Veo Robotics, Inc.

Symbotic LLC

Avery Dennison Corporation

Avery Dennison Retail Information Services, LLC

Intellivid Corporation

Patrick Sobalvarro

Clara Vu

Scott A Denenberg

Christopher J Buehler

Alberto Moel

Patrick Barragan

Lev Persits

Ilya A Kriveshko

Richard A Kelsey

Valentina Chamorro

Gene Malkin

Thomas C Ferree

Paul Jakob Schroeder

Marek Wartenberg

Brad C Mello

Neil Brock

Patrick Joseph Foy

Robert Craig Randall

Joshua Downer

Michael Redd

Michaelyn Molly McCarthy

Paulo Ferreira

Micah Waldman

Mehmet Ali Guney

Rafael Maya

Dmitriy Dedkov

Elliot Simon

Justin Bronder

Matthew Galligan

Jehanbux Edulbehram

Patrick J Foy

Paul Jacob Schroeder

Spatial regions potentially occupied by a robot (or other machinery) or portion thereof and a human operator during performance of all or a defined portion of a task or an application are computationally estimated. These 'potential occupancy envelopes' (POEs) may be based on the states (e.g., the current and expected positions, velocities, accelerations, geometry and/or kinematics) of the robot and the human operator. Once the POEs of human operators in the workspace are established, they can be used to guide or revise motion planning for task execution.

Motion planning and task execution using potential occupancy envelopes

A method of safely operating machinery in a workspace includes recording images of a portion of a workspace. The method also includes generating a three-dimensional (3D) representation of the portion of the workspace based on the recorded images, where the 3D representation includes one or more volumes that correspond to the portion of the workspace. Additionally, the method includes identifying one or more of the volumes as being either occupied or unoccupied. Further, the method includes mapping one or more safe zones based on the one or more identified volumes, where the safe zones correspond to one or more regions within the portion of the workspace for safe operation of machinery.

Safe motion planning for machinery operation

In various embodiments, systems and methods for acquiring depth images utilize an architecture suited to safety-rated applications, and may include more than sensor (such as time-of-flight sensors) operating along different optical paths and a comparison module for ensuring proper sensor operation. Error metrics may be associated with pixel-level depth values for purposes of allowing safe control based on imperfectly known depths.

Depth-sensing computer vision system

Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated. Based on the mapping, a constrained motion plan of machinery can be generated to ensure safety.

Control systems for industrial machinery (e.g., robots) or other devices such as medical devices utilize a safety processor (SP) designed for integration into safety applications and computational components that are not necessarily safety-rated. The SP monitors performance of the non-safety computational components, including latency checks and verification of identical outputs. One or more sensors send data to the non-safety computational components for sophisticated processing and analysis that the SP cannot not perform, but the results of this processing are sent to the SP, which then generates safety-rated signals to the machinery or device being controlled by the SP. As a result, the system may qualify for a safety rating despite the ability to perform complex operations beyond the scope of safety-rated components.

System architecture for safety applications

Embodiments of the present invention provide automated robotic system identification and stopping time and distance estimation, significantly improving on existing ad-hoc methods of robotic system identification. Systems and methods in accordance herewith can be used by end users, system integrators, and the robot manufacturers to estimate the dynamic parameters of a robot on an application-by-application basis.

System identification of industrial robot dynamics for safety-critical applications

Systems and methods for optimizing factory scheduling, layout or both which represent active factory elements (human and machine) as computational objects and simulate factory operation to optimize a solution. This enables the efficient assembly of customized products, accommodates variable demand, and mitigates unplanned events (floor blockages, machines/IMRs/workcell/workers downtime, variable quantity, location, and destination of supply parts).

Optimized factory schedule and layout generation

Safety systems in distributed factory workcells intercommunicate or communicate with a central controller so that when a person, robot or vehicle passes from one workcell or space into another on the same factory floor, the new workcell or space need not repeat the tasks of analysis and classification and can instead immediately integrate the new entrant into the existing workcell or space-monitoring schema. The workcell or space can also communicate attributes such as occlusions, unsafe areas, movement speed, and object trajectories, enabling rapid reaction by the monitoring system of the new workcell or space.

Safety-rated multi-cell workspace mapping and monitoring

Various approaches to ensuring safe operation of industrial machinery in a workcell include disposing multiple image sensors proximate to the workcell and acquiring, with at least some of the image sensors, the first set of images of the workcell; registering the sensors to each other based at least in part on the first set of images and, based at least in part on the registration, converting the first set of images to a common reference frame of the sensors; determining a transformation matrix for transforming the common reference frame of the sensors to a global frame of the workcell; registering the sensors to the industrial machinery; acquiring the second set of images during operation of the industrial machinery; and monitoring the industrial machinery during operation thereof based at least in part on the acquired second plurality of images, transformation, and registration of the sensors to the industrial machinery.

Systems and methods for automatic sensor registration and configuration

Systems and methods for determining safe zones in a workspace calculate safe actions in real time based on all sensed relevant objects and on the current state of the machinery (e.g., a robot) in the workspace. Various embodiments forecast, in real time, both the motion of the machinery and the possible motion of a human within the space, and continuously update the forecast as the machinery operates and humans move in the workspace.

Dynamically determining workspace safe zones with speed and separation monitoring

Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated.

Ensuring safe operation of industrial machinery

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Harvard, MA, United States of America

Patrick Sobalvarro