grasp university of pennsylvania nrl logo? autonomous network of aerial and ground vehicles vijay...

GRASPUniversity of Pennsylvania

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Autonomous Network of Aerial and Ground Vehicles

Vijay Kumar

GRASP Laboratory

University of Pennsylvania

Ron Arkin

College of Computing

Georgia Tech

Autonomous Operations Future Naval Capability Unmanned Systems Technology Review


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Adaptive Autonomous Robot TEAMS for Situational Awareness

Vijay Kumar

Univ Penn

Gaurav Sukhatme

USC

Ron Arkin

Georgia Tech

Jason Redi

BBN

A MARS 2020 project

Autonomous Operations Future Naval Capability Unmanned Systems Technology Review


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Future Combat SystemsCreation of a network-centric force of heterogeneous platforms that is strategically responsive and sustainable

Adapt to variations in communication performance and strive to maximize suitably defined network-centric measures for perception, control and communicationProvide situational awareness for remotely-located war fighters in a wide range of conditionsIntegrate heterogeneous air-ground assets in support of continuous operations over varying terrain


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Air Ground CoordinationGROUND VEHICLES

AERIAL VEHICLES

C2 VEHICLE


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Motivation

U A V


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Motivation


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Context

Communication Network 400 MHz (100Kbs), 2.4 GHz (10Mbs), 38 GHz (100

Mbs) Affected by foliage, buildings, terrain features,

indoor/outdoor Directionality

Small Team of Heterogeneous Robots UGVs with vision, range finders UAVs (blimp, helicopter)


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Research Thrusts

1. Localization and Control for the Team of Robots

2. Communication Sensitive Mission Planning

3. Communication Sensitive Reactive Behaviors

4. Verification and Validation


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1. Localization, Tracking and Control for a (small) Network of Robots

Decentralized Each robot computes the others position

relative to its reference frame Each robot shares information with the rest of

the team

Assumptions Noise can be modeled by normal distribution No dynamics Ad-hoc network Robots are easily identified


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Global Estimators for Position and OrientationFinding the optimal solution involves a graph search problem

NP-complete

or optimization over 3(N-1) vars. Local minima

Suboptimal solution Each robot calls out its observations Solve for orientations Solve for relative positions

O(N3 ), but closed form

Breadth-first search enables O(N) approximation

R2

R3

R4

R5

R1

R9

R6

Self-organization, adaptation based on the spanning tree[Das, Spletzer, Kumar, Taylor 02]


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2. Communication Sensitive Planning Provide support for terrain models and other

communications relevant topographic features to MissionLab

Use plans-as-resources as a basis for multiagent robotic communication control (spatial, behavioral, formations, etc.) and integrate within MissionLab


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Preliminary Results: Communication Planning

Additional resources in the form of internalized plans aids team communication.

No difference results when using reactive behaviors vs. communication insensitive plans.

Communication planning in serial and parallel result in significant improvement in communication.


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3. Communication Sensitive Team Behaviors

Reactive communications preserving and recovery behaviors

Communications recovery and preserving behaviors sensitive to QoS

Behaviors in support of line-of-sight and subterranean operations


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Control for Communication

Modeling of Communication Networks Effect of foliage Buildings Dependence on frequency, directionality Statistical models of delays and “hot spots” from

experimental data Neighbors, path costs (delays, power) Time of last communication

QoS metrics Control/perception tasks Individual robots vs. end-to-end Move to improve reliability and network performance


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Simulation

Six robots maintaining communication constraints and avoiding each other


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Communication Sensitive Behaviors: Preliminary Results

Using the Nearest Neighbor Recovery behavior approximately 50% of the trials were finished completely autonomously

Retrotraverse and Move to Higher Ground were usually not able to finish the trials autonomously by themselves and will require transitions/planning once communications recovered

Number of Trials Completed

0

11

0

18

02

8

19

02468

101214161820

Communication Sensitive Behaviors

Tri

als

Co

mp

lete

d

No Preserving, No Recovery

No Preserving, Higher Ground

No Preserving, Nearest Neighbor

No Preserving, Retrotraverse

Maintain Signal Strength, No Recovery

Maintain Signal Strength, Higher Ground

Maintain Signal Strength, Retrotraverse

Maintain Signal Strength, Nearesr Neighbor


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SatisfyConstraints

MaintainConstraints

4. Verification and Validation

UnconstrainedBehaviors


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Guarantees for Special Cases

Complete Graph Tree In-degree constraint Directed

[Pereira et al, IROS 03]

Hamiltonian Cycle (or Path) In-degree constraint Undirected

[Pereira et al, NRL MRS 03]

1. Reactive Controllers are Potential Field Controllers

2. Assumptions on “constraint” graph


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Three Robot Experiments:Satisfying Constraints


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Deploying a robot network


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Effect of Robot Failures

R3R1 R2 R3R1 R2

Control Graph Constraint Graph

R3R1 R2

Formation Graph

R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2 R3R1 R2+ =


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Conclusion A comprehensive model and framework

integrating communications, perception, and execution

Automated acquisition of perceptual information for situational awareness

Reactive group behaviors for a team of air and ground based robots that are communications sensitive

grasp university of pennsylvania nrl logo? autonomous network of aerial and ground vehicles vijay...

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