1

Scalable sWarms of Autonomous

Robots and Mobile Sensors

Vijay KumarUniversity of Pennsylvania

www.grasp.upenn.edu/~kumar

www.swarms.org

SWARMS

2

Motivation

Future military missions will rely on large, networked groups of resource-constrained vehicles and sensors operating in dynamic, environments E Pluribus Unum, In varietate concordia

Large groups will need to operate with little direct supervision Autonomy Human interaction at the group level

Biology provides many models and paradigms for group behaviors

SWARMS

3

DoD Relevance

Main Benefits Unmanned Inexpensive Scaleable

Potential Impact Adaptive communication networks for MOUT Chem/Bio search Reconnaissance and surveillance Minefield breaching

Resilient Secure

SWARMS

4

Biological Models (1)

Predator-prey model [Korf 1992] Moose: Moves to maximize its distance from

nearest wolf Wolves: Each wolf moves toward the moose

and away from nearest wolf

Pack of wolves surrounds larger and more powerful moose. Attack vulnerable spots while moose distracted.

SWARMS

5

Biological Models (2)Flocks of birds and schools of fish stay together, coordinate turns, and avoid obstacles and each other following 3 simple rules [Reynolds 1987]

Termites construct mounds as tall as 5 m to store food and house brood following 2 simple rules[Kugler 1990]

SWARMS

6

Biological Models (3)

Three simple rules Explore Rate nests Recruit

Tandem run; or Transport

Scalable Anonymity Decentralized Simple Franks et al, Trans. Royal Society, 2002

Information gatheringEvaluation

DeliberationConsensus building

Honey bees and ants scouting for nests

SWARMS

7

DoD Relevance and History

Scythians vs. Macedonians, Central Asian Campaign, 329-327 B.C.

Parthians vs. Romans, Battle of Carrhae, 53 B.C.

Seljuk Turks vs. Byzantines, Battle of Manzikert, 1071

Turks vs. Crusaders, Battle of Dorylaeum, 1097

Mongols vs. Eastern Europeans, Battle of Liegnitz, 1241

Woodland Indians vs. US Army, St. Clair’s Defeat, 1791

Napoleonic Corps vs. Austrians, Ulm Campaign, 1805

Boers vs. British, Battle of Majuba Hill, 1881

German U-boars versus British convoys, Battle of the Atlantic, 1939-1945

Swarming and the Future of Conflict, RAND, 2000

SWARMS

8

History (continued)

Somali insurgents vs. US commandos, Battle of the Black Sea, 1993

British swarming fire harried invasion fleet Spanish Armada in 1588

German U-boat wolfpack attacks that converged on convoys in WWII Battle of the Atlantic

Swarming Soviet anti-tank networks played significant role in defeating the German blitzkrieg in the Battle of Kursk

SWARMS

9

The SWARMS Team

Ali Jadbabaie, Daniel E. Koditchek, Ali Jadbabaie, Daniel E. Koditchek, Vijay Kumar (PI), and George PappasVijay Kumar (PI), and George Pappas

A. Stephen Morse A. Stephen Morse David SkellyDavid Skelly

Francesco BulloFrancesco Bullo Daniela RusDaniela Rus

S. Shankar SastryS. Shankar Sastry

Center for Systems Science Center for Systems Science Yale UniversityYale University

GRASP Laboratory GRASP Laboratory University of PennsylvaniaUniversity of Pennsylvania

University of California University of California Santa BarbaraSanta Barbara

CITRIS, University of CITRIS, University of California BerkeleyCalifornia Berkeley

CSAIL, Massachusetts CSAIL, Massachusetts Institute of TechnologyInstitute of Technology

SWARMS

10

Previous Work

Talk about our collective work The limitations Why they provide logical starting points for new work Outline

1. MARS Demo

2. Acclimate (Shankar?)

3. EMBER (Daniela)

4. Francesco, Steve’s work

11

Fort Benning Demonstrationof Networked

Robots

McKenna MOUT SiteDecember 1, 2004

Research supported by DARPA, ARO (Acclimate), ONR

Joint demonstration with Georgia Tech, USC, BBN, and Mobile Intelligence

SWARMS

12

ObjectiveNetwork-centric force of heterogeneous platforms Provide situational awareness for remotely-located war

fighters in a wide range of conditions Adapt to variations in communication performance Integrate heterogeneous air-ground assets in support of

continuous operations in urban environments

SWARMS

13

SWARMS

14

McKenna MOUT Site

QuickTime™ and aH.263 decompressor

are needed to see this picture.

SWARMS

15

Main Accomplishments

Single operator tasking a heterogeneous team of robots for persistent surveillance

Network-centric approach to situational awareness Independent of who is where, and who sees what Fault tolerant

Decentralized control

But…Robots are identified

Control involves maintaining “proximity graph”

Sharing of information

SWARMS

16

Cooperative search, identification, and localization

ARO, ACCLIMATE Project

Grocholsky, et al, 2004

SWARMS

17

Information Model Approximate model

SWARMS

19

Confidence Ellipsoids

+ =

SWARMS

20

UGV Trajectory

SWARMS

21

Decentralized control, but shared information…

QuickTime™ and aH.263 decompressor

are needed to see this picture.

SWARMS

22

ACCLIMATE

SWARMS

23

EMBER

SWARMS

24

Steve

SWARMS

25

Francesco

SWARMS

26

Taxonomy of Approaches

Centralized Decentralized

Vehicles identified

1. Guarantees on performance2. Optimality

Identical vehicles Anonymity, RobustnessS

cala

bl

e

Our Goal

27

Scaling up to a Swarm Paradigm

SWARMS

28

Three Overarching Themes Decentralized Anonymity Simple individuals, but versatile group

SWARMS

29

SWARMS Objective

Create a research community of biologists, computer scientists, control theorists, and roboticists

Systems-theoretic framework for swarming Modeling and analysis of group behaviors observed in

nature Analysis of swarm formation, stability and robustness Synthesis: Formation and navigation of artificial

Swarms Sensing and communication for large, networked

groups of vehicles Testbeds, demonstrations, and technology transition

SWARMS

30

SWARMS Objective

Create a research community of biologists, computer scientists, control theorists, and roboticists

Systems-theoretic framework for swarming Modeling and analysis of group behaviors observed in

nature Analysis of swarm formation, stability and robustness Synthesis: Formation and navigation of artificial

Swarms Sensing and communication for large, networked

groups of vehicles Testbeds, demonstrations, and technology transition

Block Island Workshop on Cooperative Control, June 10-11, 2003

Workshop on Swarming in Natural and Engineered Systems, August 3-4, 2005

SWARMS

31

SWARMS Research Agenda

SWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMS SWARMS SWARMS SWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMS

SWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMS SWARMS SWARMS SWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMSSWARMS SWARMS

Biology

AI

Robotics

OrganismBehaviors

SwarmArchitectures

VehicleModels

Modeling

Synthesis

Analysis

Theory of Swarming

Multi-vehicleSensing/Control

Novel Testbeds

M1, M2

A1, A2, A3

S1, S2, S3

V1, V2, V3

E1, E2, E3

T1, T2

SWARMS

32

SWARMS Research Agenda

1. System-Theoretic Framework (T) formal language of swarming behaviors

with a grammar for composition; new formalisms and mathematical

constructs for describing swarms of agents derived from the unification of methods drawn from graph theory, switched dynamical systems theory and geometry. Francesco Bullo

A. Stephen MorseGeorge Pappas

SWARMS

33

SWARMS Research Agenda

2. Modeling (M) model-based catalog of biological

behaviors and groups with decompositions into simple behaviors and sub groups;

techniques for producing abstractions of high-dimensional systems and software tools for developing low-dimensional abstractions of observed biological group behaviors. Vijay Kumar

David Skelly

SWARMS

34

Swarming in Nature

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

1 10 100 1000 10000 100000 1000000Group Size (m)

Body Size (m)

bacteria (quorum sensing)

zooplankton (feeding)

harvester ants (various)

social wasp (colony foundation)

krill (feeding)dwarf mongoose (navigation)

spotted hyena (navigation)

killer whale (navigation)bluefin tuna (feeding)

lion (navigation)

SWARMS

35

SWARMS Research Agenda

3. Analysis (A) stability and robustness analysis tools

necessary for the analysis of swarm formation; analysis of asynchronous functioning systems

and abstractions to a single synchronous process; and

theory for computability and complexity for swarming facilitating the design of scalable algorithms.

Francesco BulloAli JadbabaieA Stephen Morse

SWARMS

36

SWARMS Research Agenda

3. Analysis Organization Foraging Defense Navigation Logistical Attributes Species

Centralized X Dwarf Mongoose

X X X Lion

X Social Wasps

X Killer Whale

X X Chimpanzee

X X Spotted Hyena

Decentralized X Quorum Bacteria

X X X X Self Org Social Caterpillars

X Self Org Great Gerbils

X X Self Org Herring

X Self Org Frog Larvae

X Self Org Social Wasps

X Self Org Barn Swallow

X Self Org Spiny Lobster

X Self Org Honeybee

X X X Self Org Krill

X Self Org Canada Geese

X X Self Org Harvester Ants

X X Self Org Termites

X Self Org Honeybee

X Template Wasp

X Template Ant

Goal

SWARMS

37

SWARMS Research Agenda

4. Synthesis (S) design paradigms for the specification of cost

functions and coordination algorithms for high-level behaviors for navigation, clustering, splitting, merging, diffusing, covering, tracking, and evasion;

distributed control algorithms with constraints on sensing, actuations and communication; and

software toolkit for composition of cataloged behaviors and decomposition of synthesized behaviors with the ability to automatically infer properties of resulting behaviors.

Ali JadbabaieDan Koditschek

SWARMS

38

SWARMS Research Agenda

5. Sensing and communication (V) estimators for vehicle and sensor

platforms to localize individual agents and groups of agents;

algorithms for coordinated control in support of localization and information diffusion; and

bio-inspired, sensor-based (communication-less) strategies for coordination of a swarm of vehicles.Vijay Kumar

Daniela RusShankar Sastry

SWARMS

39

SWARMS Research Agenda

6. Testbeds, Demonstrations and Technology Transition (E)

adaptive network of micro-air vehicles for aerial surveillance of an urban environment;

self-healing swarm of ground vehicles (and sensor platforms) for threat and intrusion detection; and

swarms of UAVs, micro-air vehicles, and small ground vehicles for operation in urban environments.

Daniel KoditschekVijay KumarGeorge PappasDaniela RusShankar Sastry

SWARMS

40

Alliances

ARO Institute of Collaborative Biotechnology

Industry Lockheed Martin (Penn) Honeywell (Berkeley/Penn) UTRC (Berkeley) Boeing (MIT/Penn)

DoD Labs AFRL, ARL, NRL

SWARMS

41

Conclusion

SWARMS will develop the basic science and technology for deploying resilient, secure teams of inexpensive, unmanned vehicles

Applications Adaptive communication networks Search, reconnaissance, surveillance

missions