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

1. 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 Scalable sWarms of Autonomous Robots and Mobile Sensors Francesco Bullo Ali Jadbabaie, Daniel E. Koditchek, Vijay Kumar (PI), and George Pappas S. Shankar Sastry Daniela Rus A. Stephen Morse David Skelly University of California, Berkeley Massachusetts Institute of Technology University of California, Santa Barbara University of Pennsylvania Yale University M o t i v a t i o n Gr o u p B e h a v i o r s i n B i o l o g y Re s e a r c h T h r u s t s • Future missions will rely on large, networked groups of vehicles and sensors operating in dynamic, resource-constrained environments • Large groups will need to operate with little direct supervision • Biology provides many models and paradigms for group behaviors 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. 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. 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. 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. 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. 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. The SWARMS Project Ob j e c t i v e s • 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 Im p a c t • Technology for deploying resilient, secure teams of inexpensive, unmanned vehicles • Adaptive communication networks • Search, reconnaissance, surveillance missions Penn UAVs MIT UGVs Penn UGVs Ac k n o w l e d g e m e n t Camera image from UAV This work is supported by ARO MURI Grant W911NF-05-1-0219 under the direction of Dr. Randy Zachery, Division Chief, Computing and Information Sciences. Experimental Testbeds for SWARMS MIT Cricket Sensor Network UCB Smart Bird