Abstract: A detection system and detection method for the sensors of a robot. A detection system installs three sensors at the motor side and power output terminal of the robot. A detection unit detects the normal or abnormal state of three sensors to index the abnormal sensor for maintenance, and two normal sensors are selected for keeping the robot safety operation without stop.

Abstract: A robot system includes a robot including an arm, a control section configured to control operation of the robot, a gripping section coupled to the arm and configured to grip a cable, at one end of which a connector is provided, and a detecting section configured to detect contact of the gripping section and the connector. The control section causes the gripping section to perform first gripping for gripping the cable to restrict movement of the cable in a thickness direction of the cable, moves the gripping section toward the connector in a state in which the first gripping is performed, stops the movement of the gripping section based on a detection result of the detecting section, and causes the gripping section to perform second gripping for gripping the connector.

Abstract: A teleoperations system may be used to select from among multiple scenarios generated by an autonomous vehicle based upon context data provided to the teleoperations system by the autonomous vehicle. Furthermore, an autonomous vehicle may validate a selected scenario prior to executing that scenario to confirm that the scenario does not violate any vehicle and environmental constraints for the autonomous vehicle. Further, a user interface may be presented to a teleoperations system operator to coordinate the display representations of different scenarios with those of the user interface controls used to select such scenarios.

Abstract: An automatic control method of a mechanical arm and an automatic control system are provided. The automatic control method includes the following steps: obtaining a color image and depth information corresponding to the color image through a depth camera; performing image space cutting processing and image rotation processing according to the color image and the depth information to generate a plurality of depth images; inputting the depth images into an environmental image recognition module such that the environmental image recognition module outputs a displacement coordinate parameter; and outputting the displacement coordinate parameter to a mechanical arm control module such that the mechanical arm control module controls the mechanical arm to move according to the displacement coordinate parameter.

Abstract: A method for correcting a position of a vehicle when parking in a parking space. The method includes determining the position of the vehicle on the basis of odometry information, sensing ultrasonic signals from a linear object, carrying out a method for simultaneous localization and mapping (SLAM) on the basis of the linear object and the ultrasonic signals, and correcting the position of the vehicle A control device for a driving support system of a vehicle is also disclosed, which is designed to receive odometry information of the vehicle, to receive ultrasonic signals from at least one ultrasonic sensor of the driving support system, and to carry out the aforementioned method. A driving support system for a vehicle with an aforementioned control device and with at least one ultrasonic sensor is disclosed. The invention likewise relates to a vehicle with an aforementioned driving support system.

Abstract: One or more embodiments of the present disclosure relate generally to the field of robotic grasping systems, and in particular to an active robotic manipulator that includes a passive grasping component so that the robotic manipulator can grasp a wide variety of objects and simultaneously provide soft grasping features which reduce the risk of damage to objects.

Abstract: A method for computing joint torques applied by actuators to perform a control of a movement of a robot arm having several degrees of freedom is provided. The method includes the act of providing, by a trajectory generator, trajectory vectors specifying a desired trajectory of the robot arm for each degree of freedom. The trajectory vectors are mapped to corresponding latent representation vectors that capture inherent properties of the robot arm using basis functions with trained parameters. The latent representation vectors are multiplied with trained core tensors to compute the joint torques for each degree of freedom.

Abstract: A computer readable medium causing a computer to planning of a trajectory for an object that is capable of controlled movement in one or more degrees of freedom. At least one ordered sequence of movement profiles is obtained. The at least one ordered sequence includes: (i) movement profiles that end with a phase of increasing acceleration and (ii) movement profiles that end with a phase of decreasing acceleration. The ordered sequence is evaluated to select a movement profile capable of achieving a desired state of movement. The trajectory of the object is planned based on the selected movement profile.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing user feedback for robotic demonstration learning. One of the methods includes initiating a local demonstration learning process to collect respective local demonstration data for each of one or more demonstration subtasks defined by a skill template to be executed by a robot. Local demonstration data is repeatedly collected for each of the one or more demonstration subtasks of the skill template while a user manipulates a robot to perform each of the one or more demonstration subtasks defined by the skill template. A respective progress value for each of the one or more demonstration subtasks defined by the skill template is maintained. A user interface presentation is generated that presents a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask.

Abstract: A trajectory generation device generates a trajectory of a robot for conveying an object. A path condition acquisition unit acquires path condition information including at least coordinates of a first via point which is a position of a reference point of a suction nozzle of the robot when the suction nozzle comes into contact with the object, and a velocity, an acceleration, and a jerk of the suction nozzle at the first via point. A pressurization distance and coordinate calculation unit calculates coordinates of a second via point which is a position of the reference point when the suction nozzle is pushed into the object, based on the path condition information; and a trajectory generation unit generates the trajectory of the suction nozzle which satisfies the path condition information and reaches an end point from a predetermined start point via the first via point and the second via point.

Abstract: The present invention provides a method for monitoring a balanced state of a humanoid robot, comprising: acquiring state data of the robot falling in different directions and being stable, forming a support vector machine (SVM) training data set and obtaining, by training, an initial SVM classifier; inputting the state data of the robot to the trained SVM classifier, so that the SVM classifier outputs a classification result; taking statistics on a proportion of cycles judged to have an impending fall in the total number of control cycles within a judgment buffer time after the SVM classifier outputs the classification result, and finally determining a monitoring result of the balanced state of the robot according to the proportion and finally extracting state data of misjudged cycles within the buffer time, adding the state data to the current training data set and updating the SVM classifier, eventually enabling the classifier to achieve the effects of matching motion capabilities of the robot and monitorin

Abstract: Methods and systems according to one or more examples are provided for testing an automated platform, such as a robot. In one example, a system comprises a first robot configured to perform one or more processing operations on a workpiece. The system further comprises a second robot configured to simulate one or more parameters of the workpiece and an associated processing operation to provide one or more test conditions corresponding to each of the one or more processing operations the first robot would perform on the workpiece to test the first robot.

Abstract: The present disclosure provides a robot pose determination method including: collecting laser frames; calculating a current pose of the robot in a map pointed by a first pointer based on the laser frames, and obtaining an amount of the laser frames having been inserted into the map pointed by the first pointer; inserting the laser frames into a map pointed by the first pointer, if less than a first threshold; inserting the laser frames into the map pointed by the first pointer and a map pointed by a second pointer, if greater than or equal to the first threshold and less than a second threshold; and pointing the first pointer to the map pointed by the second pointer, pointing the second pointer to a newly created empty map, and inserting the laser frames into the map pointed by the first pointer, if equal to the second threshold.

Abstract: An embodiment of the present disclosure is a virtual home service apparatus including, a communicator, a home information collector for obtaining a design drawing of the home, and obtaining a 3D drawing by converting the design drawing, a home appliance identifier for obtaining an internal image and SLAM information of the home, and identifying the location and state of the home appliance based on the internal image and the SLAM information, and a virtual home implementator for generating virtual home information by reflecting the location and state of the home appliance to the 3D drawing. One or more among an autonomous driving vehicle, a user terminal, and a server according to an embodiment of the present disclosure may be associated with or converged with an Artificial Intelligence module, an Unmanned Aerial Vehicle (UAV), a robot, an Augmented Reality (AR) apparatus, a Virtual Reality (VR), 5G service-related apparatus, etc.