Sarah P Gibson

US Patent:

20170168488, Jun 15, 2017

Filed:

Aug 26, 2016

Appl. No.:

15/249250

Inventors:

- San Diego CA, US
Bardia Fallah BEHABADI - San Diego CA, US
Sarah Paige GIBSON - Del Mar CA, US
Aliakbar AGHAMOHAMMADI - San Diego CA, US
Saurav AGARWAL - College Station TX, US

International Classification:

G05D 1/02
G05D 1/00
G06T 7/00
G06K 9/00
G06T 17/05

Abstract:

A method of visual navigation for a robot includes integrating a depth map with localization information to generate a three-dimensional (3D) map. The method also includes motion planning based on the 3D map, the localization information, and/or a user input. The motion planning overrides the user input when a trajectory and/or a velocity, received via the user input, is predicted to cause a collision.

US Patent:

20170157769, Jun 8, 2017

Filed:

Jun 24, 2016

Appl. No.:

15/192719

Inventors:

- San Diego CA, US
Bardia Fallah BEHABADI - La Jolla CA, US
Christopher LOTT - San Diego CA, US
Kiran SOMASUNDARAM - San Diego CA, US
Sarah Paige GIBSON - Del Mar CA, US
Casimir Matthew WIERZYNSKI - La Jolla CA, US
Saurav AGARWAL - Bryan TX, US
Gerhard REITMAYR - Vienna, AT
Serafin DIAZ - San Diego CA, US

International Classification:

B25J 9/16

Abstract:

A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

US Patent:

20170160747, Jun 8, 2017

Filed:

Jun 24, 2016

Appl. No.:

15/192874

Inventors:

- San Diego CA, US
Saurav AGARWAL - Bryan TX, US
Christopher LOTT - San Diego CA, US
Kiran SOMASUNDARAM - San Diego CA, US
Bardia Fallah BEHABADI - La Jolla CA, US
Sarah Paige GIBSON - Del Mar CA, US
Casimir Matthew WIERZYNSKI - La Jolla CA, US
Gerhard REITMAYR - Vienna, AT
Serafin DIAZ - San Diego CA, US

International Classification:

G05D 1/02
H04N 13/02

Abstract:

A method of calculating a most likely map based on batch data includes gathering a corpus of sensor measurements indexed by a location of a sensor throughout an environment to be mapped. The method also includes determining, after gathering the corpus of sensor measurements, a most likely occupancy level of each voxel of multiple voxels of the environment in accordance with the corpus of sensor measurements and a stochastic sensor model. The method further includes calculating the most likely map based on the determined most likely occupancy level.

US Patent:

20170161910, Jun 8, 2017

Filed:

Jun 24, 2016

Appl. No.:

15/192603

Inventors:

- San Diego CA, US
Saurav AGARWAL - Bryan TX, US
Kiran SOMASUNDARAM - San Diego CA, US
Christopher LOTT - San Diego CA, US
Bardia Fallah BEHABADI - La Jolla CA, US
Sarah Paige GIBSON - Del Mar CA, US
Casimir Matthew WIERZYNSKI - La Jolla CA, US
Gerhard REITMAYR - Vienna, AT
Serafin DIAZ - San Diego CA, US

International Classification:

G06T 7/20
G05D 1/02
G06K 9/62

Abstract:

A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

US Patent:

20170161946, Jun 8, 2017

Filed:

Jun 24, 2016

Appl. No.:

15/192944

Inventors:

- San Diego CA, US
Saurav AGARWAL - Bryan TX, US
Kiran SOMASUNDARAM - San Diego CA, US
Christopher LOTT - San Diego CA, US
Bardia Fallah BEHABADI - La Jolla CA, US
Sarah Paige GIBSON - Del Mar CA, US
Casimir Matthew WIERZYNSKI - La Jolla CA, US
Gerhard REITMAYR - Vienna, AT
Serafin DIAZ - San Diego CA, US

International Classification:

G06T 17/05
G06F 17/18
B25J 9/16
G06N 7/00
G06N 99/00

Abstract:

A method for generating a map includes determining an occupancy level of each of multiple voxels. The method also includes determining a probability distribution function (PDF) of the occupancy level of each voxel. The method further includes performing an incremental Bayesian update on the PDF to generate the map based on a measurement performed after determining the PDF.

US Patent:

20150278683, Oct 1, 2015

Filed:

Mar 27, 2014

Appl. No.:

14/228078

Inventors:

- San Diego CA, US
Sarah Paige GIBSON - San Diego CA, US
Jeffrey Alexander LEVIN - San Diego CA, US
Ravindra Manohar PATWARDHAN - San Diego CA, US
Avijit CHAKRABORTY - San Diego CA, US
William Howard CONSTABLE - San Diego CA, US

Assignee:

QUALCOMM Incorporated - San Diego CA

International Classification:

G06N 3/08
G06N 3/04

Abstract:

A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.

US Patent:

20150161506, Jun 11, 2015

Filed:

Apr 23, 2014

Appl. No.:

14/260140

Inventors:

- San Diego CA, US
Yinyin LIU - San Diego CA, US
Sarah Paige GIBSON - San Diego CA, US
Michael CAMPOS - La Jolla CA, US
Vikram GUPTA - San Diego CA, US
Victor Hokkiu CHAN - Del Mar CA, US
Edward Hanyu LIAO - San Diego CA, US
Erik Christopher MALONE - San Diego CA, US

Assignee:

QUALCOMM INCORPORATED - San Diego CA

International Classification:

G06N 3/08

Abstract:

Methods and apparatus are provided for effecting modulation using global scalar values in a spiking neural network. One example method for operating an artificial nervous system generally includes determining one or more updated values for artificial neuromodulators to be used by a plurality of entities in a neuron model and providing the updated values to the plurality of entities.