1. Scalable sWarms of Autonomous Robots and Mobile Sensors
Vijay Kumar
University of Pennsylvania

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
Many civilian applications already, even in manufacturing industry - large numbers of embedded computers, high data rate sensors (cameras) -
Main question is how to scale up from 10’s and 100’s to 1000’s.
What happens when n ­ (1-10 to 10’s to 100’s)?
Research problems
Self aware, localize, self organize
Mobility
Communication
Sensing
Control
Tasking, interfaces
SWARMS

3. DoD Relevance Main Benefits Potential Impact SWARMS Unmanned
Inexpensive
Scaleable
Potential Impact
Adaptive communication networks for MOUT
Chem/Bio search
Reconnaissance and surveillance
Minefield breaching
Resilient
Secure
Unmanned – Place machines not people in harm’s way
Inexpensive – Low unit cost due to simplicity of individuals
Scaleable – Adding members does not require re-programming
Resilient – Attrition-tolerant, fault-tolerant due to large numbers
Secure – Intelligence lies in network, so capture of individual not dangerous
SWARMS

4. Biological Models (1)
Pack of wolves surrounds larger and more powerful moose. Attack vulnerable spots while moose distracted.
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
The Korf model is certainly a simplification. For example, because the moose hind legs are far more dangerous than its forelegs, it would be more realistic to weight the distance by danger.
It is worth noting that this model does not require any communications between the wolves.
Citation: R. E. Korf. A Simple Solution to Pursuit Games. In Proceedings of Eleventh International Workshop on Distributed Artificial Intelligence, Glen Arbor, MI, pages , 1992.
The wolves surround their prey using this strategy and close in on the prey. Until the wolf behind the prey closest to it can jump on the prey. The wolves in front of the prey keep its attention by menacing it, enabling one of the wolves behind the prey to jump on its back and bring it down.
SWARMS

5. Biological Models (2) SWARMS
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]
Flocking rules:
1/ Maintain specified minimum separation from nearest object
2/ Match velocity (magnitude and direction) to neighbors
3/ Stay close to center of flock
C. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Computer Graphics (21)4:25-34, [The BOIDS site that this guy maintains is interesting.]
Termite mound construction rules:
0/ Metabolize bodily waste, which contains pheromones
1/ Wander randomly, but prefer direction of strongest local pheromone concentration
2/ Decide stochastically whether to deposit current load. P(drop) increases with local pheromone density and with amount of current load
P. Kugler, R. Shaw, K. Vincente, J. Kinsella-Shaw, “Inquiry into intentional systems I: Issues in ecological physics.” Psychological Research, 1990
These models, like the the predator-prey model, are somewhat oversimplified.
SWARMS

6. 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

7. DoD Relevance and History
Scythians vs. Macedonians, Central Asian Campaign, 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,
Autonomous or semi-autonomous units engaging in convergent assault on a common target
Amorphous but coordinated way to strike from all directions – “sustainable pulsing” of force or fire
Many small, dispersed, internetted maneuver units
Stand-off and close-in capabilities
Attacks designed to disrupt cohesion of adversary
Swarming and the Future of Conflict, RAND, 2000
SWARMS

8. History (continued) SWARMS
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, 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

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
SWARMS

11. Fort Benning Demonstration of 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

12. 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

13. SWARMS

14. McKenna MOUT Site
SWARMS

15. Main Accomplishments But… SWARMS
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
Grocholsky, et al, 2004
ARO, ACCLIMATE Project
SWARMS

17. Information Model
Approximate model
SWARMS

18. Control
SWARMS

19. Confidence Ellipsoids+ =
SWARMS

20. UGV Trajectory
SWARMS

21. Decentralized control, but shared information…
SWARMS

22. ACCLIMATE
SWARMS

23. EMBER
SWARMS

24. Steve
SWARMS

25. Francesco
SWARMS

26. Taxonomy of Approaches
Centralized
Decentralized
Vehicles identified
1. Guarantees on performance
2. Optimality
Identical vehicles
Scalable
Anonymity, Robustness
Our Goal
SWARMS

27. Scaling up to a Swarm Paradigm

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 SWARMS
SWARMS SWARMS
Biology AI
Robotics
Organism
Behaviors
Swarm
Architectures
Vehicle
Models
Modeling
Synthesis
Analysis
Theory of
Swarming
Multi-vehicle
Sensing/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
Stephen Morse
George 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
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 Bullo
Ali Jadbabaie
A Stephen Morse
SWARMS

36. SWARMS Research Agenda
3. Analysis
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 Jadbabaie
Dan 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 Rus
Shankar 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 Koditschek
Vijay Kumar
George Pappas
Daniela Rus
Shankar 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
SWARMS