Abstract: An image processing system includes at least two three-dimensional sensors and a controller. Each of the sensors being for illuminating a corresponding field of view of the sensor, and generating an output array of pixelwise values indicative of distances to illuminated objects in the field of view, each sensor being configured to generate illumination at least at two different frequencies so that objects in the corresponding field of view are illuminated at the two different frequencies. The controller is communicably connected to the at least two sensors to receive pixelwise data from each sensor embodying intensity and distance information from the illuminated objects. The controller is configured to disambiguate a distance to each of the illuminated objects, resolve error in the received pixelwise data due to periodic distance ambiguity, and determine corrected pixelwise values indicative of true distance via each sensor illumination at the at least two different frequencies.

Abstract: A system includes sensors, memory, and a processor. The sensors are configured to capture images of a workspace, and the memory is configured to store the images and a model of machinery located within the workspace. The processor is configured to generate a spatial representation of the workspace based on the captured images. The processor is also configured to identify non-machinery entities within the workspace and, for each of the identified entities, generate an object that specifies a spatial extent of the respective entity and a protective separation distance for the entity. Further, the processor is configured to recognize an interaction between the entities and merge the corresponding objects into a merged object specifying a protective separation distance therefor. Moreover, the processor is configured to control operation of the machinery in accordance with a safety protocol and the protective separation distance for the merged object.

Abstract: This Application regards a system for continuously monitoring a workcell during operation of industrial machinery. The system includes a safety system, a monitoring system, and a controller. The safety system includes a sensor and supporting software or hardware for acquiring image data associated with the workcell. The monitoring system is for detecting a parameter value associated with the safety system. And the controller is configured to determine whether the image data is valid based at least in part on the detected parameter value, and cause an alert to be issued responsive to determining that the image data is invalid.

Abstract: 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.

Abstract: Systems and methods for determining safe and unsafe zones in a workspace—where safe actions are calculated in real time based on all relevant objects (e.g., some observed by sensors and others computationally generated based on analysis of the sensed workspace) and on the current state of the machinery (e.g., a robot) in the workspace—may utilize a variety of workspace-monitoring approaches as well as dynamic modeling of the robot geometry. The future trajectory of the robot(s) and/or the human(s) may be forecast using, e.g., a model of human movement and other forms of control. Modeling and forecasting of the robot may, in some embodiments, make use of data provided by the robot controller that may or may not include safety guarantees.

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: Systems and methods for continuously monitoring a workcell during operation of industrial machinery are disclosed. The system may comprise a safety system that includes at least one sensor and supporting software and/or hardware for acquiring image data associated with the workcell; a monitoring system for detecting a parameter value associated with the safety system; and a controller configured to determine a status of the safety system based at least in part on the detected parameter value and cause an alert to be issued if the status of the safety system does not satisfy a target objective.

Abstract: A measurement system, its assembly and use are disclose. The system may include an instrument for making sensor measurements. The instrument has a substantially cylindrical housing. The shape and size allow the instrument to easily fit in an average hand enabling handheld operation. The housing houses a board stack of electronic boards. These electronics drive an electrical signal in at least one drive channel and measure responses from at least two sensing channels. These responses are provided to a processor for analysis. The instrument has a sensor connector that enables simultaneous electrical and mechanical attachment of an end effector.

Abstract: An integrated system and method for monitoring a workspace, classifying regions therein, dynamically identifying safe states, and identifying and tracking workpieces utilizes semantic analysis of a robot in the workspace and identification of the workpieces with which it interacts, as well as a semantic understanding of entity properties both governing and arising from interaction with other entities for control purposes. These properties may be stored in a profile corresponding to the entity; depending on the implementation, the profile and methods associated with the entity may be stored in a data structure corresponding to the entity “class.” Volumetric regions (e.g., voxels) corresponding to each entity may be tracked and used to implement object methods and/or control methods. For example, machinery may be controlled in accordance with the attributes specified by the objects corresponding to identified entities in a monitored workspace.

Abstract: Crosstalk mitigation among cameras in neighboring monitored workcells is achieved by computationally defining a noninterference scheme that respects the independent monitoring and operation of each workcell. The scheme may involve communication between adjacent cells to adjudicate non-interfering camera operation or system-wide mapping of interference risks and mitigation thereof. Mitigation strategies can involve spread-spectrum techniques.

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: 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: In various embodiments, systems and methods for safely operating machinery such as industrial robots signal intrusions into and/or above computationally defined safeguarded volumes in a workspace during sensor monitoring of the workspace. Various related actions may be taken in response thereto, including restricting operation of the machinery and/or issuing safety or occlusion signals.

Abstract: Systems and methods for determining safe and unsafe zones in a workspace—where safe actions are calculated in real time based on all relevant objects (e.g., some observed by sensors and others computationally generated based on analysis of the sensed workspace) and on the current state of the machinery (e.g., a robot) in the workspace—may utilize a variety of workspace-monitoring approaches as well as dynamic modeling of the robot geometry. The future trajectory of the robot(s) and/or the human(s) may be forecast using, e.g., a model of human movement and other forms of control. Modeling and forecasting of the robot may, in some embodiments, make use of data provided by the robot controller that may or may not include safety guarantees.

Abstract: System and method for characterizing material condition. The system includes a sensor, impedance instrument and processing unit to collect measurements and assess material properties. A model of the system may be used to enable accurate measurements of multiple material properties. A cylindrical model for an electromagnetic field sensor is disclosed for modeling substantially cylindrically symmetric material systems. Sensor designs and data processing approaches are provided to focus the sensitivity of the sensor to localize material conditions. Improved calibration methods are shown. Sizing algorithms are provided to estimate the size of defects such as cracks and corrosion. Corrective measures are provided where the actual material configuration differs from the data processing assumptions. Methods are provided for use of the system to characterize material condition, and detailed illustration is given for corrosion, stress, weld, heat treat, and mechanical damage assessment.

Abstract: Crosstalk mitigation among cameras in neighboring monitored workcells is achieved by computationally defining a noninterference scheme that respects the independent monitoring and operation of each workcell. The scheme may involve communication between adjacent cells to adjudicate non-interfering camera operation or system-wide mapping of interference risks and mitigation thereof. Mitigation strategies can involve time-division and/or frequency-division multiplexing.

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: 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: 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.

Abstract: 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.