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S calable s W arms of A utonomous R obots and M obile S ensors. Vijay Kumar University of Pennsylvania www.grasp.upenn.edu/~kumar. www.swarms.org. Motivation.

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s calable s w arms of a utonomous r obots and m obile s ensors

Scalable sWarms of Autonomous Robots and Mobile Sensors

Vijay Kumar

University of Pennsylvania

www.grasp.upenn.edu/~kumar

www.swarms.org

motivation
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

dod relevance
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

biological models 1
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

biological models 2
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

biological models 3
Biological Models (3)

Honey bees and ants scouting for nests

Information gathering

  • Three simple rules
    • Explore
    • Rate nests
    • Recruit
      • Tandem run; or
      • Transport
  • Scalable
    • Anonymity
    • Decentralized
    • Simple

Evaluation

Deliberation

Consensus building

Franks et al, Trans. Royal Society, 2002

SWARMS

dod relevance and history
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

history continued
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

the swarms team
The SWARMS Team

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

A. Stephen Morse David Skelly

GRASP Laboratory University of Pennsylvania

Center for Systems Science Yale University

Francesco Bullo

Daniela Rus

University of California Santa Barbara

CSAIL, Massachusetts Institute of Technology

S. Shankar Sastry

CITRIS, University of California Berkeley

SWARMS

previous work
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

SWARMS

fort benning demonstration of networked robots

Fort Benning Demonstrationof Networked Robots

McKenna MOUT Site

December 1, 2004

Research supported by DARPA, ARO (Acclimate), ONR

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

objective
Objective

Network-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

main accomplishments
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

cooperative search identification and localization
Cooperative search, identification, and localization

Grocholsky, et al, 2004

ARO, ACCLIMATE Project

SWARMS

acclimate
ACCLIMATE

SWARMS

ember
EMBER

SWARMS

steve
Steve

SWARMS

francesco
Francesco

SWARMS

taxonomy of approaches

Scalable

Anonymity, Robustness

Taxonomy of Approaches

Our Goal

SWARMS

three overarching themes
Three Overarching Themes
  • Decentralized
  • Anonymity
  • Simple individuals, but versatile group

SWARMS

swarms objective
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

swarms objective1
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

swarms research agenda

Theory of

Swarming

SWARMS SWARMS

SWARMS SWARMS

SWARMS SWARMS

SWARMS SWARMS

SWARMS SWARMS

SWARMS SWARMS

SWARMS SWARMS

Biology

Organism

Behaviors

Modeling

T1, T2

M1, M2

Multi-vehicle

Sensing/Control

AI

Analysis

Swarm

Architectures

A1, A2, A3

V1, V2, V3

Synthesis

Robotics

Vehicle

Models

Novel

Testbeds

S1, S2, S3

E1, E2, E3

SWARMS Research Agenda

SWARMS

swarms research agenda1
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

Stephen Morse

George Pappas

SWARMS

swarms research agenda2
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

swarms research agenda3
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 Bullo

Ali Jadbabaie

A Stephen Morse

SWARMS

swarms research agenda4
SWARMS Research Agenda

3. Analysis

SWARMS

swarms research agenda5
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 Jadbabaie

Dan Koditschek

SWARMS

swarms research agenda6
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 Rus

Shankar Sastry

SWARMS

swarms research agenda7
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 Koditschek

Vijay Kumar

George Pappas

Daniela Rus

Shankar Sastry

SWARMS

alliances
Alliances
  • ARO Institute of Collaborative Biotechnology
  • Industry
    • Lockheed Martin (Penn)
    • Honeywell (Berkeley/Penn)
    • UTRC (Berkeley)
    • Boeing (MIT/Penn)
  • DoD Labs
    • AFRL, ARL, NRL

SWARMS

conclusion
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

SWARMS