Fetch Robotics, Inc.

Zebra Technologies Corporation

Brian Cairl

Melonee Wise

Levon Avagyan

Niharika Arora

Derek King

Russell Toris

Sarah Elliott

Stefan Nusser

Eric Diehr

John W Stewart, Iii

David Dymesich

David Robson

Aaron Hoy

Robert Chatman, Iii

Carl Saldanha

Rohan Bhargava

Alex Henning

A system includes a mobile robot, the robot comprising a sensor; and a server operably connected to the robot over a network, the robot being configured to detect an object by processing sensor data using a convolutional neural network. A pipeline for robotic object detection using a convolutional neural network includes: a system comprising a mobile robot, the robot comprising a sensor, the system further comprising a server operably connected to the robot over a network, the robot being configured to detect an object by processing sensor data using a pipeline, the pipeline comprising a convolutional neural network, the pipeline configured to perform a data collection step, the pipeline further configured to perform a data transformation step, the pipeline further configured to perform a convolutional neural network step, the pipeline further configured to perform a network output transformation step, the pipeline further configured to perform a results output step.

System and method for robotic object detection using a convolutional neural network

A system includes: a mobile robot comprising a sensor, the robot further comprising a computer, the robot operating in an environment; a server operably connected to the robot via a communication system, the server configured to manage the robot; a controller operably connected to the robot, the controller operably connected to the server, the controller configured to control the robot; and an object of interest marked with a marker at one or more of an approximate height and an approximate field of view of the sensor, the sensor generating data describing the object of interest, the computer configured to identify one or more of the object of interest and the location using one or more of a shape of the object of interest and an intensity of data describing the object of interest.

System and method for semantically identifying one or more of an object and a location in a robotic environment

A system includes: a cart including: four legs; at least one shelf, each shelf attached to each of the legs; the cart having a generally rectangular shape, a width of the cart being longer than a length of the robot, a length of the cart being longer than a length of the robot; four wheels, each wheel attached to a different leg at a bottom of the leg, the wheels configured to roll to facilitate movement of the cart; and a robotic dock, the robotic dock comprising four docking receptacles at ninety degree angles from adjacent docking receptacles; and a robot comprising: a sensor; and a docking module, the docking module comprising retractable docking pins, each retractable docking pin configured, when extended upward, to mate with a corresponding docking receptacle, thereby securing the robot to a bottom shelf of the cart.

Robotic cart configured for effective navigation and multi-orientation docking

A cart-based workflow system includes: a robot operating in a facility; a server, the server operably connected to the robot, the server configured to do one or more of send the robot a cart transfer location usable for transferring the cart and instruct the robot to specify the cart transfer location; a graphic user interface (GUI) comprising a map of the facility, the GUI operably connected to the server, the GUI configured to do one or more of receive input from a human user and provide output to the human user, the GUI further configured to be usable by the user to coordinate movement of one or more of robots and carts.

System and method for robot-assisted, cart-based workflows

A system for facility monitoring and reporting to improve safety using one or more robots includes: a network; a plurality of autonomous mobile robots operating in a facility, the robots configured to monitor facility operation, the robots further configured to detect a predetermined critical condition, the robots operably connected to the network; a server operably connected to the robots over the network; and an individual robot operably connected to the server over the network, the individual robot operating in the facility, the robots not comprising the individual robot, the individual robot configured to monitor facility operation; wherein the robots are configured to regularly produce a regular report under normal operating conditions, the report displaying data received from the server, wherein the robots are further configured to produce to the server a critical condition report upon occurrence of the critical condition.

Method and system for facility monitoring and reporting to improve safety using robots

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Astoria, NY, United States of America

Brian Cairl