A robot management system (RMS) includes a plurality of service robots deployed within an operations venue that includes a plurality of gaming devices, an operator terminal presenting a graphical user interface (GUI) to an operator, and a robot management system server (RMS server) configured in networked communication with the plurality of service robots. The RMS server is configured to: identify location data for the service robots; create an interactive overlay map of the operations venue that includes a static map of the operations venue, overlay data showing the location data of the plurality of service robots over the static map, and an interactive icon for each service robot of the plurality of service robots; display, via the GUI, the overlay map; receive a first input indicating a selection of a first interactive icon associated with a first service robot; and display current status information associated with the first service robot.

A collaborative robot cutting system for the assembly, construction, fabrication, and/or the completion of structural components for manufactured assemblies. A method of preparing work pieces and materials for further manufacturing operations employing the intuitive graphical interactive programming features of a robot cutting system user interface to enhance productivity and versatility in high mix, low volume fabrication environments with minimal operator training.

In an embodiment, a method during execution of a motion plan by a robotic arm includes determining a voice command from speech of a user said during the execution of the motion plan, determining a modification of the motion plan based on the voice command from the speech of the user, and executing the modification of the motion plan by the robotic arm.

A system for detecting an anomaly in an execution of a task in mixed human-robot processes. Receiving human worker (HW) signals and robot signals. A processor to extract from the HW signals, task information, measurements relating to a state of the HW, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, to determine a classification of an anomaly or no anomaly. Update HRI model with robot operation signals, HW signals and classified anomaly, determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and classified anomaly. Output the control action of the robot to change a robot action or output the type of the anomaly alarm.

A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.

A control device 1C mainly includes an operation sequence generation means 16C and a synchronization management means 17C. The operation sequence generation means 16C is configured to generate, based on an operation prediction result R2a of another working body which performs cooperative work with a robot which executes a task, an operation sequence Sra to be executed by the robot. The synchronization management means 17C is configured to synchronize an operation executed by the robot during execution of the operation sequence Sra and an operation executed by the other working body.

In an embodiment, a method and system use various sensors to determine a shape of a collection of materials (e.g., foodstuffs). A controller can determine a trajectory which achieves the desired end-state, possibly chosen from a set of feasible, collision-free trajectories to execute, and a robot executes that trajectory. The robot, executing that trajectory, scoops, grabs, or otherwise acquires the desired amount of material from the collection of materials at a desired location. The robot then deposits the collected material in the desired receptacle at a specific location and orientation.

A robot system includes a robot main body, a plurality of first control devices provided in the robot main body, and a detection unit configured to detect a state of the robot main body. In a case where one of the plurality of first control devices determines that the robot main body is in a predetermined state based on a detection result of the detection unit, the one of the plurality of first control devices outputs information indicating that the robot main body is in the predetermined state to another one of the plurality of first control devices other than the one of the plurality of first control devices.

A method for monitoring a working environment of a movable device utilizing a monitoring apparatus, wherein the working environment includes a working area and at least one protected area and the movable device is located within the working area during normal operation of said movable device, where the method includes a user inputting a plurality of convex polytopes into the monitoring apparatus, the convex polytopes corresponding to areas in which the movable device is located during normal operation, determining a convex polytope hull using the monitoring apparatus, the convex polytope hull completely enclosing the multiplicity of convex polytopes, and determining the at least one protected area by calculating a difference from the convex polytope hull and the input using the monitoring apparatus, such that monitoring of the position of the movable device is simplified because the working area can be modeled autonomously.

A system for automated guided vehicle safety may include an automated guided vehicle (AGV) having a propulsion system configured to move the AGV, and a processor configured to control the propulsion system, and a laser imaging system configured to deploy a virtual safety fence at least partially surrounding the AGV. The laser imaging system may include a plurality of laser imaging sensors including a front sensor and a rear sensor, and a movable boom, the front sensor being mounted to the movable boom and configured to extend in front of the housing of the AGV.