11,846 research outputs found

    Collective motion, sensor networks, and ocean sampling

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    Author Posting. © IEEE, 2007. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in Proceedings of the IEEE 95 (2007): 48-74, doi:10.1109/jproc.2006.887295.This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored

    Coverage problems in mobile sensing

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (p. 177-183).Sensor-networks can today measure physical phenomena at spatial and temporal scales that were not achievable earlier, and have shown promise in monitoring the environment, structures, agricultural fields and so on. A key challenge in sensor-networks is the coordination of four actions across the network: measurement (sensing), communication, motion and computation. The term coverage is applied to the central question of how well a sensor-network senses some phenomenon to make inferences. More formally, a coverage problem involves finding an arrangement of sensors that optimizes a coverage metric. In this thesis we examine coverage in the context of three sensing modalities. The literature on the topic has thus far focused largely on coverage problems with the first modality: static event-detection sensors, which detect purely binary events in their immediate vicinity based on thresholds. However, coverage problems for sensors which measure physical quantities like temperature, pressure, chemical concentrations, light intensity and so on in a network configuration have received limited attention in the literature. We refer to this second modality of sensors as estimation sensors; local estimates from such sensors can be used to reconstruct a field. Third, there has been recent interest in deploying sensors on mobile platforms. Mobility has the effect of increasing the effectiveness of sensing actions. We further classify sensor mobility into incidental and intentional motion. Incidentally mobile sensors move passively under the influence of the environment, for instance, a floating sensor drifting in the sea. We define intentional mobility as the ability to control the location and trajectory of the sensor, for example by mounting it on a mobile robot. We build our analysis on a series of cases. We first analyze coverage and connectivity of a network of floating sensors in rivers using simulations and experimental data, and give guidelines for sensor-network design. Second, we examine intentional mobility and detection sensors.(cont.) We examine the problem of covering indoor and outdoor pathways with reconfigurable camera sensor-networks. We propose and validate an empirical model for detection behavior of cameras. We propose a distributed algorithm for reconfiguring locations of cameras to maximize detection performance. Finally, we examine more general strategies for the placement of estimation sensors and ask when and where to take samples in order to estimate an unknown spatiotemporal field with tolerable estimation errors. We discuss various classes of error-tolerant sensor arrangements for trigonometric polynomial fields.by Ajay A. Deshpande.Ph.D

    Combining physical and cultural weed control with biological methods – prospects for integrated non-chemical weed management strategies

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    The paper deals with the possibilities of combining physical weed control with biological weed control

    Recent results in the development of band steaming for intra-row weed control

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    The recent achievements with developing band-steaming techniques for intra-row weed control in vegetables are presente

    F-formation Detection: Individuating Free-standing Conversational Groups in Images

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    Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

    Active Localization of Gas Leaks using Fluid Simulation

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    Sensors are routinely mounted on robots to acquire various forms of measurements in spatio-temporal fields. Locating features within these fields and reconstruction (mapping) of the dense fields can be challenging in resource-constrained situations, such as when trying to locate the source of a gas leak from a small number of measurements. In such cases, a model of the underlying complex dynamics can be exploited to discover informative paths within the field. We use a fluid simulator as a model, to guide inference for the location of a gas leak. We perform localization via minimization of the discrepancy between observed measurements and gas concentrations predicted by the simulator. Our method is able to account for dynamically varying parameters of wind flow (e.g., direction and strength), and its effects on the observed distribution of gas. We develop algorithms for off-line inference as well as for on-line path discovery via active sensing. We demonstrate the efficiency, accuracy and versatility of our algorithm using experiments with a physical robot conducted in outdoor environments. We deploy an unmanned air vehicle (UAV) mounted with a CO2 sensor to automatically seek out a gas cylinder emitting CO2 via a nozzle. We evaluate the accuracy of our algorithm by measuring the error in the inferred location of the nozzle, based on which we show that our proposed approach is competitive with respect to state of the art baselines.Comment: Accepted as a journal paper at IEEE Robotics and Automation Letters (RA-L
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