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Greg Foderaro

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3 papers

2 author rows

AAAI Conference 2020 Short Paper

A Simple Deconvolutional Mechanism for Point Clouds and Sparse Unordered Data (Student Abstract)

Thomas Paniagua
John Lagergren
Greg Foderaro

This paper presents a novel deconvolution mechanism, called the Sparse Deconvolution, that generalizes the classical transpose convolution operation to sparse unstructured domains, enabling the fast and accurate generation and upsampling of point clouds and other irregular data. Specifically, the approach uses deconvolutional kernels, which each map an input feature vector and set of trainable scalar weights to the feature vectors of multiple child output elements. Unlike previous approaches, the Sparse Deconvolution does not require any voxelization or structured formulation of data, it is scalable to a large number of elements, and it is capable of utilizing local feature information. As a result, these capabilities allow for the practical generation of unstructured data in unsupervised settings. Preliminary experiments are performed here, where Sparse Deconvolution layers are used as a generator within an autoencoder trained on the 3D MNIST dataset.

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ICRA Conference 2010 Conference Paper

A potential field approach to finding minimum-exposure paths in wireless sensor networks

Silvia Ferrari
Greg Foderaro

A novel artificial-potential approach is presented for planning the minimum-exposure paths of multiple vehicles in a dynamic environment containing multiple mobile sensors, and multiple fixed obstacles. This approach presents several advantages over existing techniques, such as the ability of computing multiple minimum-exposure paths online, while avoiding mutual collisions, as well as collisions with obstacles sensed during the motion. Other important advantages include the ability of utilizing heterogenous sensor models, and of meeting multiple objectives, such as minimizing power required, and reaching a set of goal configurations. The approach is demonstrated through numerical simulations involving autonomous underwater vehicles (AUVs) deployed in a region of interest near the New Jersey coast, with ocean currents simulated using real coastal ocean dynamics applications radar (CODAR) data.

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ICRA Conference 2010 Conference Paper

A probability density function approach to distributed sensors' path planning

Silvia Ferrari
Greg Foderaro
Andrew Tremblay

A novel artificial-potential function approach is presented for planning the paths of distributed sensor networks in a complex dynamic environment. The approach implements a novel potential function generated from a probability density function (PDF) parameterized by an adaptive Gaussian mixture that is optimized to meet network-level objectives, such as cooperative track detection. The PDF represents the goal density that would be obtained by sampling a statistically-significant number of sensors from the mixture. However, since a smaller number of sensors may be deployed, and each sensor is represented by a disk, the potential function is generated by multiplying the PDF by a likelihood update model that produces networks with disjoint fields-of-view. The approach is demonstrated through numerical simulations involving ocean sensor networks deployed in a region of interest near the New Jersey coast.

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