Abstract: A control apparatus of a robot may include a state obtaining unit configured to obtain state observation data including flexible related observation data, which is observation data regarding a state of at least one of a flexible portion, a portion of the robot on a side where an object is gripped relative to the flexible portion, and the gripped object; and a controller configured to control the robot so as to output an action to be performed by the robot to perform predetermined work on the object, in response to receiving the state observation data, based on output obtained as a result of inputting the state observation data obtained by the state obtaining unit to a learning model, the learning model being learned in advance through machine learning and included in the controller.

Abstract: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Abstract: Present embodiments describe a method for controlling a robotic vehicle that can include causing a processor to determine a localized position of a dynamic object, determining, via the processor, a predicted trajectory of the robotic vehicle based on the localized position of the dynamic object, and determining an optimum trajectory based on the predicted trajectory, the optimum trajectory chosen based a travel velocity and longitudinal velocity. Determining the optimum trajectory can include computing a cost value comprising a plurality of cost terms associated with the optimum trajectory, and determining a control command that modifies a travel velocity and a travel vector of the robotic vehicle based on the cost value. The method further includes causing, via the processor, the robotic vehicle to follow the optimal trajectory based on the control command.

Abstract: A robot control system includes a robot that screw-fastens a sub-component placed in a main-component to the main-component, a robot control unit that controls the robot, and a display as an output unit through which the robot control unit outputs a message to an operator. The robot control unit determines whether or not the robot can screw-fasten the sub-component to the main-component by itself. Then, when the robot control unit determines that the robot cannot screw-fasten the sub-component to the main-component by itself, the robot control unit outputs a cooperation message to an operator through the display.

Abstract: In an angular velocity calculation block, an angular velocity component is calculated based on a position command for a joint portion. In a kinetic calculation block, a kinetic torque is calculated based on the position command for the joint portion. In a command velocity component reversal detection block, a reversal timing is calculated based on the angular velocity component. In a reverse torque detection block, a reverse torque is calculated based on the kinetic torque and the reversal timing. In a backlash correction amount calculation block, the correction amount of the joint portion is calculated based on the kinetic torque, the reverse torque, and the reversal timing.

Abstract: A robot control device, which causes a robot mounted on a transport vehicle to be stopped on the basis of an external force applied to the robot, is equipped with a determination unit configured to determine whether or not the transport vehicle is currently traveling, and a control unit configured to cause the robot to be stopped in the case that the transport vehicle is currently traveling, and to enable the robot to be operated in the case that the transport vehicle is not currently traveling.

Abstract: A control method includes (a) measuring a misregistration index value relating to misregistration of a distal end portion of a manipulator by controlling the manipulator to perform a test operation, and (b) setting a limit value for an operation of the manipulator to prevent the misregistration index value from exceeding a predetermined threshold value.

Abstract: A method for managing elevator operation, a device for managing elevator operation, an elevator system and a computer-readable storage medium. The method for managing elevator operation includes: establishing a communication connection with at least one of robots movably arranged in a preset area; receiving data information from the robot, the data information including elevator service information generated by the robot based on input information related to an elevator visitor; and controlling the operation of the elevator and/or the operation of at least another one of the robots according to the received data information.

Abstract: In a trajectory generation apparatus, position coordinates of an obstacle existing in a motion space of a robot arm is acquired. A hand position at a second time, which is a time next to a first time, is estimated by using a learning result of machine learning, based on the position coordinates of the obstacle, a subject joint state of the robot arm at the first time, and a target joint state of the robot arm. A non-interfering joint state of the robot arm at which the obstacle does not interfere with the robot arm at the second time is searched for by using the hand position as a restriction.

Abstract: A robot for performing an assembly operation is provided. The robot comprises a processor configured to determine a control law for controlling a plurality of motors of the robot to move a robotic arm according to an original trajectory, execute a self-exploration program to produce training data indicative of a space of the original trajectory, and learn, using the training data, a non-linear compliant control law including a non-linear mapping that maps measurements of a force sensor of the robot to a direction of corrections to the original trajectory defining the control law. The processor transforms the original trajectory according to a new goal pose to produce a transformed trajectory, update the control law according to the transformed trajectory to produce the updated control law, and command the plurality of motors to control the robotic arm according to the updated control law corrected with the compliance control law.

Abstract: Disclosed is a control method of a moving robot configured to drive a work area where a plurality of beacons, the control method comprising a radio direction acquiring step of acquiring radio direction information from the beacons; a first distance tracking step of tracking first distance information between the moving robot and the beacons based on the radio direction information, after the radio direction acquiring step; a second distance tracking step of tracking second distance information between the moving robot and an object existing in the work area via a distance sensor provided in the moving robot; and a first comparison step of comparing the first distance information with the second distance information, wherein location recognition of the moving robot is performed based on the radio direction information, when the first distance is equal to the second distance based on the result of the first comparison step.

Abstract: A direct force feedback control method as well as a controller and a robot using the same are provided. The method includes: obtaining an actual position and an actual speed of an end of the robotic arm and an actual external force acting on the end in a Cartesian space; calculating an impedance control component of the end in the Cartesian space based on the obtained actual position, the obtained actual speed, the obtained actual external force, an expected position, an expected speed, and an expected acceleration of the end; calculating a force control component of the end in the Cartesian space based on an expected interaction force acting on the end, the actual external force, and the actual speed; determining whether the actual external force is larger than a preset threshold, and obtaining a total force control quantity of the end of the robotic arm in the Cartesian space.

Abstract: An autonomous mobile robot includes a first arithmetic unit configured to calculate a course direction based on an own position, a moving-object position, and a moving-object velocity vector, the course direction being a direction in which the autonomous mobile robot should travel, a second arithmetic unit configured to input the own position, the moving-object position, the moving-object velocity vector, and the course direction into a trained model and thereby calculate an estimated position, the trained model being a model that has been trained, the estimated position being a position at which the autonomous mobile robot is estimated to arrive a predetermined time later without colliding with the moving object, a generating unit configured to generate a remaining route from the estimated position to a destination, and a movement control unit configured to control a movement to the destination based on the course direction and the remaining route.

Abstract: A mobile robot device and a method for controlling a mobile robot device to more accurately control positioning of the mobile robot device relative to a docking station. The mobile robot device obtains a first position relative to a docking station by scanning surroundings of the mobile robot device using a LiDAR sensor emitting a first frequency, moves the mobile robot device towards the charging station based on the first position, determines whether a distance to the charging station is within a first distance, controls the LiDAR sensor to scan the surroundings of the mobile robot device by changing a frequency of the LiDAR sensor to a second frequency less than the first frequency when the mobile robot device approaches within the first distance, and moves the mobile robot device towards the charging station based on the LiDAR sensor emitting the second frequency.

Abstract: Robot localization or mapping can be provided without requiring the expense or complexity of an “at-a-distance” sensor, such as a camera, a LIDAR sensor, or the like. Landmark features can be created or matched using motion sensor data, such as odometry or gyro data or the like, and adjacency sensor data. Despite the relative ambiguity of adjacency-sensor derived landmark features, a particle filter approach can be configured to use such information, instead of requiring “at-a-distance” information from a constant stream of visual images from a camera, such as for robot localization or mapping. Landmark sequence constraints or a Wi-Fi signal strength map can be used together with the particle filter approach.

Abstract: A method for reducing vibration of a robot arm includes: a step of mounting at least one inertia actuator and at least one vibration signal capturing unit to a processing end of a robot arm; a step of applying the at least one vibration signal capturing unit to detect a vibration generated at the processing end of the robot arm so as to generate a vibration signal; a step of applying a central processing unit to evaluate the vibration signal and coordinates of the processing end of the robot arm so as to capture at least one set of corresponding control parameters for calculating at least one output force; and, a step of having the inertia actuator to apply the output force to the processing end of the robot arm for counteracting the vibration at the processing end of the robot arm.