S calable s W arms of A utonomous R obots and M obile S ensors

1. Scalable sWarms of Autonomous Robots and Mobile Sensors Vijay Kumar University of Pennsylvania www.grasp.upenn.edu/~kumar www.swarms.org

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) 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, 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, 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 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

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 • 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. Approximate model Information Model SWARMS

18. Confidence Ellipsoids + = SWARMS

19. UGV Trajectory SWARMS

20. Decentralized control, but shared information… SWARMS

21. ACCLIMATE SWARMS

22. EMBER SWARMS

23. Steve SWARMS

24. Francesco SWARMS

25. Scalable Anonymity, Robustness Taxonomy of Approaches Our Goal SWARMS

26. Scaling up to a Swarm Paradigm

27. Three Overarching Themes • Decentralized • Anonymity • Simple individuals, but versatile group SWARMS

28. 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

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 Block Island Workshop on Cooperative Control, June 10-11, 2003 Workshop on Swarming in Natural and Engineered Systems, August 3-4, 2005 SWARMS

30. 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

31. 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

32. 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

33. Swarming in Nature SWARMS

34. 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

35. SWARMS Research Agenda 3. Analysis SWARMS

36. 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

37. 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

38. 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

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

40. 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