Editing Unmanned aerial vehicle (section)

==Computer control systems==
[[File:AbuseMark AfroFlight Naze 32 Flight Controller rev5 white.jpg|thumb|upright=1.35|A flight controller run on either CleanFlight or BaseFlight firmware for [[multirotor]] UAVs]]
UAV computing capability followed the advances of computing technology, beginning with analog controls and evolving into microcontrollers, then [[System on a chip|system-on-a-chip]] (SOC) and [[single-board computer]]s (SBC).

Modern system hardware for UAV control is often called the flight controller (FC), flight controller board (FCB) or autopilot. Common [[UAV-systems hardware chart#Consumer UAV Flight Controller List|UAV-systems control hardware]] typically incorporate a primary microprocessor, a secondary or failsafe processor, and sensors such as accelerometers, gyroscopes, magnetometers, and barometers into a single module.

In 2024 EASA agreed on the first certification basis for a UAV flight controller in compliance with the ETSO-C198 for Embention's autopilot. The certification of the UAV flight control systems aims to facilitate the integration of UAVs within the airspace and the operation of drones in critical areas.<ref>{{Cite web |last=Antonio |date=2024-07-11 |title=EASA Approves ETSO Certification Basis for Veronte Autopilot |url=https://www.embention.com/en/news/easa-approves-etso-certification-basis-for-veronte-autopilot/ |access-date=2024-08-02 |website=Embention |language=en-US}}</ref>

===Architecture===
====Sensors====
Position and movement sensors give information about the aircraft state. Exteroceptive sensors deal with external information like distance measurements, while exproprioceptive ones correlate internal and external states.<ref name="Nature - Future of sUAVs">{{cite journal|last1=Floreano|first1=Dario|last2=Wood|first2=Robert J.|title=Science, technology and the future of small autonomous drones|journal=Nature|date=27 May 2015|volume=521|issue=7553|pages=460–466|doi=10.1038/nature14542|pmid=26017445|bibcode=2015Natur.521..460F|s2cid=4463263|url=http://infoscience.epfl.ch/record/208757|access-date=26 October 2019|archive-date=26 October 2019|archive-url=https://web.archive.org/web/20191026050814/https://infoscience.epfl.ch/record/208757|url-status=live}}</ref>

Non-cooperative sensors are able to detect targets autonomously so they are used for separation assurance and collision avoidance.<ref>{{Cite journal|last1=Fasano|first1=Giancarmine|last2=Accardo|first2=Domenico|last3=Tirri|first3=Anna Elena|last4=Moccia|first4=Antonio|last5=De Lellis|first5=Ettore|date=1 October 2015|title=Radar/electro-optical data fusion for non-cooperative UAS sense and avoid|journal=Aerospace Science and Technology|volume=46|pages=436–450|doi=10.1016/j.ast.2015.08.010|doi-access=free|bibcode=2015AeST...46..436F }}</ref>

Degrees of freedom (DOF) refers to both the amount and quality of onboard sensors: 6 DOF implies 3-axis gyroscopes and accelerometers (a typical [[inertial measurement unit]]{{snd}} IMU), 9 DOF refers to an IMU plus a compass, 10 DOF adds a barometer and 11 DOF usually adds a GPS receiver.<ref>{{Cite web|title = Arduino Playground – WhatIsDegreesOfFreedom6DOF9DOF10DOF11DOF|url = http://playground.arduino.cc/Main/WhatIsDegreesOfFreedom6DOF9DOF10DOF11DOF|website = playground.arduino.cc|access-date = 4 February 2016|archive-date = 18 February 2016|archive-url = https://web.archive.org/web/20160218110500/http://playground.arduino.cc/Main/WhatIsDegreesOfFreedom6DOF9DOF10DOF11DOF|url-status = live}}</ref>

In addition to the navigation sensors, the UAV (or UAS) can be also equipped with monitoring devices such as: [[RGB color model|RGB]], [[Multispectral imaging|multispectral]], [[Hyperspectral imaging|hyper-spectral]] cameras or [[Lidar|LiDAR]], which may allow providing specific measurements or observations.<ref>{{Cite journal |last1=Manfreda |first1=Salvatore |last2=McCabe |first2=Matthew |last3=Miller |first3=Pauline |last4=Lucas |first4=Richard |last5=Pajuelo Madrigal |first5=Victor |last6=Mallinis |first6=Giorgos |last7=Ben Dor |first7=Eyal |last8=Helman |first8=David |last9=Estes |first9=Lyndon |last10=Ciraolo |first10=Giuseppe |last11=Müllerová |first11=Jana |last12=Tauro |first12=Flavia |last13=de Lima |first13=M. |last14=de Lima |first14=João |last15=Maltese |first15=Antonino |date=2018-04-20 |title=On the Use of Unmanned Aerial Systems for Environmental Monitoring |journal=Remote Sensing |language=en |volume=10 |issue=4 |pages=641 |doi=10.3390/rs10040641 |bibcode=2018RemS...10..641M |issn=2072-4292|doi-access=free |hdl=10251/127481 |hdl-access=free }}</ref>

====Actuators====
UAV [[actuator]]s include [[Electronic speed control|digital electronic speed controllers]] (which control the [[Revolutions per minute|RPM]] of the motors) linked to motors/[[engine]]s and [[propeller]]s, [[servomotor]]s (for planes and helicopters mostly), weapons, payload actuators, LEDs and speakers.

====Software====
Modern UAVs run a software stack that ranges from low-level firmware that directly controls actuators, to high level flight planning. At the lowest level, firmware directly controls reading from sensors such as an [[Inertial measurement unit|IMU]]<ref>{{Cite web|url=https://www.roboticsbook.org/S73_drone_sensing.html|title=7.3. Sensing for Drones — Introduction to Robotics and Perception|website=www.roboticsbook.org}}</ref> and commanding actuators such as motors. Control software (often referred to as an autopilot) is responsible for computing actuator speeds given desired vehicle velocity. Due to its direct interaction with hardware, this software is time-critical and may run on [[microcontroller]]s. This software may also handle radio communications, in the case of UAVs that are not autonomous. One popular example is the PX4 autopilot.

At the next level, autonomy algorithms compute the desired velocity given higher level goals. For example, [[trajectory optimization]]<ref>{{Cite web|url=https://www.roboticsbook.org/S75_drone_planning.html|title=7.5. Trajectory Optimization — Introduction to Robotics and Perception|website=www.roboticsbook.org}}</ref> may be used to calculate a flight trajectory given a desired goal location. This software is not necessarily time-critical, and can often run on a single board computer running an operating system such as [[Linux]] with relaxed time constraints.

====Loop principles====
[[File:UAV Flight control.jpg|thumb|Typical flight-control loops for a multirotor]]
UAVs employ open-loop, closed-loop or hybrid control architectures.

* [[Open-loop controller|Open loop]]{{snd}} This type provides a positive control signal (faster, slower, left, right, up, down) without incorporating feedback from sensor data.
* [[Closed-loop transfer function|Closed loop]]{{snd}} This type incorporates sensor feedback to adjust behavior (reduce speed to reflect tailwind, move to altitude 300 feet). The [[PID controller]] is common. Sometimes, [[Feed forward (control)|feedforward]] is employed, transferring the need to close the loop further.<ref>{{Cite web|url = http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2327.pdf|title = The Navigation and Control technology inside the AR.Drone micro UAV|date = 2011|website = IFAC World Congress|author = Pierre-Jean Bristeau|author2 = François Callou|author3 = David Vissière|author4 = Nicolas Petit|access-date = 4 February 2016|archive-date = 27 February 2023|archive-url = https://web.archive.org/web/20230227213841/https://folk.ntnu.no/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2327.pdf|url-status = live}}</ref>

====Communications====
UAVs use a [[radio]] for control and [[Data link|exchange of video and other data]]. Early UAVs had only [[narrowband]] uplink. Downlinks came later. These bi-directional narrowband radio links carried command and control (C&C) and [[telemetry]] data about the status of aircraft systems to the remote operator.

In most modern UAV applications, video transmission is required. So instead of having separate links for C&C, telemetry and video traffic, a [[broadband]] link is used to carry all types of data. These broadband links can leverage [[quality of service]] techniques and carry [[TCP/IP]] traffic that can be routed over the internet.

The radio signal from the operator side can be issued from either:

* Ground control – a human operating a [[Transmitter|radio transmitter]]/receiver, a smartphone, a tablet, a computer, or the original meaning of a [[UAV ground control station|military ground control station (GCS)]]. 
* Remote network system, such as satellite duplex data links for some [[Armed forces|military powers]]. Downstream digital video over mobile networks has also entered consumer markets, while direct UAV control uplink over the cellular mesh and LTE have been demonstrated and are in trials.<ref>{{Cite web|title = Cellular enables safer drone deployments|url = https://www.qualcomm.com/invention/technologies/lte/advanced-pro/cellular-drone-communication|website = Qualcomm|access-date = 9 May 2018|archive-date = 9 May 2018|archive-url = https://web.archive.org/web/20180509151213/https://www.qualcomm.com/invention/technologies/lte/advanced-pro/cellular-drone-communication|url-status = live}}</ref>
* Another aircraft, serving as a relay or mobile control station{{snd}} military manned-unmanned teaming (MUM-T).<ref>{{Cite web|url = http://apps.dtic.mil/dtic/tr/fulltext/u2/a565510.pdf|archive-url = https://web.archive.org/web/20160206104148/http://www.dtic.mil/dtic/tr/fulltext/u2/a565510.pdf|url-status = live|archive-date = 6 February 2016|title = Identifying Critical Manned-Unmanned Teaming Skills for Unmanned Aircraft System Operators|date = September 2012|website = U.S. Army Research Institute for the Behavioral and Social Sciences}}</ref>

Modern networking standards have explicitly considered drones and therefore include optimizations. The 5G standard has mandated reduced user plane latency to 1ms while using ultra-reliable and low-latency communications.<ref>{{Cite web|title=Minimum requirements related to technical performance for IMT-2020 radio interface(s)|url=https://www.itu.int/pub/R-REP-M.2410-2017|access-date=8 October 2020|website=www.itu.int|archive-date=6 August 2020|archive-url=https://web.archive.org/web/20200806202716/https://www.itu.int/pub/R-REP-M.2410-2017|url-status=live}}</ref>

UAV-to-UAV coordination supported by [[Remote ID]] communication technology. Remote ID messages (containing the UAV coordinates) are broadcast and can be used for collision-free navigation.<ref>{{Cite book| last1 = Vinogradov| first1 = Evgenii| last2 = Kumar| first2 = A. V. S. Sai Bhargav| last3 = Minucci| first3 = Franco| last4 = Pollin| first4 = Sofie| last5 = Natalizio| first5 = Enrico| chapter = Remote ID for separation provision and multi-agent navigation| title = 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)| year = 2023| pages = 1–10| doi = 10.1109/DASC58513.2023.10311133| arxiv = 2309.00843| isbn = 979-8-3503-3357-2}}</ref>

=== Autonomy ===
{{main|Autonomous aircraft}}
[[File:Degrees of autonomy.jpg|thumb|UAV's degrees of autonomy]]
The level of autonomy in UAVs varies widely. UAV manufacturers often build in specific autonomous operations, such as:<ref>{{Cite web|title=Automated Vehicles for Safety |location=United States|publisher=[[National Highway Traffic Safety Administration]]|url=https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety|access-date=2021-10-08|language=en|archive-date=7 October 2021|archive-url=https://web.archive.org/web/20211007021013/https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety|url-status=live}}</ref>

* Self-level: attitude stabilization on the pitch and roll axes.
* Altitude hold: The aircraft maintains its altitude using barometric pressure and/or GPS data. 
* Hover/position hold: Keep level pitch and roll, stable yaw heading and altitude while maintaining position using [[GNSS]] or inertial sensors.
* Headless mode: Pitch control relative to the position of the pilot rather than relative to the vehicle's axes. 
* Care-free: automatic roll and yaw control while moving horizontally
* Takeoff and landing (using a variety of aircraft or ground-based sensors and systems; see also "[[autoland]]")
* Failsafe: automatic landing or return-to-home upon loss of control signal
* Return-to-home: Fly back to the point of takeoff (often gaining altitude first to avoid possible intervening obstructions such as trees or buildings).
* Follow-me: Maintain relative position to a moving pilot or other object using GNSS, [[image recognition]] or homing beacon.
* GPS waypoint navigation: Using GNSS to navigate to an intermediate location on a travel path.
* Orbit around an object: Similar to Follow-me but continuously circle a target.
* Pre-programmed [[aerobatics]] (such as rolls and loops)
* Pre-programmed delivery (delivery drones)

One approach to quantifying autonomous capabilities is based on [[OODA loop|OODA]] terminology, as suggested by a 2002 US [[Air Force Research Laboratory]] report, and used in the table on the right.<ref>{{Cite web|url = https://apps.dtic.mil/sti/citations/ADA515926|archive-url = https://web.archive.org/web/20200924155100/https://apps.dtic.mil/sti/citations/ADA515926|url-status = live|archive-date = 24 September 2020|title = Metrics, Schmetrics! How The Heck Do You Determine A UAV's Autonomy Anyway?|date = August 2002|website = US Air Force Research Laboratory|last = Clough|first = Bruce}}</ref>

[[File:X-47B receives fuel from an Omega K-707 tanker while operating in the Atlantic Test Ranges.jpg|thumb|A [[Northrop Grumman X-47B]] unmanned combat aircraft demonstrator of the US Navy refuels in flight from a tanker aircraft.]]
Full autonomy is available for specific tasks, such as [[airborne refueling]]<ref>{{Cite news|title = Watch a step in Navy history: an autonomous drone gets refueled mid-air|url = https://www.washingtonpost.com/news/checkpoint/wp/2015/04/23/watch-a-step-in-aviation-history-an-autonomous-drone-getting-refueled-mid-air/|newspaper = The Washington Post|date = 23 April 2015|access-date = 3 February 2016|issn = 0190-8286|first = Christian|last = Davenport|archive-date = 20 January 2016|archive-url = https://web.archive.org/web/20160120103200/https://www.washingtonpost.com/news/checkpoint/wp/2015/04/23/watch-a-step-in-aviation-history-an-autonomous-drone-getting-refueled-mid-air/|url-status = live}}</ref> or ground-based battery switching.

Other functions available or under development include; collective flight, real-time [[Collision avoidance system|collision avoidance]], wall following, corridor centring, simultaneous localization and mapping and [[Swarm robotics|swarming]],<ref>{{cite journal |last1=Tahir |first1=Anam |last2=Böling |first2=Jari |last3=Haghbayan |first3=Mohammad-Hashem |last4=Toivonen |first4=Hannu T. |last5=Plosila |first5=Juha |title=Swarms of Unmanned Aerial Vehicles – A Survey |journal=Journal of Industrial Information Integration |date=2019 |volume=16 |pages=100106 |doi=10.1016/j.jii.2019.100106 |doi-access=free}}</ref> [[cognitive radio]], and [[machine learning]]. In this context, [[computer vision]] can play an important role for automatically ensuring flight safety.