Autonomous aircraft
Most contemporary autonomous aircraft are unmanned aerial vehicles (drones) with pre-programmed algorithms to perform designated tasks, but advancements in artificial intelligence technologies (e.g. machine learning) mean that autonomous control systems are reaching a point where several air taxis and associated regulatory regimes are being developed.
The earliest recorded use of an unmanned aerial vehicle for warfighting occurred in July 1849,[1] serving as a balloon carrier (the precursor to the aircraft carrier)[2] Significant development of radio-controlled drones started in the early 1900s, and originally focused on providing practice targets for training military personnel.
[3] Autonomous features such as the autopilot and automated navigation were developed progressively through the twentieth century, although techniques such as terrain contour matching (TERCOM) were applied mainly to cruise missiles.
Some modern drones have a high degree of autonomy, although they are not fully capable and the regulatory environment prohibits their widespread use in civil aviation.
As flight, navigation and communications systems have become more sophisticated, safely carrying passengers has emerged as a practical possibility.
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.
For example, researchers from the Technical University of Košice have replaced the default control algorithm of the PX4 autopilot.
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.
Mobile networks can be used for drone tracking, remote piloting, over the air updates,[14] and cloud computing.
Individual task planning/execution Choose target of opportunity (example: go SCUD hunting) Data in limited range, timeframe and numbers Limited inference supplemented by off-board data Enemy trajectory estimated Multi-Vehicle Cooperation supplemented by off-board data Enemy trajectory sensed/estimated Possible: close air space separation (+/-100yds) for AAR, formation in non-threat conditions Multi-Vehicle Coordination Fused with off-board data RT Health Diagnosis Ability to compensate for most failures and flight conditions; Ability to predict onset of failures (e.g. Prognostic Health Mgmt) Group diagnosis and resource management Collision avoidance Medium vehicle airspace separation (hundreds of yds) Vehicle Assigned Rules of Engagement RT Health Diagnosis; Ability to compensate for most failures and flight conditions – inner loop changes reflected in outer loop performance Self resource management Deconfliction Medium vehicle airspace separation (hundreds of yds) RT Health Diagnosis (What is the extent of the problems?)
Ability to compensate for most failures and flight conditions (i.e. adaptative inner loop control) Abort/RTB is insufficient Off-board replan (as required) in response to mission and health conditions Mission Flight Control and Navigation Sensing Report status Piloted Vehicle Nose camera Remote pilot commands 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.
High-altitude outdoor navigation does not require large vertical fields-of-view and can rely on GPS coordinates (which makes it simple mapping rather than SLAM).
[clarification needed] In the military sector, American Predators and Reapers are made for counterterrorism operations and in war zones in which the enemy lacks sufficient firepower to shoot them down.
In September 2013, the chief of the US Air Combat Command stated that current UAVs were "useless in a contested environment" unless crewed aircraft were there to protect them.
The Department of Defense's Unmanned Systems Integrated Roadmap FY2013-2038 foresees a more important place for UAVs in combat.
