Abstract: 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.

Abstract: A robot lawnmower includes a body and a drive system carried by the body and configured to maneuver the robot across a lawn. The robot also includes a grass cutter and a swath edge detector, both carried by the body. The swath edge detector is configured to detect a swath edge between cut and uncut grass while the drive system maneuvers the robot across the lawn while following a detected swath edge. The swath edge detector includes a calibrator that monitors uncut grass for calibration of the swath edge detector. In some examples, the calibrator comprises a second swath edge detector.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for identifying a workpiece in a processing environment may utilize one or more sensors for digitally recording visual information and providing that information to an industrial workflow. The sensor(s) may be positioned to record at least one image of the workpiece at a location where a specified position and orientation thereof is required. A processor may determine, from the recorded image(s) and a stored digital model, whether the workpiece conforms to the specified, position and orientation.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for identifying a robot end effector in a processing environment may utilize one or more sensors for digitally recording visual information and providing that information to an industrial workflow. The sensor(s) may be positioned to record at least one image of the robot including the end effector. A processor may determine the identity of the end effector from the recorded image(s) and a library or database stored digital models.

Abstract: Embodiments of the present invention determine the configuration of a workpiece and whether it is actually being handled by a monitored piece of machinery, such as a robot. The problem solved by the invention is especially challenging in real-world factory environments because many objects, most of which are not workpieces, may be in proximity to the machinery. Accordingly, embodiments of the invention utilize semantic understanding to distinguish between workpieces that may become associated with the robot and other objects (and humans) in the workspace that will not, and detect when the robot is carrying a workpiece.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Various embodiments for enforcing safe operation of machinery performing an activity in a three-dimensional (3D) workspace includes computationally generating a 3D spatial representation of the workspace; computationally mapping 3D regions of the workspace corresponding to space occupied by the machinery and a human; and based thereon, restricting operation of the machinery in accordance with a safety protocol during physical performance of the activity.

Abstract: Various embodiments for enforcing safe operation of machinery performing an activity in a three-dimensional (3D) workspace includes computationally generating a 3D spatial representation of the workspace; computationally mapping 3D regions of the workspace corresponding to space occupied by the machinery and a human; and based thereon, restricting operation of the machinery in accordance with a safety protocol during physical performance of the activity.

Abstract: Various embodiments for enforcing safe operation of machinery performing an activity in a three-dimensional (3D) workspace includes computationally generating a 3D spatial representation of the workspace; computationally mapping 3D regions of the workspace corresponding to space occupied by the machinery and a human; and based thereon, restricting operation of the machinery in accordance with a safety protocol during physical performance of the activity.

Abstract: Various embodiments for enforcing safe operation of machinery performing an activity in a three-dimensional (3D) workspace includes computationally generating a 3D spatial representation of the workspace; computationally mapping 3D regions of the workspace corresponding to space occupied by the machinery and a human; and based thereon, restricting operation of the machinery in accordance with a safety protocol during physical performance of the activity.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for calibrating a sensor array for 3D depth sensing include the steps of providing multiple 3D sensors each for (i) illuminating a field of view of the sensor and (ii) generating an output array of pixelwise values indicative of distances to objects within the illuminated a field of view; sequentially causing each of the 3D sensors to generate an output array while other 3D sensors are illuminating their fields of view; and creating an interference matrix from the generated output arrays, the interference matrix indicating, for each of the 3D sensors, a degree of interference by other 3D sensors simultaneously active therewith.

Abstract: 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.