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9. Military Applications

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1 9. Military Applications
Autonomous Systems Tutorial: Part II 9. Military Applications David J. Atkinson, Ph.D Senior Research Scientist Presented at Air Force Research Laboratory Dayton, OH 1/12/12

2 Topics Example Requirements Military Application Domains
- Airbase Operations - Intelligence - Logistics - Flight Operations - Training How will life change? Technical and other challenges 1/12/12 D. Atkinson

3 Observations 65 countries now use military robots or are in the process of acquiring them Control schemes vary from tele-operation to semi- autonomous depending on task and mission All are candidates for greater autonomy 1/12/12 D. Atkinson

4 Example Requirements 1/12/12 D. Atkinson
Long-term independent operation Transit long distances, detect, assess and avoid threats, report back Adaptive Functionality Recognize threats, respond, replan to complete mission Flexible to changing mission requirements, dynamic adversaries Minimize real-time telecommunications Push signal processing and decision-making to lowest level where it can be successfully accomplished Weaponize within constraints of law, precedent, and procedure Common control and interoperability Cooperative / Collaborative coordination among multiple heterogeneous systems (autonomous and man-machine) Minimize frequency and complexity of operator interaction Allow human operators to interact with the system on multiple levels, in a variety of roles Operator “on-the-loop” cooperative planning 1/12/12 D. Atkinson

5 Military Application Domains
Airbase Operations Emergency First Responder UXO Response Security Weapons Handling Aircraft Support Airfield Maintenance Terminal Airspace Operations Intelligence Tasking Reconnaissance Surveillance Analysis Modeling Command & Control Assess, plan, act Logistics Supply Chain Warehousing and Distribution Convoys Flight Operations and Combat Collision avoidance Refueling Delivery of Munitions Search and Rescue Ground Forces Support Training 1/12/12 D. Atkinson

6 Airbase Operations Aircraft Support Weapons Handling
Emergency First Responder UXO Response Security Terminal Airspace Operations 1/12/12 D. Atkinson

7 Aircraft Support Fueling Maintenance Battle Damage Assessment 1/12/12
D. Atkinson AFRL/RX Robotics Roadmap (2009)

8 Weapons Handling Weapons Build-up Transportation Loading 1/12/12
XOS 2 Exoskeleton, Sarcos – Raytheon - to be deployed by 2016 1/12/12 D. Atkinson

9 First Response First Responder Robotic Support (AFRL/RX) 1/12/12
Hazardous area search & rescue Medical evacuation Close-in firefighting CBN agent neutralization 1/12/12 D. Atkinson AFRL/RX Robotics Roadmap (2009)

10 UXO Response Automated UXO Response (AFRL/RX) 1/12/12 D. Atkinson
Investigate and eliminate explosive threats including UXOs, IEDs on runways, at entry control points, and clear ordnance from ranges. Multiple cooperating UGVs to detect and dispose of UXOs 1/12/12 D. Atkinson

11 Security Integrated Base Defense (AFRL/RX) 1/12/12 D. Atkinson
Integrated air, sea and ground robots Conduct stand-off adversary challenge, identification, delay/denial and neutralization High degree of autonomy to perform tasks independently 1/12/12 D. Atkinson

12 Security 1/12/12 D. Atkinson Protector (Singapore) SGR-A1 (S. Korea)
USV for protection against suicide boats On-board munitions (explosives, guns) Designed to investigate a suspicious boat, provide warning, and attack if necessary (currently tele-operated; autonomy planned) SGR-A1 (S. Korea) Semi-autonomous gun tower for guard duty on defensive lines Optical, laser and thermal sensing, voice recognition LMG, grenade launcher, gas canisters Autonomously detect human targets to 4km, track at 2km, fire on target autonomously or with human in-loop. 1/12/12 D. Atkinson

13 Terminal Airspace Operations
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference 1/12/12 D. Atkinson

14 Intelligence Tasking Reconnaissance Surveillance Analysis Modeling
1/12/12 D. Atkinson

15 Autonomous Tasking Earth Observing-1 (NASA) Autonomous capabilities:
One node in space/ground sensor web Autonomous capabilities: Recognize features of interest in Land, ice, snow, water, thermally hot Recognize change relative to previous observations Flooding, volcano, ground deformation On-board wide-area “search” for interesting features On-board decision-making to re-task sensors to specific targets Downlink only data of interest 1/12/12 D. Atkinson R. L. Sherwood et al., “Intelligent systems in space: the EO-1 Autonomous Sciencecraft,” 2005.

16 Autonomous Reconnaissance
MAGIC 2010 (ARL, TARDEC, …) Challenge: cooperating autonomous robot teams that can execute an intelligence, surveillance and reconnaissance mission in a dynamic urban environment **Very difficult challenge! Accelerated UVS technologies for: - Task allocation, multi-UVS control machine intelligence, tactical behavior dynamic planning, data/sensor fusion HMI, multi-aspect SA, and more 1/12/12 D. Atkinson

17 Autonomous Surveillance
Typical requirement is “constant stare”: the ability to surveil a target area (near) continuously SWARM II (Australia DSTO) Brian D. O. Anderson, ANU/NICTA Autonomous capabilities: Autonomous multi-vehicle formation and control Cooperative passive radar/emitter localization Sensor network self-localization (partial GPS denial) 1/12/12 D. Atkinson

18 Autonomous Control Capability
Understands the commander’s intent with respect to missions / objectives Understands the battlespace (including events, activities, entities, and networks of entities) based on data that it has collected or to which it has access through other sources Assesses this knowledge in order to determine what the shortfalls and threats are in the knowledge of the battlespace and threats therein relative to the commander’s intent Optimally (with regard to resources, time, and significance) determines / evaluates options for courses of actions and self-tasks specific components of the sensor(s) network to address these shortfalls and threats Executes the taskings while adapting to changing conditions and being self-aware and team-aware Alerts appropriate forces or commands to engage critical threats Junkers/ONR 1/12/12 D. Atkinson

19 Logistics Convoys Convoy Escort Airlift / Mobility Supply Chain
Warehousing and Distribution KMAX cargo helicopter 1/12/12 D. Atkinson

20 Autonomous Convoy Convoy Active Safety Technology (CAST) 1/12/12
Lockheed Martin Corporation Builds on best results from DARPA Challenges “Hen and Chicks” model: - one driver everyone else follows! 1/12/12 D. Atkinson

21 Airlift / Mobility Flight Manager Assistant (SRI and CMU)
For Air Mobility Command and AFRL (Integrated Flight Management Program Capabilities: Mixed initiative real-time flight management Autonomous monitoring of progress vs. schedule Autonomous responses to anomalies (when permitted) Dynamic rescheduling for globally coherent recovery and minimal disruption to other missions Multi-agent architecture 1/12/12 D. Atkinson Wilkins, D.E., et al., Airlift mission monitoring and dynamic rescheduling, Engineering Applications of Artificial Intelligence (2007), doi: /j.engappai

22 Flight Operations & Combat
Refueling Collision avoidance Delivery of Munitions Search and Rescue Ground Forces Support 1/12/12 D. Atkinson

23 Aerial Refueling 1/12/12 D. Atkinson Flight testing since 2006
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference 1/12/12 D. Atkinson

24 Munitions Wide area loitering attack munitions
Many in development for >5 years Low Cost Autonomous Attack System (LOCAAS) Autonomous navigation to destination Area loitering Autonomous ID of high/low priority targets Autonomous target selection and attack Miniaturized autonomous munitions Target identified in “rifle” scope Data transferred to munition when fired Autonomous target recognition, final trajectory adjustment concept stage? 1/12/12 D. Atkinson

25 Training Transitional Online Post-Deployment Soldier Support in Virtual Worlds (TOPSS-VW) (RDECOM) Designed to assist soldiers who are post-deployment and reintegrating to civilian life Uses virtual world technology and “virtual humans” (humanoid agents) who serve as informed guides and help each person determine what might be of most benefit to them; tutoring and mentoring Virtual Human capabilities: Natural language Natural gestures User modeling ... Jacquelyn Ford Morie, “Re-Entry: Online worlds as a healing space for veterans“, presented at the Engineering Reality of Virtual Reality 21st Annual IS&T/SPIE Symposium, San Jose, CA. January 2009. 1/12/12 D. Atkinson

26 How Will RPAs Change? 1/12/12 D. Atkinson
Maybury (2011) “Remotely Piloted Aircraft” Unmanned Vehicle Systems Conference 1/12/12 D. Atkinson

27 Technical Challenges Not just sensing → Perception in real-time for effective decision-making and action Testing → Existing V&V processes are insufficient Trust → We can't prove it won't do something bad Interoperability An unmanned system built for the Army by one contractor cannot today seamlessly interact with another robotic system built for the Navy by another contractor. Collaboration assumptions All the unmanned systems have the same level of autonomy and s/w architecture; need the ability to introduce an unknown, autonomous system to a “team” without having to reconfigure all the robots 1/12/12 D. Atkinson

28 Other Challenges 1/12/12 D. Atkinson Clash of cultures
Force structure issues Inefficiencies created by duplicative activities for similar functions Coordination across current activities and domains is not robust stakeholders unaware of other's efforts parochialism Pockets of advocacy but no broad spectrum acceptance no consistent top level advocacy (at Service HQ level) Trust of unmanned systems is still in infancy in ground and maritime domains. Stronger in air domain but still difficult to fly in US airspace Lack of stable and robust industrial base Shortage of qualified engineers 1/12/12 D. Atkinson

29 Conclusions The needs for future military systems drives well beyond today’s familiar deterministic sequence- driven, centralized software systems towards highly distributed and intelligent systems that are capable of functioning independently as an element of a coordinated team and in close partnership with one or more humans. Such systems are likely to manifest autonomy (self control) and inevitably will become “robotic” in the sense that they are given the capability to directly interpret – and control – sensors, and to take action in the world. 1/12/12 D. Atkinson

30 Questions? 1/12/12 D. Atkinson

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