Autonomous aircraft
An autonomous aircraft is an aircraft which flies under the control of on-board autonomous robotic systems and needs no intervention from a human pilot or remote control. Most contemporary autonomous aircraft are unmanned aerial vehicles with pre-programmed algorithms to perform designated tasks, but advancements in artificial intelligence technologies mean that autonomous control systems are reaching a point where several air taxis and associated regulatory regimes are being developed.
History
Unmanned aerial vehicles
The earliest recorded use of an unmanned aerial vehicle for warfighting occurred in July 1849, serving as a balloon carrier Significant development of radio-controlled drones started in the early 1900s, and originally focused on providing practice targets for training military personnel. The earliest attempt at a powered UAV was A. M. Low's "Aerial Target" in 1916.Autonomous features such as the autopilot and automated navigation were developed progressively through the twentieth century, although techniques such as terrain contour matching were applied mainly to cruise missiles.
Before the introduction of the Bayraktar Kızılelma some modern drones have a high degree of autonomy, although they were not fully capable and the regulatory environment prohibits their widespread use in civil aviation. However some limited trials had been undertaken. On December 17, 2025, two Bayraktar Kızılelma performed the world's first autonomous close-formation flight by two unmanned fighter jets, using artificial intelligence. This was the first time in the history of aviation when two unmanned aerial vehicles flew in close formation on their own.
Passengers
As flight, navigation and communications systems have become more sophisticated, safely carrying passengers has emerged as a practical possibility. Autopilot systems are relieving the human pilot of progressively more duties, but the pilot currently remains necessary.A number of air taxis are under development and larger autonomous transports are also being planned. The personal air vehicle is another class where from one to four passengers are not expected to be able to pilot the aircraft and autonomy is seen as necessary for widespread adoption.
Control system architecture
The computing capability of aircraft flight and navigation systems followed the advances of computing technology, beginning with analog controls and evolving into microcontrollers, then system-on-a-chip and single-board computers.Sensors
Position and movement sensors give information about the aircraft state. Exteroceptive sensors deal with external information like distance measurements, while proprioceptive ones correlate internal and external states.Civil aviation regulators and standards bodies have published high-level roadmaps and discussion papers focused on assurance, safety and governance of AI-enabled systems in aviation, particularly as autonomy increases in operations and decision support.
Degrees of freedom refers to both the amount and quality of sensors on board: 6 DOF implies 3-axis gyroscopes and accelerometers, 9 DOF refers to an IMU plus a compass, 10 DOF adds a barometer and 11 DOF usually adds a GPS receiver.
Actuators
UAV actuators include digital electronic speed controllers linked to motors/engines and propellers, servomotors, weapons, payload actuators, LEDs and speakers.Software
UAV software called the flight stack or autopilot. The purpose of the flight stack is to obtain data from sensors, control motors to ensure UAV stability, and facilitate ground control and mission planning communication.UAVs are real-time systems that require rapid response to changing sensor data. As a result, UAVs rely on single-board computers for their computational needs. Examples of such single-board computers include Raspberry Pis, Beagleboards, etc. shielded with NavIO, PXFMini, etc. or designed from scratch such as NuttX, preemptive-RT Linux, Xenomai, Orocos-Robot Operating System or DDS-ROS 2.0.
| Layer | Requirement | Operations | Example |
| Firmware | Time-critical | From machine code to processor execution, memory access | ArduCopter-v1, PX4 |
| Middleware | Time-critical | Flight control, navigation, radio management | PX4, Cleanflight, ArduPilot |
| Operating system | Computer-intensive | Optical flow, obstacle avoidance, SLAM, decision-making | ROS, Nuttx, Linux distributions, Microsoft IOT |
Civil-use open-source stacks include:
- ArduCopter
- CrazyFlie
- KKMultiCopter
- MultiWii
- * BaseFlight
- ** CleanFlight
- *** BetaFlight
- *** iNav
- *** RaceFlight
- OpenPilot
- * dRonin
- * LibrePilot
- * TauLabs
- Paparazzi
- PX4 autopilot
- * DroneCode
Loop principles
UAVs employ open-loop, closed-loop or hybrid control architectures.- Open loop – This type provides a positive control signal without incorporating feedback from sensor data.
- Closed loop – This type incorporates sensor feedback to adjust behavior. The PID controller is common. Sometimes, feedforward is employed, transferring the need to close the loop further.
Communications
Most UAVs use a radio for remote control and 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 and telemetry data about the status of aircraft systems to the remote operator. For very long range flights, military UAVs also use satellite receivers as part of satellite navigation systems. In cases when video transmission was required, the UAVs will implement a separate analog video radio link.In most modern autonomous applications, video transmission is required. A broadband link is used to carry all types of data on a single radio link. These broadband links can leverage quality of service techniques to optimize the C&C traffic for low latency. Usually, these broadband links carry TCP/IP traffic that can be routed over the Internet.
Communications can be established with:
- Ground control – a military ground control station (GCS). The MAVLink protocol is increasingly becoming popular to carry command and control data between the ground control and the vehicle.
- Remote network system, such as satellite duplex data links for some 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.
- Another aircraft, serving as a relay or mobile control station – military manned-unmanned teaming.
Modern networking standards have explicitly considered autonomous aircraft and therefore include optimizations. The 5G standard has mandated reduced user plane latency to 1ms while using ultra-reliable and low-latency communications.
Autonomy
Basic autonomy comes from proprioceptive sensors. Advanced autonomy calls for situational awareness, knowledge about the environment surrounding the aircraft from exteroceptive sensors: sensor fusion integrates information from multiple sensors.Civil aviation regulators and standards bodies have published high-level roadmaps and discussion papers focused on assurance, safety and governance of AI-enabled systems in aviation, particularly as autonomy increases in operations and decision support.
Basic principles
One way to achieve autonomous control employs multiple control-loop layers, as in hierarchical control systems. As of 2016 the low-layer loops tick as fast as 32,000 times per second, while higher-level loops may cycle once per second. The principle is to decompose the aircraft's behavior into manageable "chunks", or states, with known transitions. Hierarchical control system types range from simple scripts to finite state machines, behavior trees and hierarchical task planners. The most common control mechanism used in these layers is the PID controller which can be used to achieve hover for a quadcopter by using data from the IMU to calculate precise inputs for the electronic speed controllers and motors.Examples of mid-layer algorithms:
- Path planning: determining an optimal path for vehicle to follow while meeting mission objectives and constraints, such as obstacles or fuel requirements
- Trajectory generation : determining control maneuvers to take in order to follow a given path or to go from one location to another
- Trajectory regulation: constraining a vehicle within some tolerance to a trajectory
Autonomy features
UAV manufacturers often build in specific autonomous operations, such as:- 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
- Take-off and landing
- Failsafe: automatic landing or return-to-home upon loss of control signal
- Return-to-home: Fly back to the point of takeoff.
- 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.
Functions
Full autonomy is available for specific tasks, such as airborne refueling or ground-based battery switching; but higher-level tasks call for greater computing, sensing and actuating capabilities. One approach to quantifying autonomous capabilities is based on OODA terminology, as suggested by a 2002 US Air Force Research Laboratory, and used in the table below:Medium levels of autonomy, such as reactive autonomy and high levels using cognitive autonomy, have already been achieved to some extent and are very active research fields.
Reactive autonomy
Reactive autonomy, such as collective flight, real-time collision avoidance, wall following and corridor centring, relies on telecommunication and situational awareness provided by range sensors: optic flow, lidars, radars, sonars.Most range sensors analyze electromagnetic radiation, reflected off the environment and coming to the sensor. The cameras act as simple receivers. Lidars, radars and sonars emit and receive waves, measuring the round-trip transit time. UAV cameras do not require emitting power, reducing total consumption.
Radars and sonars are mostly used for military applications.
Reactive autonomy has in some forms already reached consumer markets: it may be widely available in less than a decade.
Simultaneous localization and mapping
SLAM combines odometry and external data to represent the world and the position of the UAV in it in three dimensions. High-altitude outdoor navigation does not require large vertical fields-of-view and can rely on GPS coordinates.Two related research fields are photogrammetry and LIDAR, especially in low-altitude and indoor 3D environments.
- Indoor photogrammetric and stereophotogrammetric SLAM has been demonstrated with quadcopters.
- Lidar platforms with heavy, costly and gimbaled traditional laser platforms are proven. Research attempts to address production cost, 2D to 3D expansion, power-to-range ratio, weight and dimensions. LED range-finding applications are commercialized for low-distance sensing capabilities. Research investigates hybridization between light emission and computing power: phased array spatial light modulators, and frequency-modulated-continuous-wave MEMS-tunable vertical-cavity surface-emitting lasers.