Capabilities - Unmanned Aerial Vehicle (UAV, Drone). The Quadcopter

Lecture

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Degrees of UAV autonomy

UAV manufacturers often build in certain autonomous operations, such as:

Self-leveling: stabilizing orientation along the pitch and roll axes.
Altitude hold: the drone maintains altitude using atmospheric pressure and/or GPS data.
Hovering/position hold: maintaining level pitch and roll, a stable heading, and yaw altitude while holding position using GNSS or inertial sensors.
Headless mode: pitch control relative to the pilot's position rather than relative to the vehicle's axes.
Carefree: automatic roll and yaw control during horizontal movement
Takeoff and landing (using various airborne or ground-based sensors and systems; see Autoland)
Failsafe: automatic landing or return-to-home upon loss of the control signal
Return to home: fly back to the takeoff point (often first climbing to altitude to avoid possible obstacles such as trees or buildings).
Follow me: maintain position relative to a moving pilot or other object using GNSS, image recognition, or a radio beacon.
GPS waypoint navigation: using GNSS to navigate to an intermediate location along the route.
Orbit around a subject: similar to "Follow me", but continuously circling the target.
Pre-programmed aerobatic maneuvers (such as rolls and loops)

Capabilities

Full autonomy is available for specific tasks, such as in-flight refueling [87] or battery swapping on the ground; but higher-level tasks require greater computational, sensing, and actuation capabilities. One approach to quantifying autonomous capabilities is based on OODA terminology, proposed by the U.S. Air Force Research Laboratory in 2002 and used in the table below: [88]

US Autonomous Control Levels chart
Level	Level Descriptor	Observe	Orient	Decide	Act
	Perception / situational awareness	Analysis / coordination	Decision-making	Capability
10	Fully autonomous	Aware of everything in the battlespace	Coordinates as required	Capable of complete independence	Requires little guidance to operate
9	Battlespace swarm cognizance	Infers intent in the battlespace - own intent and that of others (friend and foe). Complex / contested environment - onboard tracking	Assigned group strategic goals Estimated enemy strategy	Distributed tactical group planning Individual determination of tactical goal Individual planning / execution of tasks Select tactical targets	Group achieves strategic goal without supervisor involvement
8	Battlespace cognizance	Infers proximity - own intent and that of others (friend and foe) Reduces dependence on external data	Assigned group strategic goals Estimated enemy tactics ATR	Coordinated tactical group planning Individual planning / execution of tasks Select target of opportunity	Group achieves strategic goal with minimal supervisory control (example: go hunt SCUDs)
7	Battlespace knowledge	Short-track awareness - history and projection of battle events Data in limited range, time frame, and numbers Limited inference, augmented by off-board data	Assigned tactical group goals Estimated enemy track	Individual planning / execution of tasks to achieve goals	Group achieves tactical goals with minimal supervision
6	Real time Multi-vehicle coordination	Long-range awareness - onboard sensing at long distances, augmented by off-board data	Assigned tactical group goals Enemy track detected / estimated	Coordinated trajectory planning and execution to achieve goals - group optimization	Group achieves tactical goals with minimal supervision Possible: close airspace separation (+/- 100 yards) for AAR, formation in safe conditions
5	Real time Multi-vehicle coordination	Sensed awareness - local sensors to detect others, Fused with external data	Tactical group plan assigned RT Health Diagnosis - ability to compensate for most failures and flight conditions; Ability to predict the onset of failures (e.g., Prognostic Health Mgmt) Group diagnostics and resource management	Onboard trajectory replanning - optimization for current and predicted conditions Collision avoidance	Autonomous execution of an externally assigned tactical plan Medium vehicle airspace separation (hundreds of yards)
4	Fault / event adaptation Vehicle	Deliberate awareness - allies pass along data	Tactical group plan assigned Assigned rules of engagement RT Health Diagnosis; ability to compensate for most failures and flight conditions - inner-loop changes are reflected in outer-loop characteristics	Onboard trajectory replanning - event-driven Autonomous resource management Conflict resolution	Autonomous execution of an externally assigned tactical plan Medium vehicle airspace separation (hundreds of yards)
3	Robust response to real-time faults / events	Health / status history and models	Tactical group plan assigned RT Health Diagnosis (what is the scope of the problems?) Ability to compensate for most failures and flight conditions (e.g., adaptive inner-loop control)	Evaluate status against required mission capabilities Abort / RTB insufficient	Autonomous execution of an externally assigned tactical plan
2	Changeable mission	Health / status sensors	RT Health diagnosis (Do I have a problem?) External replanning (as required)	Execute preprogrammed or uploaded plans in response to mission and health status	Autonomous execution of an externally assigned tactical plan
1	Execute preplanned Mission	Preloaded mission data Flight control and navigation sensing	Pre / post-flight BIT Status report	Programmed mission and abort plans	Wide airspace separation requirements (miles)
0	Remotely Piloted Vehicle	Flight control (attitude, speed) sensing Nose camera	Telemetry data External pilot commands	No data	Controlled by an external pilot

Intermediate levels of autonomy, such as reactive autonomy, and higher levels employing cognitive autonomy, have to some extent already been achieved and are very active areas of research.

Reactive autonomy Perceptual control theory

Reactive autonomy, such as collective flight, real-time collision avoidance, wall following, and corridor centering, depends on communication links and the situational awareness provided by range sensors: optical flow, [89] lidars (light radars), radars, and sonars.

Most range sensors analyze electromagnetic radiation reflected from the environment and reaching the sensor. Cameras (for visual flow) act as simple receivers. Lidars, radars, and sonars (using mechanical sound waves) emit and receive waves, measuring the round-trip travel time. UAV cameras do not require emission power, which reduces overall power consumption.

Radars and sonars are mainly used for military purposes.

Reactive autonomy has, in some forms, already reached consumer markets: it may become widely available in less than a decade. [63]

Unmanned Aerial Vehicle (UAV, Drone). The Quadcopter

Newest (2013) autonomous levels for existing systems

Simultaneous Localization and Mapping

SLAM combines odometry and external data to represent the world and the UAV's position in it in three dimensions. Outdoor navigation at high altitude does not require large vertical fields of view and can rely on GPS coordinates (which makes it simple mapping rather than SLAM). [90]

Two related research areas are photogrammetry and lidar, especially in 3D environments at low altitudes and indoors.

Photogrammetric and stereo-photogrammetric SLAM for indoor use has been demonstrated on quadcopters. [91]
Lidar platforms with heavy, expensive, traditional gimbal-mounted laser platforms have already proven themselves. Research is aimed at reducing production cost, extending from 2D to 3D, improving the power-to-range ratio, weight, and size. [92] [93] LED rangefinders are commercializing short-range detection capabilities. Research is investigating hybridization between light emission and computing power: phased-array spatial light modulators, [94] [95] and frequency-modulated continuous-wave (FMCW) MEMS-tunable vertical-cavity surface-emitting lasers (VCSELs). [96]

Swarm behavior

A robot swarm refers to a network of agents capable of dynamically changing configuration as elements leave or join the network. They provide greater flexibility than cooperation among a few agents. A swarm can open the way to data fusion. Some bio-inspired flocking behaviors use steering and flocking algorithms.

Future military potential

In the military sector, American Predators and Reapers are designed for counterterrorism operations and combat zones where the enemy lacks sufficient firepower to shoot them down. They are not designed to face air defenses or engage in air combat. In September 2013, the head of the US Combat Air Command stated that unmanned aircraft were currently "useless in a contested environment" unless manned aircraft were present to protect them. A 2012 Congressional Research Service (CRS) report suggested that in the future UAVs might perform tasks beyond reconnaissance, surveillance, intelligence gathering, and strikes; the CRS report listed "air-to-air" combat among possible future undertakings ("the more difficult task of the future"). The Department of Defense's Unmanned Systems Integrated Roadmap for 2013–2038 envisions a more important place for UAVs in combat. Challenges include extended capabilities, human-UAV interaction, managing an increased flow of information, increased autonomy, and the development of specialized munitions for UAVs. DARPA's system of systems project [97] or the work of General Atomics may foreshadow future warfare scenarios, the latter unveiling the Avenger swarm equipped with an area-defense system using a high-energy liquid laser (HEL). [98]

Cognitive Radio (Cognitive Radio System, CRS)

Cognitive radio technology may have applications in UAVs. [99]

A cognitive radio system (Cognitive Radio System, CRS) — a radio system capable of gathering information about its own operating characteristics and, based on this data, adjusting its operating parameters

Learning capabilities

UAVs can use distributed neural networks. [63]

Market

Military

As of 2020, seventeen countries have UAVs in service, and more than 100 countries use UAVs for military purposes. [100] The global military UAV market is dominated by companies based in the US and Israel. In terms of sales in 2017, the US share of the military market was more than 60%. Four of the five largest military UAV manufacturers are American, including General Atomics, Lockheed Martin, Northrop Grumman, and Boeing, followed by the Chinese company CASC. [101]Israeli companies mainly focus on small UAV surveillance systems, and by number of drones, Israel exported 60.7% (2014) of the UAVs on the market, while the United States exported 23.9% (2014); the main importers of military UAVs are the United Kingdom (33.9%) and India (13.2%). In the United States alone, more than 9,000 military UAVs were in operation in 2014. [102] General Atomics is the dominant manufacturer of the Global Hawk and Predator / Mariner product lines.

Civilian

The civilian drone market is dominated by Chinese companies. The Chinese drone manufacturer DJI alone held a 74% share of the civilian market in 2018, with no other company accounting for more than 5%, and with a forecast of $11 billion in global sales by 2020. [103] After a thorough review of its activities, the US Department of the Interior grounded its fleet of DJI drones in 2020, and the Department of Justice banned the use of federal funds to purchase DJI and other foreign-made UAVs. [104] [105] DJI is followed by the Chinese company Yuneec, the American company 3D Robotics, and the French company Parrot, with a significant gap in market share. [106]As of March 2018, more than a million UAVs were registered with the US Federal Aviation Administration (878,000 hobbyist and 122,000 commercial). NPD's 2018 data indicates that consumers are increasingly buying drones with more advanced features, with 33-percent growth in both the $500+ and $1000+ market segments. [107]

The civilian UAV market is relatively new compared to the military one. Companies are emerging simultaneously in both developed and developing countries. Many early-stage startups have received support and funding from investors, as in the United States, and from government agencies, as in the case of India. [108] Some universities offer research and educational programs or degrees. [109] Private organizations also provide online programs and personal training programs for both recreational and commercial use of UAVs. [110]

Consumer drones are also widely used by military organizations around the world because of the cost-effectiveness of consumer products. In 2018, the Israeli military began using DJI Mavic and Matrice series UAVs for light reconnaissance missions, since civilian drones are easier to use and have higher reliability. DJI unmanned aerial vehicles are also the most widely used commercial unmanned aerial system used by the US Army. [111] [112] DJI surveillance drones have also been used by Chinese police in Xinjiang since 2017.

By 2021, the global UAV market will reach $21.47 billion, with the Indian market reaching $885.7 million [115].

Lighted drones are beginning to be used in night displays for artistic and advertising purposes.

Transportation

AIA reports that large cargo and passenger drones must be certified and introduced within the next 20 years. Large sensor-equipped drones are expected from 2018; short-haul, low-altitude cargo flights outside cities from 2025; long-haul cargo flights by the mid-2030s, followed by passenger flights by 2040. R&D spending is expected to grow from a few hundred million dollars in 2018 to $4 billion by 2028 and $30 billion by 2036 [116].

Agriculture

As global demand for food production grows exponentially, resources are being depleted, farmland is shrinking, and agricultural labor is becoming increasingly scarce, there is an acute need for more convenient and intelligent agricultural solutions than traditional methods, and progress in the agricultural drone and robotics industry is expected to accelerate. [117] Agricultural drones have been used in regions such as Africa to build sustainable agriculture. [118]

Law enforcement

Use of UAVs in law enforcement

Police can use drones for applications such as search and rescue operations and traffic monitoring. [119]

Development considerations

Animal mimicry - ethology

Flapping-wing ornithopters that mimic birds or insects are an area of research in micro-UAVs. Their inherent stealth makes them well suited for spy missions.

The Nano Hummingbird is commercially available, while fly-inspired micro-UAVs weighing less than 1 gram, although using a tether, can "land" on vertical surfaces. [120]

Other projects include unmanned "beetles" and other insects. [121]

Research is studying miniature optical-flow sensors called ocelli, mimicking the compound eyes of insects formed from multiple facets, which can transmit data to neuromorphic chips capable of processing optical flow as well as differences in light intensity.

Endurance

UEL UAV-741 Wankel engine for UAV operation

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