Technological progress in integrated, low-power, CMOS communication devices and sensors makes a rich design space of networked sensors viable. They can be deeply embedded in the physical world and spread throughout our environment like smart dust. The missing elements are an overall system architecture and a methodology for systematic advance. To this end, we identify key requirements, develop a small device that is representative of the class, design a tiny event-driven operating system, and show that it provides support for efficient modularity and concurrency-intensive operation. Our operating system fits in 178 bytes of memory, propagates events in the time it takes to copy 1.25 bytes of memory, context switches in the time it takes to copy 6 bytes of memory and supports two level scheduling. The analysis lays a groundwork for future architectural advances.

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System architecture directions for networked sensors

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

We present Telos, an ultra low power wireless sensor module ("mote") for research and experimentation. Telos is the latest in a line of motes developed by UC Berkeley to enable wireless sensor network (WSN) research. It is a new mote design built from scratch based on experiences with previous mote generations. Telos' new design consists of three major goals to enable experimentation: minimal power consumption, easy to use, and increased software and hardware robustness. We discuss how hardware components are selected and integrated in order to achieve these goals. Using a Texas Instruments MSP430 microcontroller, Chipcon IEEE 802.15.4-compliant radio, and USB, Telos' power profile is almost one-tenth the consumption of previous mote platforms while providing greater performance and throughput. It eliminates programming and support boards, while enabling experimentation with WSNs in both lab, testbed, and deployment settings.

/pdf/telos-enabling-ultra-low-power-wireless-research-4f4zlqza84.pdf

Telos: enabling ultra-low power wireless research

In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace In our formulation, each robot acts fully independently, and does not communicate with other robots Based on the definition of velocity obstacles [5], we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few milliseconds To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace

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Reciprocal n-Body Collision Avoidance

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

/pdf/swarm-robotics-a-review-from-the-swarm-engineering-46mfc058e6.pdf

Swarm robotics: a review from the swarm engineering perspective

We describe a set of distributed algorithms used to disperse a large group of autonomous mobile robots efficiently throughout an indoor environment. Only local inter-robot communication and processing is used. Ad-hoc communications network topologies formed by gradient floods spread messages and guide robot motion. Special attention has been given to doors, hallways, and other constrictions. The network maintains a route to chargers to allow self-charging.

Distributed Algorithms for Dispersion in Indoor Environments Using a Swarm of Autonomous Mobile Robots

This paper presents a control strategy that allows a group of mobile robots to position themselves to optimize the measurement of sensory information in the environment. The robots use sensed information to estimate a function indicating the relative importance of different areas in the environment. Their estimate is then used to drive the network to a desirable placement configuration using a computationally simple decentralized control law. We formulate the problem, provide a practical control solution, and present the results of numerical simulations. We then discuss experiments carried out on a swarm of mobile robots.

http://www.roboticsproceedings.org/rss02/p07.pdf

Distributed Coverage Control with Sensory Feedback for Networked Robots

Human-robot interfaces for interacting with hundreds of autonomous robots must be very different from single-robot interfaces. The central design challenge is developing techniques to maintain, program, and interact with the robots without having to handle them individually. This requires robots that can support hands-free operation, which drives many other aspects of the design. This paper presents the design philosophy and practical experience with human-robot interfaces to develop, debug, and evaluate distributed algorithms on the 112-robot iRobot Swarm. These human-robot interaction (HRI) techniques fall into three categories: a physical infrastructure to support hands-free operation, utility software for centralized development and debugging, and carefully designed lights, sounds and movement that allow the user to interpret the inner workings of groups of robots without having to look away or use special equipment. The end result is a useable Swarm, with develop-run-debug cycle times approaching that of a simulation.

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Speaking Swarmish: Human-Robot Interface Design for Large Swarms of Autonomous Mobile Robots.

Methods for operating robotic devices (i.e., “robots”) that employ adaptive behavior relative to neighboring robots and external (e.g., environmental) conditions. Each robot is capable of receiving, processing, and acting on one or more multi-device primitive commands that describe a task the robot will perform in response to other robots and the external conditions. The commands facilitate a distributed command and control structure, relieving a central apparatus or operator from the need to monitor the progress of each robot. This virtually eliminates the corresponding constraint on the maximum number of robots that can be deployed to perform a task (e.g., data collection, mapping, searching). By increasing the number of robots, the efficiency in completing the task is also increased.

System and methods for adaptive control of robotic devices

Ants show an incredible ability to collectively transport complex irregular-shaped objects with seemingly simple coordination. Achieving similarly effective collective transport with robots has potential applications in many settings, from agriculture to construction to disaster relief. In this paper we investigate a simple decentralized strategy for collective transport in which each agent acts independently without explicit coordination. Using a physics-based model, we prove that this strategy is guaranteed to successfully transport a complex object to a target location, even though each agent only knows the target direction and does not know the object shape, weight, its own position, or the position and number of other agents. Using two robot hardware platforms, and a wide variety of complex objects, we validate the strategy through extensive experiments. Finally, we present a set of experiments to demonstrate the versatility of the simple strategy, including transport by 100 robots, transport of an actively moving object, adaptation to change in goal location, and dealing with partially observable goals.

/pdf/collective-transport-of-complex-objects-by-simple-robots-40oh2elir8.pdf

James McLurkin

Papers

Distributed Algorithms for Dispersion in Indoor Environments Using a Swarm of Autonomous Mobile Robots

Distributed Coverage Control with Sensory Feedback for Networked Robots

Speaking Swarmish: Human-Robot Interface Design for Large Swarms of Autonomous Mobile Robots.

System and methods for adaptive control of robotic devices

Collective transport of complex objects by simple robots: theory and experiments