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Cooperative UAVs for Remote Data Collection and Relay

Kevin L. Moore Michael J. White, Robert J. Bamberger,and David P. Watson

Systems and Information Science GroupResearch and Technology Development Center

Johns Hopkins University Applied Physics Laboratory

presented at

2005 AUVSI North America Conference and ExpoBaltimore, Maryland

June 2005

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Outline

Dynamic Surveillance NetworksDSNs for Data Exfiltration– Project Concept– Hardware– Architecture

DSN Algorithmic Approach– Consensus Variables– Global Optimization via Coordination

Preliminary ResultsConclusions

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Dynamic Surveillance Networks

Network of “entities”– Communication infrastructure– Entity-level functionality– Implied global functionality– Not necessarily homogeneous

Surveillance: – Primarily considering entities that are sensors– “Bigger picture” includes actors as well

Dynamic– Entities may be mobile– Communication topology might be time-varying– Data actively and deliberately shared among entities– Decision-making and learning

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Motivating Example 1

Autonomous swarm for plume tracking

Sensor-carrying UAVs and UGVsassess and track the developmentof a hazardous plume resulting from a CBR terrorism act.

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Motivating Example 2

Autonomous confederation building, adaptive to changes in battlefield conditions

Confederation A

Confederation B

Target Cluster ATarget Cluster B

Confederation A

Confederation B


Confederation A

Confederation B


Confederation A

Confederation B


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Outline




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DSN Data Exfiltration Project

Demonstrate UAVs data collection from unattended ground sensors– Clusters of low-power, unattended sensors– Cluttered urban environment– Cooperating UAVs for fly-by data collection

Payload limits restrict rangeRun-time issues dictate need for “smart” operation

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Data exfiltration applicationSensor clusters are deployed “by hand”Not all clusters can communicate with each otherExact cluster locations are not known

UAVs execute a cooperative search to find the clusters− Search begins with a pre-planned raster scan− Search is refined based on cluster discovery results

After discovery, UAVs cooperate to collect data from all the clusters in some optimal way

− UAVs configure to provide maximum coverage of clusters or

− An optimal path is planned to travel between clusters

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Adaptation occurs in response to changes in the UAV resources or the sensor clusters:

− If a sensor cluster is lost the UAVscooperatively reconfigure

− Periodically, one of the UAVs returns to the base station to relay data from the senor clusters; when this happens the remaining UAV automatically re-plans its operations


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Leverages Several Previous APL Efforts– UAV for fly-by data collection– UAV for data-hopping between ground nodes– Coordinated (central control) of multiple UGVs– Cooperative control of UGVs for search applications– Communication framework for cooperative autonomy networks

(802.11-based) – Distributed (swarm-like) UGVs and UAVs demonstrating emergent

behavior:Heterogeneous Mix of Small Ground & Air VehiclesCo-Fields Behavior AlgorithmsCommon Supervisory Control ArchitectureCollaborative Field Testing with Army Research Lab, Aberdeen.Initial Focus on Distributed ISR Application

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DSN Hardware/Communications Architecture

PiccoloAutopilot

900 MHzWireless DataTransceiver

72 MHz R/C

GPS

Antenna

Wirelesscommunications

RS232 cable

72 MHzFutaba

Receiver

Ethernet cable

What we added

Normal Setup

Applied Data SystemsBitsy Plus Single Board

Computer

UAV Fuselage

2.4 GHz 802.11b CiscoPCMCIA Client Adapter

APL GroundStation

2.4 GHz802.11b NetworkCommunications

Second UAVCisco Access

PointLaptop

COTS UAV Setup

PiccoloGroundStation

900 MHz PiccoloTelemetry Link

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DSN Architecture Concept

Servos

Reactive Control

Advanced Autopilot

VehicleSensors

TraditionalAutopilot

with Add-ons

Airframe

External Environment

Waypoints

GPS IMU

Communication System

Mission Payload(Sensor)

Users, Other Vehicles

ReactiveSensors

Arbitrator

Data

Supervisory Control

LocalDecision

Logic

Cooperative Decision

Logic

Autonomy Board

Future work

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DSN Supervisor

Loiterw/Negotiate

Manual

Loiter w/Data Transfer

Loiter w/Exfilt

Loiter w/Exfilt &Negotiate

A/FP,DCV

M

MM

M

E/FP,DCV

T/FP,DCV

E/FP,DCV

ND/FP,DCV

CR/FP,DCV

ND/FP,DCV

E/FP,DCV

Input/Output1,Output2, …

Inputs: A AutomaticM ManualND Negotiation DoneCR Change RequestT TransferTP Transfer CompleteE Error

Outputs: FP Flight PlanDCV Data Collection Variable

T/FP,DCV

Each state has its ownsub-state machine

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Outline




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DSN Algorithmic Approach

Bigger picture is coordination and control of multiple, cooperating, heterogeneous entities:

Our technical approach, a generalization of potential field approaches, is based on so-called consensus variables and has connections to problems in:

– Coupled-oscillator synchronization

– Neural networks

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1 2

3

4

56

89

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Consensus Variable Perspective

Assertion: – Multi-agent coordination requires that some information must be shared

The idea:– Identify the essential information, call it the coordination or consensus

variable.– Encode this variable in a distributed dynamical system and come to

consensus about its valueExamples:

– Heading angles– Phase of a periodic signal– Mission timings

In the following we build on work by Randy Beard, et al., at BYU, to use consensus variables to solve global problems in a distributed fashion

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Consensus Variables

Suppose we have N agents with a shared global consensusvariableEach agent has a local value of the variable given as Each agent updates their local value based on the values of the agents that they can communicate with

where are gains and defines the communication topology graph of the system of agentsKey result from literature: If the graph has a spanning tree then for all i

ijk ijG

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Example: Single Consensus Variable

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3

4

56

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9

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DSN Application

Example 2: Spatially locate group of resources (green) to optimally cover a group of targets (targets)

Targets and resources have some assumed strength and capability, respectively

Resources Targets

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DSN Application

Corresponding node equations become:

where h

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DSN Application

4 targets, 2 resources


4 targets, 1 resource


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Outline




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Preliminary Test Flights at ARL-Aberdeen

N

300 ft100 m

1120B

MarshArea

Water

Water

1111

1112

1129

11271119B

S3

S2

S1

AGS

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Waypoint Tracking and “Pinging”

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39.482Connectivity Map

Longitude

Latit

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-76.086 -76.0855 -76.085 -76.0845 -76.084 -76.0835 -76.083 -76.0825 -76.08239.472

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Latit

ude

Longitude

Connectivity Map

-76.0855 -76.085 -76.0845 -76.084 -76.0835 -76.083 -76.0825 -76.08239.473

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Longitude

Latit

ude

Autonomous Flight - Tracking Two Waypoints

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Outline

Dynamic Surveillance NetworksDSNs for Data Exfiltration

– Project Concept– Hardware– Architecture



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Concluding Comments

We have presented the idea of dynamic surveillance networks– Special case of a more general notion of resource networks

We discussed a specific DSN concept: data exfiltration from UGS using UAVs and presented our hardware and architectureWe presented an algorithmic approach to coordination for the data exfiltration problem

– Based on the idea of consensus variables– We have discussed extensions of these ideas to the problem of

global optimization via cooperating distributed entitiesThese ideas are being applied to our dynamic surveillance network project

– Preliminary results showed the ability to fly autonomously usingour autonomy flight board and to successfully communicate between UAVs and ground nodes

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