Cooperating Unmanned Vehicles

Chalmers, Robert; Scheidt, David; Neighoff, Todd M.; Witwicki, S.; Bamberger, Robert J.

doi:10.2514/6.2004-6252

Cited by 18 publications

(8 citation statements)

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“…In field-based systems a scalar field is generated by a combination of attracting and repelling elements, and the agents respond to those forces or follow gradients in this field. Within this class of algorithms are particle systems based on Reynold's model 7,8 , potential fields based on physics models [9][10][11][12] , and digital pheromones based on insect models [13][14][15][16][17] . Digital pheromones are similar to potential fields, but they more naturally lend themselves to decentralized computation than potential fields.…”

Section: Approaches To Surveillance and Perimeter Protectionmentioning

confidence: 99%

Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection

Sauter¹,

Matthews²,

Robinson³

et al. 2009

AIAA Infotech@Aerospace Conference

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Section: Approaches To Surveillance and Perimeter Protectionmentioning

confidence: 99%

Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection

Sauter¹,

Matthews²,

Robinson³

et al. 2009

AIAA Infotech@Aerospace Conference

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“…Co-fields are based upon cooperation through social potential fields (Chalmers et al 2004). Each robot is considered to be a particle with a given position in a fixed time.…”

Section: Representation Of the Environmentmentioning

confidence: 99%

A Game Theoretic Approach to Swarm Robotics

Givigi

Schwartz

2006

Applied Bionics and Biomechanics

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In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.

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“…Our technique is inspired by previous work on dynamic co-fields (DCF) [4,5] which introduced a method for controlling the movement of a mobile autonomous agent swarm through the use of coordination fields. We re-frame the neuron tracing problem as one where autonomous agents trace an EM volume utilizing the outputs of a series of weighted potential fields as local decision making rules to seek out unexplored regions and search for synapses.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity

Drenkow

Joyce

Matelsky

et al. 2020

Preprint

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As biological imaging datasets continue to grow in size, extracting information from large image volumes presents a computationally intensive challenge. State-of-the-art algorithms are almost entirely dominated by the use of convolutional neural network approaches that may be difficult to run at scale given schedule, cost, and resource limitations. We demonstrate a novel solution for high-resolution electron microscopy brain image volumes that permits the identification of individual neurons and synapses. Instead of conventional approaches whereby voxels are labelled according to the neuron or neuron segment to which they belong, we instead focus on extracting the underlying brain graph represented by synaptic connections between individual neurons while also identifying key features like skeleton similarity and path length. This graph represents a critical step and scaffold for understanding the structure of neuronal circuitry. Our approach recasts the segmentation problem to one of path-finding between keypoints (i.e., connectivity) in an information sharing framework using virtual agents. We create a family of sensors which follow local decision-making rules that perform computationally cheap operations on potential fields to perform tasks such as avoiding cell membranes and finding synapses. These enable a swarm of virtual agents to efficiently and robustly traverse three-dimensional datasets, create a sparse segmentation of pathways, and capture connectivity information. We achieve results that meet or exceed state-of-the-art performance at a substantially lower computational cost. This tool offers a categorically different approach to connectome estimation that can augment how we extract connectivity information at scale. Our method is generalizable and may be extended to biomedical imaging problems such as tracing the bronchial trees in lungs or road networks in natural images.

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Cooperating Unmanned Vehicles

Cited by 18 publications

References 0 publications

Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection

Swarming Unmanned Air and Ground Systems for Surveillance and Base Protection

A Game Theoretic Approach to Swarm Robotics

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity

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