Irobot Corporation

Danielle O'Connor Dean

Michael J Halloran

Husain al-Mohssen

Laura V Herlant

Elena Jakubiak

Zupei Li

Ryan Schneider

Stefan Zickler

Hyun Woo Paik

Sung-Yee Guo

Ottillia Shirhan Ni

Eric Beowulf Martinson

Douglas Ferrer

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.

Image capture devices for autonomous mobile robots and related systems and methods

A method of operating a mobile cleaning robot in an environment can include detecting, such as using an optical stream from the mobile cleaning robot, a seasonal object located in the environment. A seasonal cleaning zone can be created based on the detected seasonal object when a current date is within a specified date range. The seasonal cleaning zone can be displayed on a map of the environment.

Seasonal cleaning zones for mobile cleaning robot

Described herein are systems, devices, and methods for localizing and recognizing an object in an environment. In an example, a mobile cleaning robot comprises a drive system to move the mobile cleaning robot about an environment, an imaging sensor to take images of an object in the environment from different perspectives. The multiple observations include images of the object that is at least partially occluded by an obstacle. A controller circuit of the mobile robot can, for multiple different locations in a map of the environment, calculate respective fractional visibility values using the plurality of images. The fractional visibility values each represent a probability of the object being visible through the corresponding location. The controller circuit can localize and recognize the object based on the fractional visibility values at the multiple locations on the map.

Object localization and recognition using fractional occlusion frustum

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Westford, MA, United States of America

Danielle O'Connor Dean