335 research outputs found

    Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

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    The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs

    Military airborne and maritime application for cooperative behaviors.

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    Robotics and Military Operations

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    In the wake of two extended wars, Western militaries find themselves looking to the future while confronting amorphous nonstate threats and shrinking defense budgets. The 2015 Kingston Conference on International Security (KCIS) examined how robotics and autonomous systems that enhance soldier effectiveness may offer attractive investment opportunities for developing a more efficient force capable of operating effectively in the future environment. This monograph offers 3 chapters derived from the KCIS and explores the drivers influencing strategic choices associated with these technologies and offers preliminary policy recommendations geared to advance a comprehensive technology investment strategy. In addition, the publication offers insight into the ethical challenges and potential positive moral implications of using robots on the modern battlefield.https://press.armywarcollege.edu/monographs/1398/thumbnail.jp

    Virtual environment UAV swarm management using GPU calculated digital pheromones

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    Our future military force will be complex: a highly integrated mix of manned and unmanned units. These unmanned units could function individually or within a swarm. The readiness of future warfighters to work alongside and utilize these new forces depends on the creation of usable interfaces and training simulators. The difficulty is that current unmanned aerial vehicle (UAV) control interfaces require too much operator attention and common swarm control methods require expensive computational power. This dissertation discusses how to improve upon current user interfaces and how to improve the performance of a common swarm control method, the digital pheromone field. This method uses digital pheromones to bias the movements of individual units within a swarm toward areas that are attractive and away from areas that are dangerous or unattractive. A more efficient method for performing pheromone field calculations is introduced, one that harnesses the power of the GPU (graphics processing unit) in today\u27s graphics cards by reshaping the ADAPTIV swarm control algorithm into a form acceptable to the GPU\u27s pipeline. The GPU ADAPTIV implementation is tested in scenarios that involve up to 50,000 virtual UAVs. When compared to its counterpart CPU implementation, the GPU version performed over 30 times faster than the CPU version. This gain translates directly into lower costs for training the future warfighter today and fielding the swarms of tomorrow. Finally, this dissertation presents a vision for combining these new interface ideas and performance enhancements into an effective swarm control interface and training simulator

    Creative or Not? Birds and Ants Draw with Muscle

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    In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

    Quantifying Spatiotemporal Stability by means of Entropy: Approach and Motivations

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    Several studies demonstrate that there are critical differences between real wireless networks and simulation models. This finding has permitted to extract spatial and temporal properties for links and to provide efficient methods as biased link sampling to guarantee efficient routing structure. Other works have focused on computing metrics to improve routing, specially the reuse of the measure of entropy. From there, rises the idea of formulating a new measure of entropy that gives an overview of the spatiotemporal stability of a link. This measure will rely on spatial and temporal properties of links and fed with the efficiency of biased link sampling.Comment: 10 pages, Telecom SudParis Research Repor

    Techniques for modeling and analyzing RNA and protein folding energy landscapes

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    RNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins
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