9. Military Applications

David J. Atkinson, Ph.DSenior Research Scientist

Presented at Air Force Research LaboratoryDayton, OH

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Autonomous Systems Tutorial: Part II

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Topics

Example Requirements

Military Application Domains

- Airbase Operations

- Intelligence

- Logistics

- Flight Operations

- Training

How will life change?

Technical and other challenges

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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

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Example Requirements• 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

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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

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Airbase Operations

Aircraft Support

Weapons Handling

Emergency First Responder

UXO Response

Security

Terminal Airspace Operations

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Aircraft Support

• Fueling

• Maintenance• Battle Damage Assessment

AFRL/RX Robotics Roadmap (2009)

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Weapons Handling

• Weapons Build-up• Transportation• Loading

XOS 2 Exoskeleton, Sarcos – Raytheon - to be deployed by 2016

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First Response

• First Responder Robotic Support (AFRL/RX)– Hazardous area search & rescue– Medical evacuation– Close-in firefighting– CBN agent neutralization

AFRL/RX Robotics Roadmap (2009)

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UXO Response

• Automated UXO Response (AFRL/RX)– 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

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Security

• Integrated Base Defense (AFRL/RX)– Integrated air, sea and ground robots– Conduct stand-off adversary challenge, identification,

delay/denial and neutralization– High degree of autonomy to perform tasks independently

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Security

• Protector (Singapore)– 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.

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Terminal Airspace Operations

Maybury (2011) “Remotely Piloted Aircraft”Unmanned Vehicle Systems Conference

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Intelligence

• Tasking• Reconnaissance• Surveillance• Analysis• Modeling

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Autonomous Tasking

Earth Observing-1 (NASA)

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

R. L. Sherwood et al., “Intelligent systems in space: the EO-1 Autonomous Sciencecraft,” 2005.

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Autonomous Reconnaissance

MAGIC 2010 (ARL, TARDEC, …)

Challenge: cooperating autonomousrobot teams that can execute anintelligence, surveillance and reconnaissancemission in a dynamic urban environment

**Very difficult challenge!

Accelerated UVS technologies for:

- Task allocation, multi-UVS controlmachine intelligence, tactical behaviordynamic planning, data/sensor fusionHMI, multi-aspect SA, and more

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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)

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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

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Logistics

• Convoys• Convoy Escort• Airlift / Mobility• Supply Chain• Warehousing and Distribution

KMAX cargo helicopter

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Autonomous Convoy

Convoy Active Safety Technology (CAST)Lockheed Martin Corporation

Builds on bestresults from DARPA Challenges

“Hen and Chicks”model:- one drivereveryone elsefollows!

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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

Wilkins, D.E., et al., Airlift mission monitoring and dynamic rescheduling, Engineering Applications of Artificial Intelligence (2007), doi: 10.1016/j.engappai.2007.04.001

Multi-agent architecture

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Flight Operations & Combat

Refueling

Collision avoidance

Delivery of Munitions

Search and Rescue

Ground Forces Support

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Aerial Refueling

Maybury (2011) “Remotely Piloted Aircraft”Unmanned Vehicle Systems Conference

Flight testing since 2006

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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?

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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.

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How Will RPAs Change?

Maybury (2011) “Remotely Piloted Aircraft”Unmanned Vehicle Systems Conference

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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

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Other Challenges

• 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

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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.

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Questions?