Abstract: The present disclosure relates to a method, an apparatus and a system for controlling a robot, and a storage medium. The method uses a neural network connected to an external memory to conduct the controlling of the robot, and comprises: inputting input data into the learned neural network to obtain output data, wherein said input data comprises an image about an object, said output data comprises control data about said robot; and establishing an association between part or all of the information generated by said neural network during the calculation and said input data and/or said output data, wherein said part or all of the information represents a feature of said object related to said control data. Thus, the user can grasp the calculation process of the neural network.

Abstract: A horizontal articulated robot includes a base; a first arm coupled to the base and configured to turn around a first axis, a second arm coupled to the first arm and configured to turn around a second axis parallel to the first axis, a third arm coupled to the second arm and configured to turn around a third axis parallel to the first axis and move along the third axis, a supporting section provided in the second arm and configured to support the third arm, and a force detecting section provided between the second arm and the supporting section and configured to detect force applied to the third arm.

Abstract: The present invention features a computer-implemented method for adjustably distributing cooperative motion between a first manipulator and a second manipulator in a manufacturing processing system. The method includes receiving, by a computing device, data for the first manipulator configured to hold a tool, data for the second manipulator configured to hold a workpiece, and process data defining a process to be performed by the tool on at least a portion of the workpiece. The data for at least one of the first or second manipulator comprises a weighting factor adjustable by a user to specify at least a percentage of motion for the corresponding manipulator. The method also includes generating a relative transformation function for defining the process path and distributing motions between the first and second manipulators to complete the process path based on the at least one weighting factor.

Abstract: A grasping robot includes: a grasping mechanism configured to grasp a target object; an image-pickup unit configured to shoot a surrounding environment; an extraction unit configured to extract a graspable part that can be grasped by the grasping mechanism in the surrounding environment by using a learned model that uses an image acquired by the image-pickup unit as an input image; a position detection unit configured to detect a position of the graspable part; a recognition unit configured to recognize a state of the graspable part by referring to a lookup table that associates the position of the graspable part with a movable state thereof; and a grasping control unit configured to control the grasping mechanism so as to displace the graspable part in accordance with the state of the graspable part recognized by the recognition unit.

Abstract: A front blind spot detection and warning system for vehicle comprising: a monitoring element mounted to a front portion of a host vehicle for monitoring the condition of a current lane and a neighboring lane in front of the host vehicle, the monitoring element defining a detectable blind spot zone in a front area of the neighboring lane; and a controller connected with the monitoring element for receiving detected information from the monitoring element; wherein the controller is configured to obtain the running state of the host vehicle and the detected information of the monitoring element and to determine there is a blind spot risk.

Abstract: A method of planning a path for an articulated arm of robot includes generating a directed graph corresponding to a joint space of the articulated arm. The directed graph includes a plurality of nodes each corresponding to a joint pose of the articulated arm. The method also includes generating a planned path from a start node associated with a start pose of the articulated arm to an end node associated with a target pose of the articulated arm. The planned path includes a series of movements along the nodes between the start node and the end node. The method also includes determining when the articulated arm can travel to a subsequent node or the target pose, terminating a movement of the articulated arm towards a target node, and initiating a subsequent movement of the articulated arm to move directly to the target pose or the subsequent node.

Abstract: Disclosed are a robot and a method of controlling the same, wherein a robot arm is moved until a pressure sensor provided at the robot arm senses a predetermined pressure, and the quantity of products arranged on a shelf is counted based on the operation of the robot arm.

Abstract: The present invention provides a robot system capable of, regardless of the type of the robot, precisely measuring the positional relationship between an AR device and markers, and comparatively easily and with high precision recognizing the position or orientation of the robot with the AR device.

Abstract: A mobile robot includes a traveling unit configured to move a main body, a LiDAR sensor configured to acquire geometry information, a camera sensor configured to acquire an image of the outside of the main body, and a controller. The controller generates odometry information based on the geometry information acquired by the LiDAR sensor. The controller determines a current location of the mobile robot by performing feature matching between images acquired by the camera sensor based on the odometry information. The controller combines the information obtained by the camera sensor and the LiDAR sensor to accurately determine the current location of the mobile robot.

Abstract: A robotic network includes multiple work cells that communicate with a cloud server using a network bus (e.g., the Internet). Each work cell includes an interface computer and a robotic system including a robot mechanism and a control circuit. Each robot mechanism includes an end effector/gripper having integral multimodal sensor arrays that measure physical parameter values (sensor data) during interactions between the end effector/gripper and target objects. The cloud server collects and correlates sensor data from all of the work cells to facilitate efficient diagnosis of problematic robotic operations (e.g., accidents/failures), and then automatically updates each work cell with improved operating system versions or AI models (e.g., including indicator parameter value sets and associated secondary robot control signals that may be used by each robot system to detect potential imminent robot accidents/failures during subsequent robot operations.

Abstract: A trajectory control device includes: a contact sensor that can contact side surfaces of a workpiece; an actuator that moves a trajectory tracking member and the contact sensor; and a trajectory controller that calculates XY coordinates of a trajectory on the workpiece that is placed in an arbitrary position, by transforming XY coordinates of the trajectory on the workpiece in a reference position, based on positional information about the side surfaces of the workpiece in the reference position and positional information about the side surfaces of the workpiece placed in the arbitrary position. The positional information about the side surfaces of the workpiece placed in the arbitrary position is obtained by the contact sensor.

Abstract: A system and method for automatically moving one or more items between a structure at a source location and a destination using a robot is provided. The system includes first and second vision systems to identify an item and to determine the precise location and orientation of the item at the source location and the precise location and orientation of the destination, which may or may not be in a fixed location. A controller plans the best path for the robot to follow in moving the item between the source location and the destination. An end effector on the robot picks the item from the source location, holds it as the robot moves, and places the item at the destination. The system may also check the item for quality by one or both of the vision systems. An example of loading and unloading baskets from a machine is provided.

Abstract: A flexible manipulator apparatus includes an elongate flexible manipulator having a sensor, a user output device configured to provide sensory outputs to the user, and processing circuitry. The flexible manipulator may be movable to form a curve in the flexible manipulator. The processing circuitry may be configured to receive captured sensor data from the sensor during movement of the flexible manipulator, and determine a collision likelihood score based on application of the captured sensor data to a collision detection model used for position estimation. The collision detection model may be based on an empirical data training for the flexible manipulator that includes training sensor data from the sensor and training image data of positions of the flexible manipulator. The processing circuitry may be configured to control the user output device based on the collision likelihood score to provide a collision alert sensory output to the user.

Abstract: In a control method by a robot system, in the robot system including a plurality of robots and a teaching device connected to the plurality of robots via a network, the teaching device transmits, to an identification act execution robot among the plurality of robots, an identification act instruction signal for causing the identification act execution robot to perform an identification act of identifying a transmission target robot to which operation data is planned to be transmitted among the plurality of robots, the identification act execution robot, which receives the identification act instruction signal, performs the identification act, and, after the identification act execution robot performs the identification act, when the identification act execution robot and the transmission target robot are the same, the teaching device transmits the operation data to the transmission target robot.

Abstract: A method includes receiving, from an imaging device associated with a robotic apparatus, an image signal including an object of interest represented in the image signal; receiving, by the robotic apparatus from an external agent, a task indication related to the object of interest, the task indication representative of a task to be performed by the robotic apparatus with respect to the object of interest; and responsive to receipt of the task indication: detecting salient features of the object of interest within the image; storing, by the robotic apparatus, a task context, the task context comprising data pertaining to the salient features and data pertaining to the task indication; and commencing, by the robotic apparatus, the task with respect to the object of interest.

Abstract: A method, computer system, and computer program product for optimizing a number of robots for operation of a process at a target system. The method may include providing a plurality of available robots to carry out tasks in the process at the target system. The method may monitor the target system by carrying out the process or part of the process with a varying number of robots to determine the processor utilization whilst the robots are executing a varying number of tasks. The method may balance process constraints of the execution of the process with physical system constraints of the target system by measuring a relationship between a number of tasks at a transactional level and the processor utilization. The method may output the optimized number of robots to be allocated for the process or part of the process.

Abstract: A braking system and method utilizing a simplified estimate of a distance between two locations on the earth based on spherical geometry. A braking system utilizing the aforementioned simplified estimate does not require computationally intensive calculations and is more efficient and better equipped to handle real-time generation of distance estimates for braking needs and variable conditions. In the present invention, geodesics evaluations are not used; rather, a modified Haversine formula that simplifies computations is used, including a one-time computation of the cosine of latitude coordinate.

Abstract: The disclosure is directed at a method and apparatus for an active convertor dolly for use in a tractor-trailer configuration. In one embodiment, the apparatus includes a system to connect a tractor to a trailer. The apparatus further includes a charge generating system for translating the mechanical motions or actions of the dolly into electricity or electrical energy so that this energy can be used to charge a battery or to power other functionality for either the dolly or the tractor-trailer. The active dolly may also operate to assist in shunting the tractor-trailer.

Abstract: A device, a method and a program, by which a weight and a horizontal position of a gravity center of a load attached to a movable part of a robot can be estimated by a simple configuration. The device has: two torque sensors configured to detect a first torque applied to a first axis of a robot, and a second torque applied to a second axis of the robot; and a calculation section configured to calculate a weight and a horizontal position of a gravity center of a workpiece, by using two detection values of the torque sensors in one posture in which a hand attached to a movable part of the robot holds the workpiece.

Abstract: A method for controlling an arm of a robot to imitate a human arm, includes: acquiring first pose information of key points of a human arm to be imitated; converting the first pose information into second pose information of key points of an arm of a robot; determining an angle value of each joint of the arm according to inverse kinematics of the arm based on the second pose information; and controlling the arm to move according to the angle values.