A lab automation system receives an instruction from a user to perform a protocol within a lab via an interface including a graphical representation of the lab. The lab includes a robot and set of equipment rendered within the graphical representation of the lab. The lab automation system identifies an ambiguous term of the instruction and pieces of equipment corresponding to the ambiguous term and modifies the interface to include a predictive text interface element listing the pieces of equipment. Upon a mouseover of a listed piece of equipment within the predictive text interface element, the lab automation system modifies the graphical representation of the lab to highlight the listed piece of equipment corresponding to the mouseover. Upon a selection of the listed piece of equipment within the predictive text interface element, the lab automation system modifies the instruction to include the listed piece of equipment.

Provided is a teaching device including a grouping unit which divides machining points into machining point groups so that a machining head can sequentially machine each machining point for a machining time and so that a non-machining time can be minimized, a machining path determination unit which determines a machining path on which an in-group movement time of a robot is shortest for each machining point group, a teaching process adjustment unit which adjusts a machining order of the machining points and an operation order of the machining point groups so as to minimize a distance between groups and which optimizes the grouping so as to minimize a total movement time for completing machining, and a teaching data output unit which outputs, as teaching data, machining execution positions on the machining path obtained as a result of processing of the teaching process adjustment.

Provided is a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing visual readings to objects within an environment; capturing readings of wheel rotation; capturing readings of a driving surface; capturing distances to obstacles; determining displacement of the robotic device in two dimensions based on sensor readings of the driving surface; estimating, with the processor, a corrected position of the robotic device to replace a last known position of the robotic device; determining a most feasible element in an ensemble based on the visual readings; and determining a most feasible position of the robotic device as the corrected position based on the most feasible element in the ensemble and the visual readings.

A simulation apparatus acquires a position and an operating speed in each drive axis of the robot at a set point set for each minute section of a motion path of the robot when an operation program of a robot is executed. The simulation apparatus comprises a stop position estimation part that estimates a stop position where the robot is stopped after moving by inertia in each dive axis, based on the position in each drive axis of the robot, the operating speed in each drive axis, and the weight of the work tool, when an emergency stop of the robot is performed at the set point. The simulation apparatus comprises a swept space calculation part that calculates a swept space of three-dimensional models of the robot and the work tool based on the stop position.

A method, device, and system for configuring a coating machine for coating a surface of a product using a coating substance are provided. The method includes determining a value associated with one or more parameters from a plurality of parameters associated with the coating operation. The method also includes predicting a value associated with at least one attribute associable with the coating substance based on the determined value associated with the one or more parameters using a trained machine learning model. The method includes configuring the coating machine for coating the surface using the coating substance based on the predicted value associated with the at least one attribute associable with the coating substance. The method also includes initiating a coating operation at the configured coating machine for coating the surface of the product using the coating substance.

A virtualization system implemented within a cloud server enables the simulation of robot structure and behavior in a virtual environment. The simulated robots are controlled by clients remote from the cloud server, enabling human operators or autonomous robot control programs running on the clients to control the movement and behavior of the simulated robots within the virtual environment. Data describing interactions between robots, the virtual environment, and objects can be recorded for use in future robot design. The virtualization system can include robot templates, enabling users to quickly select and customize a robot to be simulated, and further enabling users to update and re-customize the robot in real-time during the simulation. The virtualization system can re-simulate a portion of the robot simulation when an intervention by a human operator is detected, positioning robots, people, and objects within the virtual environment based on the detected intervention.

A system for parameter tuning for robotic manipulators is provided. The system includes an interface configured to receive a task specification, a plurality of physical parameters, and a plurality of control parameters, wherein the interface is configured to communicate with a real-world robot via a robot controller. The system further includes a memory to store computer-executable programs including a robot simulation module, a robot controller, and an auto-tuning module a processor, in connection with the memory. In this case, the processor is configured to acquire, in communication with the real-world robot, state values of the real-world robot, state values of the robot simulation module, simultaneously update, by use of a predetermined optimization algorithm with the auto-tuning module, an estimate of one or more of the physical, and said control parameters, and store the updated parameters.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

In various examples, a computer-implemented method of generating instructions for a welding robot. The computer-implemented method comprises identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part, scanning a workspace containing the part to produce a representation of the part, identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam, determining an actual position of the candidate seam, and generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.

A system includes: a first module configured to, based on a set of target robot joint angles, generate a first estimated end effector pose and a first estimated latent variable that is a first intermediate variable between the set of target robot joint angles and the first estimated end effector pose; a second module configured to determine a set of estimated robot joint angles based on the first estimated latent variable and a target end effector pose; a third module configured to determine joint probabilities for the robot based on the first estimated latent variable and the target end effector pose; and a fourth module configured to, based on the set of estimated robot joint angles, determine a second estimated end effector pose and a second estimated latent variable that is a second intermediate variable between the set of estimated robot joint angles and the second estimated end effector pose.