• Autonomous AI Equipped Flying Killer Robots Are Here! - Autonomous AI E

    From Larry Dighera@21:1/5 to All on Fri Jun 4 08:55:30 2021
    Video: https://www.stm.com.tr/en/kargu-autonomous-tactical-multi-rotor-attack-uav

    Autonomous AI Equipped Flying Killer Robots, what could possibly go wrong?

    --------------------------------------------------------------------------- https://www.npr.org/2021/06/01/1002196245/a-u-n-report-suggests-libya-saw-the-first-battlefield-killing-by-an-autonomous-d

    A Military Drone With A Mind Of Its Own Was Used In Combat, U.N. Says

    June 1, 20213:09 PM ET

    A Kargu rotary-wing attack drone loitering munition system manufactured by
    the STM defense company of Turkey. A U.N. report says the weapons system was used in Libya in March 2020.
    Emre Cavdar/STM

    Military-grade autonomous drones can fly themselves to a specific location, pick their own targets and kill without the assistance of a remote human operator. Such weapons are known to be in development, but until recently
    there were no reported cases of autonomous drones killing fighters on the battlefield.

    Now, a United Nations report about a March 2020 skirmish in the military conflict in Libya says such a drone, known as a lethal autonomous weapons system — or LAWS — has made its wartime debut. But the report does not say explicitly that the LAWS killed anyone.

    "If anyone was killed in an autonomous attack, it would likely represent an historic first known case of artificial intelligence-based autonomous
    weapons being used to kill," Zachary Kallenborn wrote in Bulletin of the
    Atomic Scientists. https://thebulletin.org/2021/05/was-a-flying-killer-robot-used-in-libya-quite-possibly/

    The assault came during fighting between the U.N.-recognized Government of National Accord and forces aligned with Gen. Khalifa Haftar, according to
    the report by the U.N. Panel of Experts on Libya.

    "Logistics convoys and retreating [Haftar-affiliated forces] were
    subsequently hunted down and remotely engaged by the unmanned combat aerial vehicles or the lethal autonomous weapons systems such as the STM Kargu-2
    ... and other loitering munitions," the panel wrote.

    The Kargu-2 https://www.stm.com.tr/en/kargu-autonomous-tactical-multi-rotor-attack-uav
    is an attack drone made by the Turkish company STM that can be operated both autonomously and manually and that purports to use "machine learning" and "real-time image processing" against its targets.

    The U.N. report goes on: "The lethal autonomous weapons systems were
    programmed to attack targets without requiring data connectivity between the operator and the munition: in effect, a true 'fire, forget and find' capability."

    "Fire, forget and find" refers to a weapon that once fired can guide itself
    to its target.

    The idea of a "killer robot" has moved from fantasy to reality

    Drone warfare itself is not new. For years, military forces and rebel groups have used remote-controlled aircraft to carry out reconnaissance, target infrastructure and attack people. The U.S. in particular has used drones extensively to kill militants and destroy physical targets.

    Azerbaijan used armed drones to gain a major advantage over Armenia in
    recent fighting for control of the Nagorno-Karabakh region. Just last month, the Israel Defense Forces reportedly used drones to drop tear gas on
    protesters in the occupied West Bank, while Hamas launched loitering
    munitions — so-called kamikaze drones — into Israel.

    What's new about the incident in Libya, if confirmed, is that the drone that was used had the capacity to operate autonomously, which means there is no human controlling it, essentially a "killer robot," formerly the stuff of science fiction.

    Not all in the world of security are concerned.

    "I must admit, I am still unclear on why this is the news that has gotten so much traction," Ulrike Franke, a senior policy fellow at the European
    Council on Foreign Relations, wrote on Twitter.

    Franke noted that loitering munitions have been used in combat for "a while" and questioned whether the autonomous weapon used in Libya actually caused
    any casualties.

    Jack McDonald, a lecturer in war studies at King's College London, noted
    that the U.N. report did not make clear whether the Kargu-2 was operating autonomously or manually at the time of the attack.

    While this incident may or may not represent the first battlefield killing
    by an autonomous drone, the idea of such a weapon is disquieting to many.

    A global survey commissioned by the Campaign to Stop Killer Robots last year found that a majority of respondents — 62% — said they opposed the use of lethal autonomous weapons systems. https://www.stopkillerrobots.org/2021/01/poll-opposition-to-killer-robots-strong/


    Was a flying killer robot used in Libya? Quite possibly
    By Zachary Kallenborn | May 20, 2021

    A promotional video about autonomous weaponized drone. A screenshot from a promotional video advertising the Kargu drone. In the video, the weapon
    dives toward a target before exploding.

    Last year in Libya, a Turkish-made autonomous weapon—the STM Kargu-2
    drone—may have “hunted down and remotely engaged” retreating soldiers loyal
    to the Libyan General Khalifa Haftar, according to a recent report by the UN Panel of Experts on Libya. Over the course of the year, the UN-recognized Government of National Accord pushed the general’s forces back from the
    capital Tripoli, signaling that it had gained the upper hand in the Libyan conflict, but the Kargu-2 signifies something perhaps even more globally significant: a new chapter in autonomous weapons, one in which they are used
    to fight and kill human beings based on artificial intelligence.

    The Kargu is a “loitering” drone that can use machine learning-based object classification to select and engage targets, with swarming capabilities in development to allow 20 drones to work together. The UN report calls the Kargu-2 a lethal autonomous weapon. It’s maker, STM, touts the weapon’s “anti-personnel” capabilities in a grim video showing a Kargu model in a
    steep dive toward a target in the middle of a group of manikins. (If anyone
    was killed in an autonomous attack, it would likely represent an historic
    first known case of artificial intelligence-based autonomous weapons being
    used to kill. The UN report heavily implies they were, noting that lethal autonomous weapons systems contributed to significant casualties of the
    manned Pantsir S-1 surface-to-air missile system, but is not explicit on the matter.)

    Many people, including Steven Hawking and Elon Musk, have said they want to
    ban these sorts of weapons, saying they can’t distinguish between civilians and soldiers, while others say they’ll be critical in countering fast-paced threats like drone swarms and may actually reduce the risk to civilians
    because they will make fewer mistakes than human-guided weapons systems. Governments at the United Nations are debating whether new restrictions on combat use of autonomous weapons are needed. What the global community
    hasn’t done adequately, however, is develop a common risk picture. Weighing risk vs. benefit trade-offs will turn on personal, organizational, and
    national values, but determining where risk lies should be objective.

    It’s just a matter of statistics.

    At the highest level, risk is a product of the probability and consequence
    of error. Any given autonomous weapon has some chance of messing up, but
    those mistakes could have a wide range of consequences. The highest risk autonomous weapons are those that have a high probability of error and kill
    a lot of people when they do. Misfiring a .357 magnum is one thing; accidentally detonating a W88 nuclear warhead is something else.

    There are at least nine questions that are important to understanding where
    the risks are when it comes to autonomous weapons.

    How does an autonomous weapon decide who to kill? Landmines—in some sense an extremely simple autonomous weapon—use pressure sensors to determine when to explode. The firing threshold can be varied to ensure the landmine does not explode when a child picks it up. Loitering munitions like the Israeli Harpy typically detect and home in on enemy radar signatures. Like with landmines, the sensitivity can be adjusted to separate civilian from military radar.
    And thankfully, children don’t emit high-powered radio waves.

    But what has prompted international concern is the inclusion of machine learning-based decision-making as was used in the Kargu-2. These types of weapons operate on software-based algorithms “taught” through large training datasets to, for example, classify various objects. Computer vision programs can be trained to identify school buses, tractors, and tanks. But the
    datasets they train on may not be sufficiently complex or robust, and an artificial intelligence (AI) may “learn” the wrong lesson. In one case, a company was considering using an AI to make hiring decisions until
    management determined that the computer system believed the most important qualification for job candidates was being named Jared and playing high
    school lacrosse. The results wouldn’t be comical at all if an autonomous
    weapon made similar mistakes. Autonomous weapons developers need to
    anticipate the complexities that could cause a machine learning system to
    make the wrong decision. The black box nature of machine learning, in which
    how the system makes decisions is often opaque, adds extra challenges.

    Worried about the autonomous weapons of the future? Look at what's already
    gone wrong
    What role do humans have? Humans might be able to watch for something going wrong. In human-in-the-loop configurations, a soldier monitors autonomous weapon activities, and, if the situation appears to be headed in a horrific direction, can make a correction. As the Kargu-2’s reported use shows, a human-off-the-loop system simply does its thing without a safeguard. But
    having a soldier in the loop is no panacea. The soldier may trust the
    machine and fail to adequately monitor its operation. For example, Missy Cummings, the director of Duke University’s Human and Autonomy Laboratory, finds that when it comes to autonomous cars, “drivers who think their cars
    are more capable than they are may be more susceptible to increased states
    of distractions, and thus at higher risk of crashes.”

    Of course, a weapon’s autonomous behavior may not always be on—a human might
    be in, on, or off the loop based on the situation. South Korea has deployed
    a sentry weapon along the demilitarized zone with North Korea called the SGR A-1 that reportedly operates this way. The risk changes based on how and
    when the fully autonomous function is flipped on. Autonomous operation by default obviously creates more risk than autonomous operation restricted
    only to narrow circumstances.

    What payload does an autonomous weapon have? Accidentally shooting someone
    is horrible, but vastly less so than accidentally detonating a nuclear
    warhead. The former might cost an innocent his or her life, but the latter
    may kill hundreds of thousands. Policymakers may focus on the larger
    weapons, recognizing the costs of mistake, potentially reducing the risks of autonomous weapons. However, exactly what payloads autonomous weapons will
    have is unclear. In theory, autonomous weapons could be armed with guns,
    bombs, missiles, electronic warfare jammers, lasers, microwave weapons, computers for cyber-attack, chemical weapons agents, biological weapons
    agents, nuclear weapons, and everything in between.

    What is the weapon targeting? Whether an autonomous weapon is shooting a
    tank, a naval destroyer, or a human matters. Current machine learning-based systems cannot effectively distinguish a farmer from a solider. Farmers
    might hold a rifle to defend their land, while soldiers might use a rake to knock over a gun turret. But even adequate classification of a vehicle is difficult too, because various factors may inhibit an accurate decision. For example, in one study, obscuring the wheels and half of the front window of
    a bus caused a machine learning-based system to classify the bus as a
    bicycle. A tank’s cannon might make it easy to distinguish from a school bus
    in an open environment, but not if trees or buildings obscure key parts of
    the tank, like the cannon itself.

    Perdix drone swarm test. A US Department of Defense swarming drone test. Credit: US Department of Defense.
    How many autonomous weapons are being used? More autonomous weapons means
    more opportunities for failure. That’s basic probability. But when
    autonomous weapons communicate and coordinate their actions, such as in a
    drone swarm, the risk of something going wrong increases. Communication
    creates risks of cascading error in which an error by one unit is shared
    with another. Collective decision-making also creates the risk of emergent error in which correct interpretation adds up to a collective mistake. To illustrate emergent error, consider the parable of the blind men and the elephant. Three blind men hear a strange animal, an elephant, had been
    brought to town. One man feels the trunk and says the elephant is thick like
    a snake. Another feels the legs and says it’s like a pillar. A third feels
    the elephant’s side and describes it as a wall. Each one perceives physical reality accurately, if incompletely, but their individual and collective interpretations of that reality are incorrect. Would a drone swarm conclude
    the elephant is an elephant, a snake, a pillar, a wall, or something else entirely?

    If a killer robot were used, would we know?
    Where are autonomous weapons being used? An armed, autonomous ground vehicle wandering a snow-covered Antarctic glacier has almost no chance of killing innocent people. Not much lives there and the environment is mostly barren
    with little to obstruct or confuse the vehicle’s onboard sensors. But the
    same vehicle wandering the streets of New York City or Tokyo is another
    matter. In cities, the AI system would face many opportunities for error: trees, signs, cars, buildings, and people all may jam up correct target assessment.

    Sea-based autonomous weapons might be less prone to error just because it
    may be easier to distinguish between a military and a civilian ship, with
    fewer obstructions, than it is to do the same for a school bus and an
    armored personnel carrier. Even the weather matters. One recent study found foggy weather reduced the accuracy of an AI system used to detect obstacles
    on roads to 58 percent compared to 92 percent in clear weather. Of course,
    bad weather may also hinder humans in effective target classification, so an important question is how AI classification compares to human

    How well tested is the weapon? Any professional military would verify and
    test whether an autonomous weapon works as desired before putting soldiers
    and broader strategic goals at risk. However, the military may not test for
    all the complexities that may confound an autonomous weapon, especially if those complexities are unknown. Testing will also be based on anticipated
    uses and operational environments, which may change as the strategic
    landscape changes. An autonomous weapon robustly tested in one environment
    may break down when used in another. Seattle has a lot more foggy days than Riyadh, but far fewer sandstorms.

    How have adversaries adapted? In a battle involving autonomous weapons, adversaries will seek to confound operations, which may not be very
    difficult. OpenAI—a world-leading AI company—developed a system that can classify an apple as a Granny Smith with 85.6 percent confidence. Yet, tape
    a piece of paper that says “iPod” on the apple, and the machine vision
    system concludes with 99.7 percent confidence the apple is an iPod. In one case, AI researchers changed a single pixel on an image, causing a machine vision system to classify a stealth bomber as a dog. In war, an opponent
    could just paint “school bus” on a tank or, more maliciously, “tank” on a school bus and potentially fool an autonomous weapon.

    How widely available are autonomous weapons? States and non-state actors
    will naturally vary in their risk tolerance, based on their strategies, cultures, goals, and overall sensitivity to moral trade-offs. The easier it
    is to acquire and use autonomous weapons, the more the international
    community can expect the weapons to be used by apocalyptic terrorist groups, nefarious regimes, and groups that are just plain insensitive to the error risk. As Stuart Russell, a professor of computer science at the University
    of California, Berkeley, likes to note: “[W]ith three good grad students and possibly the help of a couple of my robotics colleagues, it will be a term project to build a weapon that could come into the United Nations building
    and find the Russian ambassador and deliver a package to him.” Fortunately, technical acumen, organization, infrastructure, and resource availability
    will limit how sophisticated autonomous weapons are. No lone wolf terrorist will ever build an autonomous F-35 in his garage.

    Autonomous weapon risk is complicated, variable, and multi-dimensional—the what, where, when, why, and how of use all matter. On the high-risk end of
    the spectrum are autonomous nuclear weapons and the use of collaborative, autonomous swarms in heavily urban environments to kill enemy infantry; on
    the low-end are autonomy-optional weapons used in unpopulated areas as defensive weapons and only used when death is imminent. Where states draw
    the line depend on how their militaries and societies balance risk of error against military necessity. But to draw a line at all requires a shared understanding of where risk lies. --------------------------------------------------------------------------


    Who We Are
    Our Solutions


    Rotary Wing Attack Drone Loitering Munition System

    KARGU® is a rotary wing attack drone that has been designed for asymmetric warfare or anti-terrorist operations. It can be carried by a single
    personnel in both autonomous and manual modes.

    KARGU® can be effectively used against static or moving targets through its indigenous and real-time image processing capabilities and machine learning algorithms embedded on the platform.

    The system is comprised of the “Rotary-Wing Combat UAV (UCAV)” and “Ground Control Unit” components.

    KARGU®, which is included in the inventory of the Turkish Armed Forces,
    enables soldiers to detect and eliminate threats in a region, and can be
    used easily by the soldiers in the area without entering the risky areas, especially in asymmetric terrorist operations and asymmetric warfares.

    Kargu Logo



    Technical Features
    Download Brochure

    Capabilities | Competencies
    Reliable Day and Night Operation
    Autonomous and Precise Hit with Minimal Collateral Damage
    Different Ammunition Options
    Tracking Moving Targets
    High Performance Navigation and Control Algorithms
    Deployable and Operable by Single Soldier
    In-Flight Mission Abort and Emergency Self-Destruction
    Platform-tailored, advanced electronic ammunition safety, setup and trigger systems
    Disposal at Adjustable Altitude
    Indigenous National Embedded Hardware and Software
    Image Processing-Based Control Applications
    Embedded and Real-Time Object Tracking, Detection and Classification
    Ability to Load Ammunition Prior to Use
    10x Optical Zoom
    2 Axis Stabilised Indigenous POD
    User-Friendly Ground Control Unit interface

    Technical Features
    Range : 5 km
    Endurance : 30 minutes
    Mission Altitude : 500 meters
    Maximum Altitude : 2.800 meters (MSL)
    Maximum Speed : 72 km/hour
    Dimensions : 600mm x 600mm x 430mm
    Weight : 7.060 grams
    Operating Temperature : -20 / + 50 °C
    Kargu Teknik Ozellikler

    PDF - 255.06 KB
    Other Autonomous Drone Products
    TOGAN - Autonomous Multi-Rotor Reconnaissance UAV
    ALPAGU - Fixed Wing Loitering Munition System
    Autonomous Drone Projects
    Swarm Intelligence UAV Project
    KERKES Project


    Opposition to killer robots remains strong — poll
    January 28, 2021

    Opposition to killer robots remains strong — poll
    A new survey in 28 countries finds that more than three in five people
    oppose using lethal autonomous weapons systems, commonly called “killer robots.” 62% of respondents said they oppose the use of lethal autonomous weapons systems, while 21% support such use and 17% said they were not sure.

    Graphic shows the number of respondents who oppose the use of lethal
    autonomous weapons.
    Opposition was strong for both women (63%) and men (60%) although men are
    more likely to favor use of these weapons (26%) compared with women (16%). Opposition to killer robots was strong across generations and steadily increased with age, from 54% for those under 35 to 69% for ages 50 to 74.

    The survey, conducted in December 2020 by the market research company Ipsos
    and commissioned by the Campaign to Stop Killer Robots, indicates that even with COVID-19 and economic uncertainty dominating headlines in 2020, public awareness of and sentiment against the development of killer robots remains steady and strong.

    In response to these findings, Mary Wareham, coordinator of the Campaign to Stop Killer Robots, said:

    “States must launch negotiations to create a new treaty to retain meaningful human control over the use of force. Public opposition to killer robots is strong, which raises expectations of bold political action to ban them.”

    Opposition to killer robots has increased since 2017
    Opposition to killer robots increased in 13 of the 26 countries previously surveyed in 2018, with the biggest increases in Brazil (up 16% points from 2018), Israel (up 12%), Japan (11%), and South Africa (up 7%) followed by Australia and Sweden (both up 5%). This is the third Ipsos survey in six
    years to survey opposition to killer robots. The first survey conducted in
    2017 found that only 56% of those surveyed opposed killer robots. By 2020, opposition increased to 62%.

    Graphic shows what concerned respondents the most about lethal autonomous weapons.
    The 2020 Ipsos poll also asked those opposed to killer robots what concerned them the most. 66% answered that lethal autonomous weapons systems would
    “cross a moral line because machines should not be allowed to kill.” More
    than half (53%) said killer robots would be “unaccountable” and there is opposition (42%) due to concerns that killer robots would be subject to technical failures.

    Detailed results by country
    The 2020 Ipsos poll surveyed nearly 19,000 people, using samples of 500 to 1,000 people in each of the 28 countries: Argentina, Australia, Belgium, Brazil, Canada, China, Colombia, France, Germany, Great Britain, Hungary, India, Israel, Italy, Japan, Mexico, Netherlands, Norway, Peru, Poland,
    Russia, South Africa, South Korea, Spain, Sweden, Switzerland, Turkey, and United States.

    A majority of respondents in 26 countries opposed killer robots. The only countries where a majority of respondents did not oppose killer robots were France (47%) and India (36%).The strongest opposition was in Sweden (76%), Turkey (73%), Hungary (70%), Germany (68%), Norway (67%), and Mexico (66%).

    Graphic shows the countries with the strongest opposition to killer robots.
    In 21 countries, 59% or more of respondents were opposed: Sweden (76%),
    Turkey (73%), Hungary (70%), Germany (68%), Norway (67%), Colombia (66%), Belgium (66%, Mexico (66%), Spain (66%), South Africa (66%), Peru (65%),
    Poland (65%), South Korea (65%), Australia (64%), Brazil (62%), Canada
    (60%), Switzerland (60%), Argentina (59%), Italy (59%), Japan (59%), and the Netherlands (59%).

    Notably, a majority opposed killer robots in five countries most active in
    the development and testing of weapons systems with decreasing levels of
    human control: Russia (58%), UK (56%), US (55%), China (53%) and Israel

    All countries surveyed by Ipsos have participated since 2014 in diplomatic meetings on concerns raised by lethal autonomous weapons systems. Those
    talks have been stalled since November 2020, when the Convention on Certain Conventional Weapons (CCW) failed to agree on its program of work in 2021.

    “Public sentiment against fully autonomous weapons has not diminished,”
    Wareham said. “Now’s the time for strong preventive measures, not further diplomatic inaction.”

    For more updates on the Campaign to Stop Killer Robots, subscribe to our newsletter. Media enquiries can be directed to media@stopkillerrobots.org. -----------------------------------------------------------------------


    Indigenous kamikaze drone KARGU by STM appears in Libya
    by Fatih Mehmet Küçük May 28, 2020 in Land Forces and Land Systems 0 Indigenous kamikaze drone KARGU by STM appears in Libya
    Developed by Savunma Teknolojileri Mühendislik ve Ticaret A.S (STM), a prominent Turkish defense industry company, KARGU Autonomous Tactical Multi-Rotor Attack UAV was spotted on Wednesday, May 27 in Ain Zara, south
    of Tripoli, Libya.

    A number of social media accounts supporting Haftar alleged a drone/UAV departing from Mitigar Air Base was downed. Yet, as a result of the first examinations of the photos of the platform allegedly downed made by Defence Turk, it was revealed that the platform was not downed. Instead, what was
    seen on the photo indicated they were only some parts of the drone after attacking.

    Detection of KARGU from photos
    Kadir Dogan, author of Defence Turk, stated after his examinations that the parts in the photos belonged to KARGU.

    Parts in Libya
    According to Dogan, the parts in the photos;

    Foldable rotor,
    Foldable arms and legs
    Metal battery compartment
    case structure and dimensions of the structure are the parts of KARGU by STM that was spotted in Libya.

    Why is KARGU still intact despite being hit?
    When the structure of KARGU kamikaze UAV is examined, the condition of the front side, where the explosive and the camera are set, can be
    In the first examinations by Kadir Dogan, it turned out that the guided
    warhead and camera were not visible and these parts became separated from
    the rear side after burning / exploding. As a consequence, the explosive on
    the front side detonated while acquiring the target. However, the rear side remained structurally intact. Said to be the explosive head on various
    social media accounts, the silvery parts wrapped with folio were indeed the battery blocks.

    Pro-Haftar forces claimed a number of Bayraktar TB2 were downed
    The forces affiliated to putschist Khalifa Haftar disinform people on social media. In the previous months, Haftar supporters claimed they downed 26 Bayraktar TB2, which is a made-up story. When the images were analyzed, it
    came to light that some UAVs downed in the region were being carried to
    various locations in trucks and rephotographed and share as if they had been recently downed.

    KARGU is an Autonomous Tactical Multi-Rotor Attack UAV solution for asymmetrical warfare and anti-terror campaigns that can be deployed by
    single personnel and either autonomous or remote-controlled. KARGU can be
    used effectively against stationary or moving targets with real-time image processing and deep learning algorithms embedded on the platform.

    The system is composed of three components: “Rotary Wing Attack UAV”,
    “Ground Control Unit” and “UAV Charging Station” In our special news on IDEF-19, it was stated that, according to the information obtained by
    Defence Turk, STM Kargu Attack UAV has reached the last stage in its export
    to an unannounced country and has drawn great interest from a few countries
    to be exported to.

    While STM continues its marketing activities, the company is also in an
    attemp to make new developments for the products In this vein, the flight
    time of the renewed Kargu-2 has risen from 25 minutes to 30 minutes. In addition, a variety of design improvements were made on Kargu. ---------------------------------------------------------------------------


    Back to the Future: Autonomous Technology
    by Seray Güldane May 11, 2020 in Articles 0
    Back to the Future: Autonomous Technology

    Autonomous systems are an integral part of warfare and used for multiple
    roles during or prior to an operation. An autonomous aerial vehicle is
    capable of collecting intelligence, surveillance or reconnaissance. Even
    opted by non- state actors, UAVs have been presumed to lack the tenacity
    humans possess. Unreliable as UAVs are said to be, there have hitherto been
    a variety of trials to make them a platform that can match wits with humans, reaping the fruits of AI-powered systems in order to lower the burden and
    risks of using humans in the field.

    There are three groups of UAVs categorized by STM, a leading Turkish defense industry company.

    Class Category Altitude (ft) Examples
    Class I
    (Below 150 kg)

    Micro <200 Black Widow
    Mini <3,000 Malazgirt
    Small <5,000 Hermes 90
    Class II
    (150 to 600 kg)

    Tactical <10,000 Karayel, Bayraktar Tactical UAV

    Class III

    (Above 600 kg)

    Medium-altitude long endurance (MALE) <45,000 ANKA, Predator
    High-altitude long endurance (HALE) <65,000 Global Hawk
    Combat <65,000 X 47-B
    Levels of Autonomy
    According to NATO, levels of autonomy of UAVs are categorized as:

    Level 1: Remotely Controlled System – System reactions and behaviour depend
    on operator input Level 2: Automated System – Reactions and behaviour depend
    on fixed built-in functionality (preprogrammed)

    Level 3: Autonomous non-learning system – Behaviour depends upon fixed
    built-in functionality or upon a fixed set of rules that dictate system behaviour (goal-directed reaction and behaviour).

    Level 4: Autonomous learning system with the ability to modify rules
    defining behaviours – Behaviour depends upon a set of rules that can be modified for continuously improving goal directed reactions and behaviours within an overarching set of inviolate rules/behaviours.

    In operating UAVs, the duration of OODA -the loop of orient, observe, decide and act- loop sets the level an operator may partake in. Level of autonomy
    is measured by the moment ‘the button’ is pushed.

    Turkey and Autonomous Drones: STM

    STM offers a wide range of products with autonomous navigation, learning and decision making capabilities, both in a single-platform and swarm formation. STM’s competence on deep learning and computer vision facilitates real-time object detection, identification, tracking and classification.

    Capabilities | Competences

    High performance autonomous dispatch and control algorithms
    Artificial intelligence
    Machine learning
    Sophisticated computer vision and deep learning algorithms
    Vision-based control
    Embedded and real-time object tracking, detection and classification
    Real-time obstacle detection and avoidance
    Real-time localization and mapping
    Platform-tailored, advanced electronic ammunition safety, arm and fire
    KARGU | Autonomous Tactical Multi-Rotor Attack
    Kargu is a multi-rotor UAV solution that can be deployed and operated by
    single personnel. It features autonomous navigation, surveillance and reconnaissance abilities. The system can also be used to neutralize threats
    per operational requirements.

    Range: 5 km

    Endurance: 10 min

    Maximum Altitude: 1000 m

    Maximum Speed: 72 km/h

    Weight: 6,285 g

    ALPAGU | Fixed-Wing Autonomous Tactical Attack

    Alpagu is a fixed-wing UAV solution that can be deployed and operated by
    single personnel. It features autonomous navigation with surveillance and reconnaissance capabilities. The system can also be used to neutralize
    threats per operational requirements.

    Alpagu was

    Range: 5 km

    Endurance: 10 m

    [continued in next message]

    --- SoupGate-Win32 v1.05
    * Origin: fsxNet Usenet Gateway (21:1/5)