Abstract: In various examples, systems and methods are disclosed relating to decider networks for reactive decision-making, including for control of robotic systems. The decider networks can allow robotic systems to operate more collaboratively, such as by allowing the robotic systems to more frequently process and react to dynamic states of the environment and objects in the environment, such as to change decisions and/or paths of decision execution responsive to dynamic changes in logical states. The decider network can include a plurality of nodes having functions to process the logical states in sequence to determine actions for the robotic systems to perform.

Abstract: A vehicle control device includes a detection unit that detects a motion of a driver of a vehicle, a behavior of the vehicle, and biological information of the driver, an abnormal sign determination unit that determines whether there is an abnormal sign on the driver based on the motion of the driver, the behavior of the vehicle, and the biological information of the driver, and a vehicle controller that performs different types of vehicle control according to a situation of the abnormal sign on the driver.

Abstract: Architectures and techniques are provided for improved vibration compensation for a boom lift-robot-mounted (BLMR) system, which can result in improved BLMR operating precision and safety at a reduced cost. Boom lifts typically contain extendable large-scale, variable-stiffness structures, subject to complex nonlinear static deformation and dynamic motion/gust-induced vibrations. Said vibrations can be compensated for at an end effector of a robot via a Jacobian-based vibration compensation via IMU (JVCI) procedure. The output of the JVCI procedure can rely on IMU feedback and can be combined with other vibration-mitigation techniques, including various feedforward techniques.

Abstract: A battery electric vehicle includes: a traction motor; an inverter driving the motor; a first battery; an electric device running on electric power from the first battery; a detachable portable second battery; a first connecting and disconnecting device connecting and disconnecting a power line connected to the inverter and the first battery to and from each other; a second connecting and disconnecting device connecting and disconnecting the power line and the second battery to and from each other; and a control device controlling the first connecting and disconnecting device and the second connecting and disconnecting device. When a system startup instruction is given, the control device controls the first and second connecting and disconnecting devices so that the second battery is preferentially connected to the power line over the first battery when a temperature of the second battery is within an allowable temperature range of the second battery.

Abstract: A computer-implemented method of performing robotic simulation and design may include performing a simulation where a robot interacts with a user, and generating sensor data indicative of the robot interaction with the user during the simulation. The computer-implemented method may include generating discussion data indicative of user feedback from the simulation, generating form data associated with the user feedback from the simulation, and combining the sensor data, the discussion data, and the form data in a multimodal signal. The computer-implemented method may include determining a design characteristic of the robot based on the multimodal signal, and augmenting a robot model to incorporate the design characteristic using a machine learning algorithm.

Abstract: A robotic arm includes: a robotic arm main body; a carrying component connected to the robotic arm main body; a driving motor; and a controller connected to the driving motor. The controller is configured to generate a control signal that controls the driving motor to drive the carrying component to move. Along with a movement of the carrying component and under actions of a friction and the gravity, a target object placed on the carrying component slides on an upper surface of the carrying component and slides away from the carrying component at a target position at a target speed.

Abstract: The purpose of the present invention is to provide a robot control device capable of efficiently adjusting a parameter. This robot control device comprises: a first application unit that applies a setting parameter to a first safety parameter for verification in a verification mode; a first update unit that updates, on the basis of the first safety parameter, first safety information related to the robot in a verification function in the verification mode; a copy unit that copies the setting parameter; a second application unit that, after switching from the verification mode to the safety mode by a switch unit, applies the setting parameter copied by the copy unit to a second safety parameter; and a second update unit that updates, on the basis of the second safety parameter, second safety information related to the robot in a safety function in the safety mode.

Abstract: An embodiment for altering operational parameters of a machine in a multi-machine environment is provided. The embodiment may include receiving an IoT feed from one or more IoT devices and data relating to an activity in a multi-machine environment. The embodiment may also include identifying a health condition of one or more machines. The embodiment may further include in response to determining at least one machine of the one or more machines requires one or more maintenance actions, identifying a timeframe during which the one or more maintenance actions are able to be performed. The embodiment may also include updating a movement path of one or more service robots in the multi-machine environment. The embodiment may further include deploying the one or more service robots to execute the one or more maintenance actions. The embodiment may also include adapting one or more operational parameters of the one or more machines.

Abstract: A method and system for analyzing data describing touchups to a robot's tool center point position. A robot operator performs the touchups to a robot's tool center point position when the operator notices inaccuracies in the location of operations performed by the robot—such as a spot weld in a wrong location on a workpiece—where the inaccuracies may be caused by mechanical wear in robot joints. Each operator-defined touchup changes the position and optionally orientation of a particular point in the robot's motion program. The disclosed method provides a graphical display of the history of position touchups for the robot, and analyzes the touchup data against certain criteria for the distance, direction and frequency of the touchups. When the analysis determines that any individual or combination of criteria are met, an alert is provided which indicates a possible mechanical wear issue exists with the robot and needs attention.

Abstract: A vehicle steering method is provided for a vehicle having a plurality of steering wheels operated by a plurality of users. The method includes obtaining steering information from the plurality of steering wheels of the plurality of users; obtaining priority information of the steering wheels of the corresponding plurality of users; and in response to a user of a steering wheel with a highest priority operates to steer the vehicle, reading the steering information of the steering wheel with the highest priority.

Abstract: A method for controlling movement of a moving body includes: obtaining a first voxel set of an obstacle, where the first voxel set includes at least one first voxel that identifies the obstacle in space in which the moving body is located; determining a valid moving path based on a mapping relationship between a voxel and a moving path and the first voxel set; and controlling the moving body to move along the valid moving path.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for robot navigation. One of the methods includes obtaining one or more images of an area from a robot; detecting two or more lines within the one or more images; identifying at least two of the two or more lines as vanishing lines; determining, using the vanishing lines, a correction maneuver; and controlling the robot to implement the correction maneuver.

Abstract: Provided is a generalization generation method of industrial robot pose trajectories supporting changeable operation path points, which relates to the field of robot trajectory planning. The method includes: acquiring a plurality of groups of robot end pose trajectories; aligning time steps of the pose trajectories via a multi-dimensional dynamic time warping algorithm; constructing a Gaussian mixture model of robot pose trajectories in combination with a variational Bayesian method and an attitude quaternion tangent space mapping method; calculating reference pose trajectory distribution via a Gaussian mixture regression method; performing kernelized representation on pose trajectory distribution, and solving an optimal hyperparameter of a kernel function by minimizing a root mean square error of a reproduced reference trajectory; and generating the robot end pose trajectories adapted to the operation path points by updating the reference pose trajectory distribution.

Abstract: A robot controller includes at least one memory that stores a plurality of mechanism error parameters including a first mechanism error parameter and a second mechanism error parameter. At least one processor of the robot controller acquires an actual measurement position and information on a status of a robot by driving the robot with a plurality of orientations at a plurality of positions based on the second mechanism error parameter. The at least one processor calculates a third mechanism error parameter by correcting a value of the first mechanism error parameter based on the acquired actual measurement position and information on the status of the robot. The robot is controlled based on the plurality of mechanism error parameters including the second mechanism error parameter and the third mechanism error parameter.

Abstract: Embodiments of the present disclosure provide a method and system for positioning a robot and adjusting a posture. The method may include obtaining a first image and a second image of a target object. The first image may be captured using an image capturing apparatus, and the second image may be captured using a medical imaging device. The method may also include determining at least one target region corresponding to at least one target portion of the target object from the first image. The at least one target portion may be less affected by physiological motions than other portions. The method may further include determining positioning information of the robot based on the at least one target region and the second image.

Abstract: System and techniques to map a workspace for a robotic device are described herein. The robotic device has a movable portion with a sensor that is used to capture readings from part of the workspace. These readings are used to map a portion of the workspace and then to plot a new position of the movable portion to get readings for another part of the workspace. As the technique iterates, the robotic device is able to safely and efficiently map the entirety of the workspace.

Abstract: A system and method for teaching a robot program based on user input. In some embodiments, poses of a posing device manipulated by a user are captured and analyzed. In some embodiments, robot instructions are determined by a machine-learning algorithm that is trained on data concerning previously executed instructions evaluated by a user. In some embodiments, a user-selected process type defines workpiece geometry types for guiding the creation of robot instructions. In some embodiments, an augmented- or virtual-reality user interface is provided for obtaining user input.

Abstract: A robot arm control device is proposed. The robot arm control device may include a memory. The device may also include an acquisition unit acquiring an arbitrary target image, a virtual canvas image in which a virtual drawing operation of a robot arm for the target image is reflected, and a virtual nib image of the robot arm whose position is changed as the virtual drawing operation is reflected in the virtual canvas image. The device may further include a processor configured to input the target image to the pre-trained learning model, determine a position change amount of the virtual nib image in the virtual canvas image using the pre-trained learning model, and output a joint angle change amount for driving the robot arm on the basis of the position change amount.

Abstract: A management server according to an embodiment includes a communication part, an acquisition part, an intention estimation part, and a motion control part. The communication part communicates with one or more robot devices and one or more operation terminals that remotely operate at least one of the one or more robot devices via a network. The acquisition part acquires an operation content inputted by the operation terminal. The intention estimation part estimates an intention of an operator of the operation terminal based on the operation content acquired by the acquisition part. The motion control part controls a motion of the robot device based on an intention result estimated by the intention estimation part.

Abstract: A method for controlling a robot device. The method includes providing a selection model and executing multiple instances of a task, including, in each execution, when a function of the robot device needs to be selected to perform the task instance, checking whether the selection model provides a selection of a function and, if yes, controlling the robot device to perform the function selected by the selection model and if no, receiving user input indicating a selection of a function, selecting a function according to the selection indicated by the user input, controlling the robot device to perform the function selected according to the selection indicated by the user input and training the selection model according to the selection indicated by the user input.