Abstract: A system for enabling spot cleaning includes a mobile computing device and a mobile cleaning robot. The mobile computing device includes at least one camera configured to capture images of an environment, and at least one data processor configured to (a) establish, based at least in part on first information provided by the at least one image sensor, a coordinate system in the environment, (b) determine, based at least in part on second information provided by the at least one camera, a first set of coordinates of a region at a first location, (c) determine, based at least in part on third information provided by the at least one camera, a second set of coordinates of a mobile cleaning robot at a second location, (d) send the first set of coordinates and second set of coordinates, or coordinates of the first location relative to the second location, to the mobile cleaning robot, and (e) send an instruction to the mobile cleaning robot to request the mobile cleaning robot to travel to the first location.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: An autonomous cleaning robot includes a drive system to support the autonomous cleaning robot above a floor surface, an image capture device positioned on the autonomous cleaning robot to capture imagery of a portion of the floor surface forward of the autonomous cleaning robot, and a controller operably connected to the drive system and the image capture device. The drive system is operable to maneuver the autonomous cleaning robot about the floor surface. The controller is configured to execute instructions to perform operations including initiating, based on a user-selected sensitivity and the imagery captured by the image capture device, an avoidance behavior to avoid an obstacle on the portion of the floor surface.

Abstract: An autonomous cleaning robot includes a drive system to support the autonomous cleaning robot above a floor surface, an image capture device positioned on the autonomous cleaning robot to capture imagery of a portion of the floor surface forward of the autonomous cleaning robot, and a controller operably connected to the drive system and the image capture device. The drive system is operable to maneuver the autonomous cleaning robot about the floor surface. The controller is configured to execute instructions to perform operations including initiating, based on a user-selected sensitivity and the imagery captured by the image capture device, an avoidance behavior to avoid an obstacle on the portion of the floor surface.

Abstract: A system for enabling spot cleaning includes a mobile computing device and a mobile cleaning robot. The mobile computing device includes at least one camera configured to capture images of an environment, and at least one data processor configured to (a) establish, based at least in part on first information provided by the at least one image sensor, a coordinate system in the environment, (b) determine, based at least in part on second information provided by the at least one camera, a first set of coordinates of a region at a first location, (c) determine, based at least in part on third information provided by the at least one camera, a second set of coordinates of a mobile cleaning robot at a second location, (d) send the first set of coordinates and second set of coordinates, or coordinates of the first location relative to the second location, to the mobile cleaning robot, and (e) send an instruction to the mobile cleaning robot to request the mobile cleaning robot to travel to the first location.

Abstract: A system for enabling spot cleaning includes a mobile computing device and a mobile cleaning robot. The mobile computing device includes at least one camera configured to capture images of an environment, and at least one data processor configured to (a) establish, based at least in part on first information provided by the at least one image sensor, a coordinate system in the environment, (b) determine, based at least in part on second information provided by the at least one camera, a first set of coordinates of a region at a first location, (c) determine, based at least in part on third information provided by the at least one camera, a second set of coordinates of a mobile cleaning robot at a second location, (d) send the first set of coordinates and second set of coordinates, or coordinates of the first location relative to the second location, to the mobile cleaning robot, and (e) send an instruction to the mobile cleaning robot to request the mobile cleaning robot to travel to the first location.

Abstract: A method for prioritizing a demand response program event based on occupancy for one or more buildings of one or more building types participating in a demand response program, the method comprising: receiving energy consumption and outside temperature streams Ei(h,T) corresponding to a portion of the one or more buildings, and receiving and employing occupancy components for each of the one or more buildings within the portion to process the streams, and generating occupancy levels corresponding to the one or more buildings within the portion, and assigning the occupancy levels to remaining ones of the one or more buildings not in the portion; and optimizing execution of the demand response program event by employing the occupancy components to prioritize dispatch messages to the one or more buildings to achieve objectives of the demand response program event.

Abstract: A method for prioritizing a demand response program event based on occupancy for one or more buildings of one or more building types participating in a demand response program, the method comprising: receiving energy consumption and outside temperature streams Ei(h,T) corresponding to a portion of the one or more buildings, the energy consumption provided by streaming consumption sources, and receiving and employing occupancy components for each of the one or more buildings within the portion to process the streams, and generating occupancy levels corresponding to the one or more buildings within the portion, and assigning the occupancy levels to remaining ones of the one or more buildings not in the portion; and optimizing execution of the demand response program event by employing the occupancy components to prioritize dispatch messages to the one or more buildings to achieve objectives of the demand response program event.

Abstract: An autonomous cleaning robot includes a drive system to support the autonomous cleaning robot above a floor surface, an image capture device positioned on the autonomous cleaning robot to capture imagery of a portion of the floor surface forward of the autonomous cleaning robot, and a controller operably connected to the drive system and the image capture device. The drive system is operable to maneuver the autonomous cleaning robot about the floor surface. The controller is configured to execute instructions to perform operations including initiating, based on a user-selected sensitivity and the imagery captured by the image capture device, an avoidance behavior to avoid an obstacle on the portion of the floor surface.

Abstract: A method for predicting when a grid will exceed production capacity including generating time shifted versions of consumption data for each of the buildings, each version comprising consumption values along with time and temperature values, where the consumption values are shifted by lag values relative to the time and temperature values, and where each lag value is different; performing a regression analysis on each version to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and lags for all of the buildings to estimate a cumulative consumption for the buildings, and to predict the time when the grid will exceed production capacity; and receiving the time when the grid will exceed production capacity, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when consumption on a grid will exceed normal capacity for buildings including generating time shifted versions of consumption data for the buildings, each version comprising consumption values along with time and outside temperature values, where the energy consumption values within each version are shifted by one of a plurality of lag values relative to the time and outside temperature values; performing regression analysis on each version to yield regression model parameters and a residual; determining a least valued residual indicating an energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags to estimate a cumulative consumption for the buildings, and to predict when consumption on the grid will exceed normal capacity; and receiving the time when consumption on the grid will exceed normal capacity, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when consumption on a grid will exceed production capacity for buildings within the grid including time shifted version for each of the buildings, each version comprising consumption values along with time and temperature values, where the consumption values within each set are shifted by one of a plurality of lag values relative to the time and temperature values; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative consumption, and to predict the time when consumption on the grid will exceed production capacity; and receiving the time, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A computer-implemented method for labeling images includes capturing, using an augmented-reality enabled device, a first set of images that include views of a first object; at the augmented-reality enabled device, for each of the first set of images, identifying the first object and generating a bounding box that is associated with the first object; receiving an input providing a first label for the first object; and at the augmented-reality enabled device, for each of at least some of the first set of images, associating the first label with the first object bound by the bounding box.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: An occupancy-based traffic control system for augmenting traffic control mechanisms corresponding to buildings of one or more building types within a geographic subsector is provided. The system includes, a subsector aggregator, an other facility occupancy estimator, and a subsector processor. The subsector aggregator receives energy consumption and outside temperature streams corresponding to a portion of the buildings, and employs occupancy components to generate average occupancy components corresponding to each building type. The other facility occupancy estimator matches the average occupancy components to remaining ones of the buildings not in the first portion according to building type. The subsector processor uses the first and second aggregated occupancy components modify defaults of traffic control devices based on the occupancy estimate to optimize flow of pedestrians.