to suboptimal planning (e.g. drones might have widely different requirements in terms of connectivity: one drone might need to transmit 8K video under a fire emergency, whereas other drones may only need basic connectivity).

Summary

It is an object of the present disclosure to provide methods of UAV route planning which consider KPIs when determining a UAV route.

Aspects of embodiments provide a UTM, methods and computer programs which at least partially address one or more of the challenges discussed above.

An aspect of the disclosure provides a method of unmanned aircraft system traffic management, UTM, for unmanned aerial vehicle, UAV, route planning. The method comprises receiving as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage. The method further comprises receiving as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV. The method further comprises determining a UAV route based on the route planning data. The method further comprises initiating transmission, to a UAV, of the determined UAV route.

Advantageously, by considering KPI information when determining the UAV route, the UAV route is configured such that the UAV passes through positions in the geographical area with reliable network coverage. This improves overall performance and reliability of the UAV.

Optionally, the method may comprise receiving as route planning data, from the UAV, route positions.

Receiving route positions from the UAV ensures the determined UAV route meets the route requirements of the specific UAV from which the route positions are received. This improves overall accuracy and performance.

Optionally, the route positions comprise at least one of: a UAV starting position, and a UAV destination position.

Determining the UAV route based on the UAV starting position ensures the UAV does not have to travel to a generic starting position before starting the determined UAV route. This thereby minimised the UAV travel time and improves overall efficiency. Determining the UAV route based on the UAV finishing position ensures the UAV route finishes at the exact location required by the UAV route requirements. This improves overall accuracy.

Optionally, the method may comprise determining the UAV route by identifying positions within the geographical area: located within a predetermined distance of corresponding route positions, and having corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement.

The UTM, the methods and the computer programs of the present disclosure may be used to determine the UAV route to include positions which are within the predetermined distance of the route positions and have a corresponding network performance of more than or equal to the minimum network performance. Advantageously, the UTM is thereby able to determine the UAV route which meets service requirements of the UAV while ensuring a minimum possible distance is travelled by the UAV. This improves overall efficiency and performance of the UAS.

Another aspect of the disclosure provides an unmanned aircraft system traffic manager, UTM, for unmanned aerial vehicle, UAV, route planning. The UAV comprises processing circuitry and a memory containing instructions executable by the processing circuitry. The UTM is operable to receive as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage. The UTM is further operable to receive as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV. The UTM is further operable to determine a UAV route based on the route planning data. The UTM is further operable to initiate transmission, to a UAV, of the determined UAV route.

Another aspect of the disclosure provides a computer-readable medium comprising instructions which, when executed on a computer, cause the computer to perform a method of unmanned aircraft system traffic management, UTM, for unmanned aerial vehicle, UAV, route planning. Further aspects provide apparatuses and computer-readable media comprising instructions for performing the methods set out above, which may provide equivalent benefits to those set out above.

Brief Description of Drawings

For a better understanding of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Figure 1 is a schematic diagram of a 3GPP 5G architecture;

Figure 2 is a schematic diagram of a UAV architecture in a 3GPP network;

Figure 3 is a flowchart illustrating a method of determining a UAV route;

Figure 4A is a schematic diagram of a UTM for determining a UAV route;

Figure 4B is another schematic diagram of a UTM for determining a UAV route; and

Figures 5A and 5B are flowcharts illustrating a route planning method according to certain embodiments.

Detailed Description

The following sets forth specific details, such as particular embodiments for purposes of explanation and not limitation. It will be appreciated by one skilled in the art that other embodiments may be employed apart from these specific details. In some instances, detailed descriptions of well-known methods, nodes, interfaces, circuits, and devices are omitted so as not obscure the description with unnecessary detail. Those skilled in the art will appreciate that the functions described may be implemented in one or more nodes using hardware circuitry (e.g., analog and/or discrete logic gates interconnected to perform a specialized function, ASICs, PLAs, etc.) and/or using software programs and data in conjunction with one or more digital microprocessors or general purpose computers that are specially adapted to carry out the processing disclosed herein, based on the execution of such programs. Nodes that communicate using the air interface also have suitable radio communications circuitry. Moreover, the technology can additionally be considered to be embodied entirely within any form of computer-readable memory, such as solid- state memory, magnetic disk, or optical disk containing an appropriate set of computer instructions that would cause a processor to carry out the techniques described herein.

Hardware implementation may include or encompass, without limitation, digital signal processor (DSP) hardware, a reduced instruction set processor, hardware (e.g., digital or analog) circuitry including but not limited to application specific integrated circuit(s) (ASIC) and/or field programmable gate array(s) (FPGA(s)), and (where appropriate) state machines capable of performing such functions.

In terms of computer implementation, a computer is generally understood to comprise one or more processors, one or more processing modules or one or more controllers, and the terms computer, processor, processing module and controller may be employed interchangeably. When provided by a computer, processor, or controller, the functions may be provided by a single dedicated computer or processor or controller, by a single shared computer or processor or controller, or by a plurality of individual computers or processors or controllers, some of which may be shared or distributed. Moreover, the term “processor” or “controller” also refers to other hardware capable of performing such functions and/or executing software, such as the example hardware recited above. Embodiments of the present disclosure provide methods of planning an optimal UAV route based on KPI information (e.g. network performance indicators) and KPI requirements of a UAV. That is, the methods provide a means for allowing a network operator to expose (e.g. via an API) KPIs of a MNO (e.g. coverage, capacity, latency) in order to allow external parties (e.g. a UTM or a UAV controller acting as an AF) to plan for the optimal UAV route based on the UAV communication requirements (e.g. transmit 8K video).

In certain embodiments, the UTM may alternatively be referred to as an unmanned aircraft traffic manager.

Figure 3 is a flowchart illustrating UAV route planning performed by a UTM. In particular, the UAV route planning method receives route planning data, determines an optimum UAV route based on the route planning data, and initiates transmission of the optimal UAV route.

Figure 4A and 4B show UTMs 400A and 400B in accordance with certain embodiments. The UTMs 400A and 400B may perform the method of Figure 3. In order that a UAV route can be determined by the UTM 400A, 400B, route planning data is received by the UTM 400A, 400B. Route planning data may be received from at least one of: the wireless communication network, a single UAV, plural UAVs, and a third party, such as a security system.

The wireless communication network may be a 3GPP network such as a 3GPP 5G network (i.e. a 5^th generation new radio (5G NR) network), as described above in relation to Figure 2. The wireless communication network may provide radio communication between: the UTM 400A, 400B located in a geographical area, one or more UAVs operating (i.e. flying through) the geographical area, and one or more external databases accessed via APIs. The APIs may be, for example, MNO APIs which are exposed to provide information about: UAV restricted routes in the geographical area, topographical data of the geographical area and/or KPI information of the geographical area. For ease of explanation, the wireless communication network will be referred to as the network hereafter.

In step S302, KPI information is received as route planning data at the UTM 400A, 400B from the wireless communication network. The KPI information may comprise network performance indicators corresponding to positions within the geographical area for which the wireless communication network provides coverage. Receiving the KPI information may be performed, for example, by the processor 402 of the UTM 400A running a program stored on the memory 404 in conjunction with the interfaces 406, or may be performed by a receiver 452 of the UTM 400B.

A network performance indicator may be a value representing a certain performance metric of the wireless communication network. Network performance indicators may be measured empirically at geographical locations or taken from a database of known values for certain operating conditions. Examples of network performance indicators relevant to UAV applications include:

Network Coverage Data: for example, a value indicating the geographical area covered by the network. The value may be, for example, a distance in square kilometres (km²) in relation to a reference location.

Network Throughput Data: for example, a value indicating the rate at which the network processes signalling (e.g. in kilobits per second (kbps)).

Network Capacity Data: for example, a value indicating the amount of traffic the network can handle at any given time (e.g. in kbps).

Network Latency Data: for example, a value indicating a delay in the network (i.e. the duration of time taken for data to be transmitted across the network). The value may be, for example, a time in milliseconds (ms).

Guaranteed Network Bit Rate: for example, a value indicating a minimum number of bits that will consistently be processed by the network per unit of time (i.e. the rate at which bits are transferred across the network in a given amount of time). The value may be given in, for example, kbps.

Maximum Available Network Bit Rate: for example, a value indicating the maximum number of bits that may be consistently processed by the network per time unit (i.e. the maximum rate at which bits may be transferred across the network in a given amount of time). The value may be given in, for example, kbps.

It will be understood that each network performance indicator may indicate a geographical area/location, for example using two or three dimensional coordinates, as discussed in more detail below (e.g. a certain geographical area/location (e.g. geo coordinate) may have a maximum latency of 10ms).

Additionally or alternatively, the network performance indicators may be a Boolean value (e.g. coverage = YES/NO or TRUE/FALSE for a certain geographical area/location), a binary value (e.g. coverage = 1/0 for a certain geographical area/location) and/or a general indicator value (e.g. no coverage/bad coverage/medium coverage/high coverage for each geographical area/location (e.g. geo coordinate)).

It will be understood that embodiments are not limited to the above example network performance indicators, and that the KPI information may comprise additional network performance indicators.

The KPI information may represent the network performance indicators according to a variety of different methods. For example, the network performance indicators may be represented in a database (e.g. in the UDR) which associates each network performance indicator with a corresponding position (i.e. positions defined by two or three dimensional coordinates, as discussed in more detail below). Access to the database may be facilitated using an exposure API thereby providing a means for the UTM 400A, 400B to access network performance indicators that are relevant for UAV route planning.

The UTM 400A, 400B may receive the KPI information automatically (e.g. according to a predetermined frequency). The UTM 400A, 400B may additionally or alternatively request KPI information from the wireless network (for example, by using an exposure API).

According to certain embodiments, the KPI information is a two or three dimensional map configured to represent network performance indicators at the positions (i.e. a 3D heat map). For example, the map may represent the geographical area for which the wireless communication network provides coverage, and the map may comprise the positions corresponding to the network performance indicators.

The map may indicate the positions in such a manner so as to indicate a quality of one or more network performance indicators corresponding to each position, according to a predefined scale. The scale may be, for example, a sliding scale of 1 to 10 with 1 indicating poor network performance indicator(s) and 10 indicating good network performance indicator(s). Alternatively or additionally, the scale may be a colour scale with green indicating good network performance indicator(s), amber indicating average network performance indicator(s) and red indicating poor network performance indicator(s). For example, in certain embodiments, if the network performance indicators corresponding to a certain position indicate a high network throughput and a low network latency, the position may be represented with a green colour and/or a relatively high score (e.g. 9/10) in the map.

It will be understood that the map may be used to indicate single or plural network performance indicators at each position. For example, in embodiments where each position has plural corresponding network performance indicators, the map may indicate an average of all available network performance indicators associated with each position (i.e. in the form of a scale, as discussed above).

According to certain embodiments, the KPI information may define a range of coordinates within the geographical area and the KPI information may indicate at least one network performance indicator between the range of coordinates (i.e. indicate at least one network performance indicator that remains consistent between the range of coordinates). For example, the KPI information may define a lower altitude value and an upper altitude value between which the network performance indicators are a certain quality (e.g. a data rate of 100 kbps at an altitude between sea level (i.e. 0 meters) and 100 meters; a data rate of 50kbps at an altitude between 100 meters and 200 meters; a data rate of 50 kbps at an altitude between 100 meters and 200 meters; there is no coverage (i.e. 0 kbps) at an altitude above 200 meters).

The positions within the geographical area may be (geographical) positions defined by two dimensional coordinates within the geographical area for which the wireless communication network provides coverage. Alternatively, the positions within the geographical area may be (geographical) positions defined by three dimensional coordinates within the geographical area for which the wireless communication network provides coverage. The coordinates may define at least two of longitude, latitude and altitude. For example, two dimensional coordinates may define longitude and latitude, and three dimensional coordinates may define longitude, latitude and altitude. In embodiments where the network performance indicators are represented in the form of a three dimensional map, the positions may be represented in two or three dimensions. In embodiments, where the network performance indicators are represented in the form of a two dimensional map, the positions may be represented in two dimensions only.

The distribution of positions within the geographical area may be defined by global positioning system (GPS) technology. For example, each position may cover an area of one meter squares, 10 meters squared or 1 kilometer squared within the geographical area.

Each position within the geographical area may have a single corresponding network performance indicator. Alternatively, each position within the geographical area may have a plurality of corresponding network performance indicators. For example, the KPI information may comprise one or more network performance indicators for each position.

The two or three dimensional coordinates which define the positions in the geographical area may each correspond to different one or more network performance indicators.

The geographical area may be defined as an area within which the wireless communication network can reliably provide wireless radio communication between the UTM 400A, 400B, the one or more UAVs and the MNO APIs. The geographical area may be a two dimensional area of space defined by two dimensional coordinates. Alternatively, the geographical area may by a three dimensional volume of space defined by three dimensional coordinates. Where the geographical area is defined by three dimensional coordinates, the geographical area may also be referred to as a geographical volume.

In step 304, a KPI requirement is received as route planning data at the UTM 400A, 400B from a UAV. The KPI requirement defines a minimum network performance required by the UAV. The minimum network performance required by the UAV may be defined by the intended service of the UAV. In certain embodiments, the KPI requirement may define at least one of: a minimum data rate required by the UAV, and a maximum end to end latency required by the UAV The intended service may be provided to the UTM 400A, 400B as part of the KPI requirement (i.e. the KPI requirement may define the intended service of the UAV). That is, if a specific KPI performance value (e.g. a specific data rate and/or a specific end-to-end latency) of the wireless communication network is required in order for the intended service to be performed at an acceptable quality, the UAV may calculate the minimum network performance based on the specific KPI performance value (e.g. minimum required data rate and/or maximum end-to-end latency required to perform the intended service). The calculated minimum network performance is subsequently transmitted to the UTM 400A, 400B as route planning data as part of the KPI requirement.

Alternatively, the intended service may be already known to the UTM 400A, 400B (e.g. based on previously acquired information about the UAV).

The KPI requirement may define the minimum network performance as a single value based on a single specific KPI performance value required by the UAV, or as plural values each corresponding to a different specific KPI performance value required by the UAV. Alternatively, the KPI requirement may be an average KPI performance value calculated by averaging plural specific KPI performance values required by the UAV. Receiving the KPI requirement may be performed, for example, by the processor 402 of the UTM 400A running a program stored on the memory 404 in conjunction with the interfaces 406, or may be performed by a receiver 452 of the UTM 400B.

It will be understood that the receiver 452 may be a single receiver configured to receive the KPI information and the KPI requirement as well as any further route planning data. Alternatively, the receiver 453 may comprise two separate receivers, wherein a first receiver is configured to receive the KPI information, a second receiver is configured to receiver the KPI requirement, and either receiver may be configured to receive further route planning data.

It will be understood that the use of “further” in “further route planning data” is used merely to indicate route planning data that is received after the initial instance of route planning data is received by the UTM 400A, 400B. The word “further” imposes no other limitation on the route planning data. According to certain embodiments, the UTM 400A, 400B may receive route positions from the UAV as further route planning data. The route positions may comprise a UAV starting position and/or a UAV destination position. For example, in embodiments where the UTM 400A, 400B already knows the UAV destination position (e.g. based on instructions received from a third party), only the UAV starting position is required by the UTM 400A, 400B in order to determine the UAV route. Alternatively, the UTM 400A, 400B may already know the UAV start position (e.g. based on previous UAV information required from the UAV), in which case only the UAV destination position is required by the UTM 400A, 400B in order to determine the UAV route. In some embodiments, the UTM 400A, 400B may already know the UAV start position and the UAV destination position, in which case the UAV is not required to provide route positions to the UTM 400A, 400B (i.e. because the UTM has already acquired the route positions).

In some embodiments, the route positions may comprise one or more UAV intermediate positions indicating intermediate position(s) through which the UAV is required to travel between the UAV start position and the UAV destination position (for example, to avoid certain locations, to collect a package and/or to deliver a package).

In step 306, the UAV route is determined by the UTM 400A, 400B based on the route planning data received by the UTM 400A, 400B. The UAV route may be determined by identifying positions within the geographical area which:

(a) are located within a predetermined distance of corresponding route positions, and

(b) have corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement.

It will be understood that the UTM 400A, 400B may determine a different UAV route for each of a plurality of different UAVs according to the steps described above and below.

Identifying positions within the geographical area that correspond to route positions may be performed by the UTM 400A, 400B using satellite navigation technology, such as GPS. For example, the UTM 400A, 400B may highlight the route positions on a GPS map before identifying positions within the geographical area which correspond to the route positions. In embodiments where the route positions comprise the UAV start position and the UAV destination position, the UTM 400A, 400B may highlight both of these positions on the GPS map before identify two positions within the geographical area that correspond to the UAV start position and the UAV destination position and meet above requirements (a) and (b).

The first step in determining the UAV route is identifying positions within the geographical area that are within the predetermined distance (i.e. step (a), above). The predetermined distance may be a radius from the corresponding route position within which the UAV is considered to be still following the determined UAV route (e.g. a radius of 10 meters, 100 meters of 1 kilometer). If the UTM 400A, 400B identifies positions within the geographical area that correspond to the route positions but exceed the predetermined distance, these positions may be disregarded for determining the UAV route, because they may cause the UAV to deviate too far from an optimum UAV route.

The second step in determining the UAV route (i.e. step (b), above) is identifying positions within the geographical area having corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement (in addition to being within the predetermined distance). The corresponding network performance indicators are acquired by the UTM 400A, 400B from the received KPI information. The network performance may indicate the performance of the wireless communication network at a corresponding position within the geographical area. The network performance may be calculated from the corresponding one or more network performance indicators for each position (e.g. as an average where the position has plural corresponding network performance indicators).

The UTM 400A, 400B may determine if the network performance indicator of a position is more than or equal to the minimum network performance defined by the KPI requirement by comparing a network performance indicator value against a minimum network performance value. Determining the UAV route may be performed, for example, by the processor 402 of the UTM 400A running a program stored on the memory 404 in conjunction with the interfaces 406, or may be performed by a determiner 454 of the UTM 400B. Advantageously, by determining the UAV route to include positions which are within the predetermined distance of the route positions and have a corresponding network performance of more than or equal to the minimum network performance, the UTM is able to determine the UAV route which meets service requirements of the UAV while ensuring a minimum possible distance is travelled by the UAV. This improves overall efficiency and performance of the UAS.

According to certain embodiments, the UTM 400A, 400B may receive UAV restriction information as further route planning data from at least one of the wireless communication network and a security system. The UAV restriction information may indicate a UAV restricted area within the geographical area and the UTM 400A, 400B may utilise the UAV restriction information in determining the UAV route.

The UAV restriction information may indicate one or more areas within the geographical area through which the UAV should not travel (i.e. to ensure that the UAV route does not intersect with UAV restricted area(s)) due to security rules, safety rules and/or any other aviation rules. For example, the UAV restriction information may indicate airports, commercial and domestic flight paths, mountain ranges, buildings over a certain height (e.g. 250 meters), other conflicting UAV routes, UAV traffic and/or areas to which access is restricted for the general public.

The UTM 400A, 400B may utilise the UAV restriction information by identifying positions of the UAV route within the geographical area which:

(a) are located within a predetermined distance of corresponding route positions,

(b) have corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement, and

(c) are not located within the UAV restricted area.

The security system may be a third party (external) security system configured to provide the UTM 400A, 400B with UAV restriction information in addition to or as well as the wireless communication network. The security system may have a dedicated secure wireless communicating link with the UTM 400A, 400B for providing the UAV restriction information. In certain embodiments, the UAV restriction information comprises topographical data corresponding to three dimensional coordinates within the geographical area. That is, the UAV restriction information may represent a topography of the restricted area(s) at three dimensional coordinates within the geographical area. The topographical data may be a topographical map and/or topographical values representing ground elevation and ground contours at the three dimensional coordinates.

In some embodiments, the UTM 400A, 400B may receive updated KPI information as further route planning data from the wireless communication network. The updated KPI information is received after the KPI information received during S302, described above. The updated KPI information may indicate updated network performance indicators and the UTM 400A, 400B may utilise the updated network performance indicators in determining the UAV route. The updated network performance indicators may corresponding to positions within the geographical area and may indicate any changes to network performance compared to the network performance indicators received by the UTM 400A, 400B at S302.

The UTM 400A, 400B may utilise the updated network performance indicators by identifying positions of an updated UAV route within the geographical area which:

(b) have corresponding updated network performance indicators that indicate an updated network performance of more than or equal to the minimum network performance defined by the KPI requirement, and

(c) are not located within the UAV restricted area.

Advantageously, by determining the updated UAV route using the updated network performance indicators, the UTM 400A, 400B ensures the service requirements of the UAV are always met (by taking into account changes to the operating environment and conditions of the wireless communication network) while ensuring the minimum possible distance is travelled by the UAV. This improves overall efficiency and performance of the UAS.

According to some embodiments, the UTM 400A, 400B may receive recurring updated KPI information as further route planning date from the wireless communication system. The recurring updated KPI information may be received periodically (e.g. every minute or hour after the UAV route has been determined). Alternatively, the recurring updated KPI information may be transmitted to the UTM 400A, 400B from the wireless communication network if one or more of the network performance indicators change and/or fall to below a minimum threshold.

Each recurring updated KPI information may indicate updated network performance indicators. That is, each of the recurring updated KPI information may comprise at least one updated network performance indicator that is different to the updated network performance indicators comprised in preceding recurring updated KPI information.

In step S308, the UTM 400A, 400B initiates transmission of the UAV route. The UAV route may be transmitted directly from the UTM 400A, 400B to the UAV (for example, to the UAVC). Alternatively, the UAV route may be transmitted to a third party server via the wireless communication network before being forward to the UAV by the third party server. Initiating transmission of the UAV route may be performed, for example, by the processor 402 of the UTM 400A running a program stored on the memory 404 in conjunction with the interfaces 406, or may be performed by an initiator 456 of the UTM 400B.

Embodiments of the method for UAV route planning by a UTM 400A, 400B will not be discussed with references to Figures 5A and 5B.

Figures 5A and 5B illustrate a method based on the definition of a new API for exposure of an MNO^'s 3D heat map (i.e. KPI information) which represents certain KPIs (i.e. network performance indicators), such as coverage, capacity, latency, etc. This method provides a means for external parties (e.g. UTM or UAVC, acting as an AF) to determine an optimal UAV route based on the UAV communication requirements (i.e. KPI requirements), such as: transmit 8K video.

In the UAV route planning method of Figures 5A and 5B, the following assumptions are made:

• a UTM (and optionally a UAVC) has access to a topographic map (i.e. the topographical data of the UAV restriction information), which may be previously obtained from local government administration and stored in the form if a 3D map to indicate obstacles and/or forbidden areas such as airports; and • the MNO provides access to a network map (e.g. stored in the UDR in the form of a 3D heat map on a per KPI basis), where a KPI may be: coverage, throughput, latency, etc. This network map might be a coverage and capacity map corresponding to radio planning based on deployed RAN sites deployed, antenna transmission power, antenna tilt, etc.

The steps for determining a UAV route according to the methods of Figures 5A and 5B are as follows:

• the UTM subscribes to receive and/or requests access to the MNO’s network map for certain one or more KPIs (e.g. coverage, throughput, latency, etc.);

• the UTM then correlates the MNO’s network map with the topographical map;

• the UAV or the UAVC plans a route (from A to B) and requests the UTM to validate the planned route for a certain UAV including, as inputs, a (new) set of KPIs (e.g. requested quality for 8K video).

• the UTM validates the planned route based both on the topographic map and the MNO^'s network map, while also taking into account the potential number of UAVs in the route, and returns either OK/NOK (in the latter case of returning NOK, the UTM may propose alternate routes which meet the UAV communication requirements).

The steps illustrated in the method of Figures 5A and 5B will now be described. Certain abbreviations are used for different interfaces in the following description, each of which will now be described in more detail:

• Nudr: the Nudr interface is used by the 5G architecture (e.g. the network functions UDM, PCF and NEF) to access a particular set of the data stored in the UDR;

• Nausf: the Nausf interface is used by the 5G architecture (i.e. the network functions) to access a service offered by the AUSF;

• Nudm: the Nudm interface is used by the 5G architecture (i.e. the network functions) to access a service offered by the UDM;

• Nutm: the Nutm interface is used by the 5G architecture (i.e. the network functions) to access a service offered by the UTM.

In the first sequence illustrated by steps 1 to 4 of Figure 5A (i.e. between “UTM subscription to MNO 3D map” and “UDR sends latest MNO 3D map periodically”), the UTM subscribes to receive the network map from the MNO(s). The UTM may subscribe to receive plural different network maps from plural different MNOs. The network map(s) is/are dynamic map(s) that change constantly depending on a number of flying UAVs and corresponding requested QoS/KPIs. Anytime that a new UAV starts a new flight, the network map(s) change to represent network usage from the new UAV through its flight path.

Step 1: the UTM logs in to a corresponding MNO NEF to request a subscription to receive the MNO network map ([1] Nudr_MNO_subscribe in Figure 5A);

According to certain embodiments, a new API (or service)is defined and exposed, from the MNO to the UTM (acting as external AF), which allows the UTM to request/subscribe to the MNO network map. As the UTM is external to the MNO, the request/subscribe may go through the MNO^'s NEF. For example, the MNO network map may be stored at the UDR (into a new data structure, e.g. as network map information).

Step 2: the NEF forwards the subscription request to the UDR ([2] Nudr_MNO_subscribe in Figure 5A);

Step 3: the UDR accepts the UTM subscription and starts sending network map changes to the UTM by NEF ([3] Nudr_MNO_subscribe response in Figure 5A);

Step 4: the NEF forwards the subscription acceptance to the UTM ([4] Nudr_MNO_subscribe response in Figure 5A);

In the second sequence illustrated by steps 5 and 6 in Figure 5A (i.e. between “UDR sends latest MNO 3D map periodically” and “UAV session”), the UDR detects changes to the network map, such as: a new drone flight paths, a new forbidden area (e.g. airport), degradation/improvement of radio conditions (e.g. overload, new deployments).

Step 5: whenever there is a map change, the UDR notifies the UTM subscribed to receive the network map of the change. The UDR may also notify additional UTMs which are also subscribed to receive the network map ([5] Nudr_MNO_3dmap_update in Figure 5A); Step 6: the NEF forwards the new network map to the UTM ([6] Nudr_MNO_3dmap_update in Figure 5A);

In the third sequence illustrated by steps 7 to 12 in Figures 5A and 5B, a UAV will start a flight. It is noted, the UAV must first register with the 5G network (i.e. the wireless communication network) before accessing an optimum UAV route determined by the UTM. Once a desired UAV route is generated by the UAV (usually from a UAVC), the UAV requests permission to take off from the UTM. The UTM authorises the desired UAV route according to the optimum UAV route determined by the UTM based on the network map from the MNO and the topographical map.

In the below description of step 7 to 12, the following acronyms are used: subscription permanent identifier (SUPI), subscription concealed identifier (SUCI) and serving network name (SNN).

Step 7: the UAV is powered on and requests authentication to access the MNO network from the AUSF

([7] Nausf_UEAuthentication_Authenticate_Request(SUCI/SUPI,SNN) in Figure 5A);

Step 8: the AUSF requests UAV authorisation from the UDM ([8] Nudm_UEAuthentication_GetRequest(SUCI/SUPI,SNN) in Figure 5A);

Step 9: the UDM sends an UAV authorisation response to the AUSF ([9] Nudm_UEAuthentication_GetResponse in Figure 5A);

Step 10: the AUSF either authorises or rejects UAV communication using the 5G Network ([10] Nausf_UEAuthentication_Authenticate_Response in Figure 5B);

Step 11: if the UAV is authorised, the UAV generates a desired UAV route and requests permission to take off and to start the desired UAV route from the UTM. The desired UAV route may be previously loaded from the UAVC or the desired UAV route may be generated after the UAV has established a connection with the 5G network . Assuming service based procedures as an example, the UAV triggers a Permission Takeoff request message towards the UTM

([11] Nutm_Permission_Takeoff_request(Certificate,3DRoute,qualityKPIs) in Figure 5B). Step 11 comprises the following procedures: 11a: certification - the UAV provides security certification to demonstrate that it is operating under necessary regulations and requirements,

11b: desired UAV route (3D route) - generation of the desired UAV route by the UAVC, the route comprising at least: origin, destination, speed, altitude, cargo.

11 c: KPIs for intended service - the KPIs may comprise at least one of: downlink requirements (DL), uplink requirements (UL), minimum/maximum/guaranteed bitrate, network measurements and minimum delay.

The final step requires the UTM to check the UAV MNO and send the optimum UAV route to the UAV base on the latest network map from the MNO.

Step 12: the UTM responds to the UAV with the optimum UAV route (i.e. final flight path defined by GPS positions). The optimum UAV route defines where the UAV can fly to guarantee a minimum service quality (i.e. minimum network performance). The UAV maintains connectivity with the UTM in order to provide future optimum UAV route changes based on network map and/or topographic map changes ([12] Nutm_Permission_Takeoff_response(3droute) in Figure 5B).

It will be understood that the detailed examples outlined above are merely examples. According to embodiments herein, the steps may be presented in a different order to that described herein. Furthermore, additional steps may be incorporated in the method that are not explicitly recited above. For the avoidance of doubt, the scope of protection is defined by the claims.

Claims

1. A method of unmanned aircraft system traffic management, UTM, for unmanned aerial vehicle, UAV, route planning, the method comprising: receiving as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage; receiving as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV; determining a UAV route based on the route planning data; and initiating transmission, to a UAV, of the determined UAV route.

2. The method according to claim 1, the method further comprising: receiving as route planning data, from the UAV, route positions.

3. The method according to claim 2, wherein the route positions comprise at least one of: a UAV starting position, and a UAV destination position.

4. The method according to claim 2 or claim 3, wherein determining the UAV route comprises identifying positions within the geographical area: located within a predetermined distance of corresponding route positions, and having corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement.

5. The method according to any preceding claim, the method further comprising: receiving as route planning data, from the wireless communication network, updated KPI information indicating updated network performance indicators and utilising the updated network performance indicators in determining the UAV route.

6. The method according to claim 5, the method further comprising: receiving, from the wireless communication network, recurring updated KPI information, each updated KPI information indicating at least one different updated network performance indicator.

7. The method according to any preceding claim, the method further comprising: receiving as route planning data, from at least one of the wireless communication network and a security system, UAV restriction information indicating a UAV restricted area within the geographical area and utilising the UAV restriction information in determining the UAV route.

8. The method according to claim 7, wherein the UAV restriction information is utilised such that the UAV route does not intersect with the UAV restricted area.

9. The method according to claim 7 or claim 8, wherein the UAV restriction information comprises topographical data corresponding to three dimensional coordinates within the geographical area.

10. The method according to any preceding claim, wherein the network performance indicators of the KPI information comprise at least one of: network coverage data, network throughput data, network capacity data, network latency data, guaranteed network bit rate, and maximum available network bit rate.

11. The method according to any preceding claim, wherein the network performance indicators correspond to either two dimensional coordinates which define positions within the geographical area or three dimensional coordinates which define positions within the geographical area.

12. The method according to claim 11, wherein the KPI information is a two dimensional map configured to represent network performance indicators at the two or three dimensional coordinates.

13. The method according to claim 11, wherein the KPI information is a three dimensional map configured to represent network performance indicators at the two or three dimensional coordinates.

14. The method according to claim 11, wherein the KPI information defines a range of coordinates and indicates at least one network performance indicator between the range of coordinates.

15. The method according to any preceding claim, wherein the KPI requirement defines at least one of: a minimum data rate required by the UAV, and a maximum end-to-end latency required by the UAV.

16. The method according to any preceding claim, wherein the KPI requirement defines an intended service of the UAV, and wherein the method further comprises: calculating, by the UTM, a minimum data rate and/or a maximum end to end latency required to perform the intended service.

17. The method according to any preceding claim, the method further comprising: requesting, from the wireless communication network, KPI information.

18. An unmanned aircraft system traffic manager, UTM, for unmanned aerial vehicle, UAV, route planning, the UAV comprising processing circuitry and a memory containing instructions executable by the processing circuitry, whereby the UTM is operable to: receive as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage; receive as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV; determine a UAV route based on the route planning data; and initiate transmission, to a UAV, of the determined UAV route.

19. The UTM according to claim 18, wherein the UTM is further operable to: receiving as route planning data, from the UAV, route positions.

20. The UTM according to claim 19, wherein the route positions comprise at least one of: a UAV starting position, and a UAV destination position.

21. The UTM according to claim 19 or claim 20, wherein the UTM is further operable to determine the UAV route by identifying positions within the geographical area: located within a predetermined distance of corresponding route positions, and having corresponding network performance indicators that indicate a network performance of more than or equal to the minimum network performance defined by the KPI requirement.

22. The UTM according to any of claims 18 to 21, wherein the UTM is further operable to: receive as further route planning data, from the wireless communication network, updated KPI information indicating updated network performance indicators and utilising the updated network performance indicators in determining the UAV route.

23. The UTM according to claim 22, wherein the UTM is further operable to: receive, from the wireless communication network, recurring updated KPI information, each updated KPI information indicating at least one different updated network performance indicator.

24. The UTM according to any of claims 18 to 23, wherein the UTM is further operable to: receive as route planning data, from at least one of the wireless communication network and a security system, UAV restriction information indicating a UAV restricted area within the geographical area and utilising the UAV restriction information in determining the UAV route.

25. The UTM according to claim 24, wherein the UAV restriction information is utilised such that the UAV route does not intersect with the UAV restricted area.

26. The UTM according to claim 24 or claim 25, wherein the UAV restriction information comprises topographical data corresponding to three dimensional coordinates within the geographical area.

27. The UTM according to any of claims 18 to 26, wherein the network performance indicators of the KPI information comprise at least one of: network coverage data, network throughput data, network capacity data, network latency data, guaranteed network bit rate, and maximum available network bit rate.

28. The UTM according to any of claims 18 to 27, wherein the network performance indicators correspond to either two dimensional coordinates which define positions within the geographical area or three dimensional coordinates which define positions within the geographical area.

29. The UTM according to claim 28, wherein the KPI information is a two dimensional map configured to represent network performance indicators at the two or three dimensional coordinates.

30. The UTM according to claim 28, wherein the KPI information is a three dimensional map configured to represent network performance indicators at the two or three dimensional coordinates.

31. The UTM according to claim 28, wherein the KPI information defines a range of coordinates and indicates at least one network performance indicator between the range of coordinates.

32. The UTM according to any of claims 18 to 31, wherein the KPI requirement defines at least one of: a minimum data rate required by the UAV, and a maximum end-to-end latency required by the UAV.

33. The UTM according to any of claims 18 to 32, wherein the KPI requirement defines an intended service of the UAV, and wherein the UTM is further operable to: calculate a minimum data rate and/or a maximum end to end latency required to perform the intended service.

34. The UTM according to any of claims 18 to 33, wherein the UTM is further operable to: request, from the wireless communication network, KPI information.

35. An unmanned aircraft system traffic manager, UTM, for unmanned aerial vehicle, UAV, route planning, wherein the UAV comprises: a receiver to: receive as route planning data, from a wireless communication network, key performance indicator, KPI, information, the KPI information comprising network performance indicators corresponding to positions within a geographical area for which the wireless communication network provides coverage, and receive as route planning data, from a UAV, a KPI requirement of the UAV, wherein the KPI requirement defines a minimum network performance required by the UAV; a determiner to determine a UAV route based on the route planning data; and an initiator to initiate transmission, to a UAV, of the determined UAV route.

36. The UTM according to claim 35, wherein the UTM is configured to perform any of the steps defined in claims 1 to 17.

37. A computer-readable medium comprising instructions which, when executed on a computer, cause the computer to perform a method in accordance with any of claims 1 to 17.