We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.
The last decade saw the introduction of the first top-down approaches to the development of distributed computing systems. Various abstractions and programming languages have been proposed in the sensor network community @cite_17 . A programming methodology inspired by embryogenesis and designed for self-assembly applications was proposed in @cite_16 . Dantu proposed Karma @cite_13 , a framework that combines centralized and distributed elements to perform task allocation in a swarm of aerial robots unable to communicate directly.
@cite_16 @cite_13 @cite_17
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Distributed nature of the sensor network architecture introduces unique challenges and opportunities for collaborative networked signal processing techniques that can potentially lead to significant performance gains. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We propose a new way of removing this redundancy in a completely distributed manner, i.e., without the sensors needing to talk, to one another. Our constructive framework for this problem is dubbed DISCUS (distributed source coding using syndromes) and is inspired by fundamental concepts from information theory. We review the main ideas, provide illustrations, and give the intuition behind the theory that enables this framework.We present a new domain of collaborative information communication and processing through the framework on distributed source coding. This framework enables highly effective and efficient compression across a sensor network without the need to establish inter-node communication, using well-studied and fast error-correcting coding algorithms. This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms.Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises.Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation Recent advances in forward error correction and scalable video coding enable new approaches for robust, distributed streaming in Mobile Ad Hoc Networks (MANETs). This paper presents an approach for distribution of real time video by uncoordinated peer-to-peer relay or source nodes in an overlay network on top of a MANET. The approach proposed here allows for distributed, rate-distortion optimized transmission-rate allocation for competing scalable video streams at relay nodes in the overlay network. The approach has the desirable feature of path source diversity that can be used for enhancing reliability in connectivity to serving nodes and or attaining a higher throughput. The distributed approach reduces signaling overhead as well as avoiding scalability issues that come with centralized processing in MANETs. Results show a significant performance gain over both single-server systems and previously proposed multi-source systems. This paper considers robotic sensor networks performing spatially-distributed estimation tasks. A robotic sensor network is deployed in an environment of interest, and takes successive point measurements of a dynamic physical process modeled as a spatio-temporal random field. Taking a Bayesian perspective on the Kriging interpolation technique from geostatistics, we design the distributed Kriged Kalman filter for predictive inference of the random field and of its gradient. The proposed algorithm makes use of a novel distributed strategy to compute weighted least squares estimates when measurements are spatially correlated. This strategy results from the combination of the Jacobi overrelaxation method with dynamic average consensus algorithms. As an application of the proposed algorithm, we design a gradient ascent cooperative strategy and analyze its convergence properties in the absence of measurement errors via stochastic Lyapunov functions. We illustrate our results in simulation.