3,005 research outputs found
Experiments with cooperative networked control of underwater robots
No full textWe present details and results obtained with an underwater system comprising two different autonomous underwater robots (AUV) and ten static underwater nodes (USN) networked together optically and acoustically. The AUVs can locate and hover above the static nodes for data upload, and they can perform network maintenance functions such as deployment, relocation, and recovery. The AUVs can also locate each other, dock, and move using coordinated control that takes advantage of each AUV’s strength. \u
Toward Pervasive Robots (Invited Talk)
No full textThe digitization of practically everything coupled with the mobile Internet, the automation of knowledge work, and advanced robotics promises a future with democratized use of machines and wide-spread use of robots and customization. However, pervasive use of robots remains a hard problem. Where are the gaps that we need to address in order to advance toward a future where robots are common in the world and they help reliably with physical tasks? What is the role of geometric reasoning along this trajectory?
In this talk I will discuss challenges toward pervasive use of robots and recent developments in geometric algorithms for customizing robots. I will focus on a suite of gemetric algorithms for automatically designing, fabricating, and tasking robots using a print-and-fold approach. I will also describe how geometric reasoning can play a role in creating robots more capable of reasoning in the world. By enabling on-demand creation of programmable robots, we can begin to imagine a world with one robot for every physical task
A Scalable Information Theoretic Approach to Distributed Robot Coordination
No full textThis paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a recursive Bayesian filter, while continuously moving to improve the quality of the estimate by following the gradient of mutual information. Both the filter and the controller use a novel algorithm for approximating the robots' joint measurement probabilities, which combines consensus (for decentralization) and sampling (for scalability). The approximations are shown to approach the true joint measurement probabilities as the size of the consensus rounds grows or as the network becomes complete. The resulting gradient controller runs in constant time with respect to the number of robots, and linear time with respect to the number of sensor measurements and environment discretization cells, while traditional mutual information methods are exponential in all of these quantities. Furthermore, the controller is proven to be convergent between consensus rounds and, under certain conditions, is locally optimal. The complete distributed inference and coordination algorithm is demonstrated in experiments with five quad-rotor flying robots and simulations with 100 robots.This work is sponsored by the Department of the Air Force under Air Force contract number FA8721-05-C-0002. The opinions, interpretations, recommendations, and conclusions are those of the authors and are not necessarily endorsed by the United States Government. This work is also supported in part by ARO grant number W911NF-05-1-0219, ONR grant number N00014-09-1-1051, NSF grant number EFRI-0735953, ARL grant number W911NF-08-2-0004, MIT Lincoln Laboratory, the European Commission, and the Boeing Company
Guaranteeing Spoof-Resilient Multi-Robot Networks
No full textMulti-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can "sense" spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of robotic coverage problems. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%
Supplemental Material - Grounded: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather
No full textSupplemental Material for Grounded: A localizing ground penetrating radar evaluation dataset for learning to localize in inclement weather by Teddy Ort, Igor Gilitschenski, and Daniela Rus in The International Journal of Robotics Research</p
Um ambiente computacional ubíquo orientado a mobilidade
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação
Modular Robot Systems From Self-Assembly to Self-Disassembly
No full textWe have presented a detailed retrospective on modular robots and discussed connections between modular robots and programmable matter. This field has seen a great deal of creativity and innovation at the level of designing physical systems capable of matching shape to function and algorithms that achieve this capability. The success of these projects rests on the convergence of innovation in hardware design and materials for creating the basic building blocks, information distribution for programming the interaction between the blocks, and control. Most current systems have dimensions on the order of centimeters, yet pack computation, communication, sensing, and power transfer capabilities into their form factors. Additionally, these modules operate using distributed algorithms that use a modules ability to observe its current neighborhood and local rules to decide what to do next.United States. Defense Advanced Research Projects Agency. Programmable Matter ProgramUnited States. Defense Advanced Research Projects Agency. Chembot ProgramUnited States. Army Research Office (grant W911NF-08-1-0228)United States. Army Research Office (grant W911NF-08-C-0060)National Science Foundation (U.S.). Office of Emerging Frontiers in Research and InnovationIntel CorporationNational Defense Science and Engineering Graduate Fellowshi
Persistent ocean monitoring with underwater gliders: Adapting sampling resolution
No full textOcean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long-term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions.United States. National Oceanic and Atmospheric Administration. Monitoring and Event Response for Harmful Algal Blooms (NA05NOS4781228)National Science Foundation (U.S.). Center for Embedded Networked Sensing (CCR-0120778)National Science Foundation (U.S.). (Grant number CNS-0520305)National Science Foundation (U.S.). (Grant number CNS-0540420)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-09-1-1031)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-08-1-0693)United States. Office of Naval Research. Service-Oriented Architectur
An end-to-end system for designing mechanical structures for print-and-fold robots
No full textThis work presents a script-based development environment aimed at allowing users to easily design and create mechanical bodies for folded plastic robots. The origami-inspired fabrication process is inexpensive and widely accessible, and the tools developed in this work allow for open source design sharing and modular reuse. Designs are generated by recursively combining mechanical components - from primitive building blocks, through mechanisms and assemblies, to full robots - in a flexible yet well-defined manner. This process was used to design robotic elements of increasing complexity up to a multi-degree-of-freedom compliant manipulator arm, demonstrating the power of this system. The developed system is extensible, opening avenues for further research ultimately leading to the development of a complete robot compiler.National Science Foundation (U.S.) (Grant 1240383)National Science Foundation (U.S.) (Grant 1138967
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