A system for determining a discrete number of flexible, non-rigid workpiece items loaded onto a robotic carrier. The system includes a robotic carrier capable of traveling to multiple workstations, at least one of which is a weigh station. A loading mechanism is functional to load one or more workpieces onto the robotic carrier which weighed by the weigh station. By comparing the weight of the loaded items with a predetermined weight range of a single workpiece, the number of discrete workpieces loaded onto the robotic carrier can be determined. In addition, a method can be provided for determine position error of a mobile robot based on a detected center of gravity of the mobile robot.

An autonomous control system includes an acquirer configured to acquire state data of a robot, visual data of the robot, and tactile data of the robot and a processor configured to decide on an action of the robot capable of accomplishing a task given to the robot on the basis of the state data, the visual data, and the tactile data. The processor generates first compressed data having a smaller number of dimensions than data obtained by combining the visual data and the tactile data by fusing and dimensionally compressing the visual data and the tactile data. The processor generates second compressed data having a smaller number of dimensions than the tactile data by dimensionally compressing the tactile data. The processor decides on the action on the basis of combined state data obtained by combining the state data, the first compressed data, and the second compressed data into one.

A surface following control method for a robot is used for controlling the robot including a hand part, an arm part, and a controller. In this surface following control method for the robot, processes including a normal direction identification process and a work tool posture control process are performed. In the normal direction identification process, a normal direction of a virtual shape at a virtual position where the work tool attached to the hand part contacts the virtual shape which is a shape represented by the formula is identified. In the work tool control process, the work tool attached to the hand part is brought into contact with the target workpiece at a corresponding position which is a position corresponding to the virtual position on the surface of the target workpiece, in a posture along the normal direction identified in the normal direction identification process.

This patent proposal document provides a complete robot hand control scheme using myoelectric intention estimation of the human being using the kernel Principal Component Analysis Algorithm (kPCA). The robot hand system includes a biometric EMG sensor system, a robot hand including with multiple fingers, a controller connected with the biometric EMG sensor system, and a robot hand. The controller acquires the biometric EMG signal by means of a biometric sensor system, estimates myoelectric motion intention by applying the kernel principal component analysis (kPCA) algorithm using a kernel function, and delivers a control command corresponding to the estimated motion intention of the user to the robot hand.

A material transport device including a transfer hand for receiving a material from a counterpart device or delivering the material to the counterpart device includes an unmanned transport vehicle moving along a preset path, a main body disposed on the unmanned transport vehicle, and a transfer hand disposed inside the main body, at least partially protruding outward from the main body, loading or unloading the material, and including a positioning sensor detecting a marker disposed on the counterpart device and determining a position difference with the counterpart device, and the material transport device calibrates a position of the transfer hand based on the position difference determined by the positioning sensor.

A subsea manipulator for a remotely operated underwater vehicle (ROV) that includes at least one linear, oil-filled electric actuator to control a motion of the manipulator in a subsea environment is disclosed. The remotely operated underwater manipulator includes an electric actuator for each axis of motion of the manipulator, and an end effector that includes a rotational joint and a tool motor for controlling a tool affixed to the end effector. A method for changing the tool of the manipulator in a subsea environment is disclosed.

The automatic operation system includes a plurality of learned imitation models and a model selecting unit. The learned imitation models are constructed by machine learning of operation history data, the operation history data being classified into several groups by an automatic classification system algorithm, the operation history data of each group being learned by the imitation model corresponding to the group. The operation history data include data indicating a surrounding environment and data indicating an operation of an operator in the surrounding environment. The model selecting unit selects one imitation model from several imitation models based on a result of classifying data indicating a given surrounding environment by the automatic classification algorithm of the classification system. The automatic operation system inputs data indicating the surrounding environment to the imitation model selected by the model selecting unit to predict an operation of the operator with respect to the surrounding environment.

A robotic surgical system for treating a patient is disclosed including a surgical tool movable relative to the patient and a user input device including a base and a space joint including a central portion movable relative to the base to effect a motion. The robotic surgical system further includes a control circuit configured to receive a user selection signal indicative of a selection between a first motion scaling profile of the motion of the surgical tool and a second motion scaling profile of the motion of the surgical tool, receive a motion control signal from the user input device indicative of a user input force, and cause the surgical tool to be moved in response to the motion control signal in accordance with the first motion scaling profile or the second motion scaling profile based on the user selection signal. The first motion scaling profile is different than the second motion scaling profile.

A robotic kitting machine is disclosed. In various embodiments, a robotic arm is used to move an item to a location in proximity to a slot into which the item is to be inserted. Force information generated by a force sensor is received via a communication interface. The force sensor information is used to align a structure comprising the item with a corresponding cavity comprising the slot, and the item is inserted into the slot.

A fiber includes M primary cores and N redundant cores, where M an integer is greater than two and N is an integer greater than one. Interferometric circuitry detects interferometric pattern data associated with the M primary cores and the N redundant cores when the optical fiber is placed into a sensing position. Data processing circuitry calculates a primary core fiber bend value for the M primary cores and a redundant core fiber bend value for the N redundant cores based on a predetermined geometry of the M primary cores and the N redundant cores in the fiber and detected interferometric pattern data associated with the M primary cores and the N redundant cores. The primary core fiber bend value and the redundant core fiber bend value are compared in a comparison. The detected data for the M primary cores is determined reliable or unreliable based on the comparison. A signal is generated in response to an unreliable determination.