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Environment Sensor Coverage using Multi-Agent Headings

Description

This work describes an approach for distance computation between agents in a

multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-

Arrival (AoA) data and local trajectory data. Each agent in the swarm is able

to discretely determine distance and

This work describes an approach for distance computation between agents in a

multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-

Arrival (AoA) data and local trajectory data. Each agent in the swarm is able

to discretely determine distance and bearing to every other neighbor agent in the

swarm. From this information, I propose a lightweight method for sensor coverage

of an unknown area based on the work of Sameera Poduri. I also show that this

technique performs well with limited calibration distances.

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Created

Date Created
2020

Rock Traits from Machine Learning: Application to Rocky Fault Scarps

Description

Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at

Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at fault scarps. Fault scarps indicate the structure of fault zones fans, relay ramps, and double faults, as well as the surface process response to the deformation and can thus indicate the activity of the fault zone and its potential hazard. “Rocky” fault scarps are unusual because they share characteristics of bedrock and alluvial fault scarps. The Volcanic Tablelands in Bishop, CA offer a natural laboratory with an array of rocky fault scarps. Machine learning mask-Region Convolutional Neural Network segments an orthophoto to identify individual particles along a specific rocky fault scarp. The resulting rock traits for thousands of particles along the scarp are used to develop conceptual models for rocky scarp geomorphology and evolution. In addition to rocky scarp classification, these tools may be useful in many sedimentary and volcanological applications for particle mapping and characterization.

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Agent

Created

Date Created
2020