Abstract: With an object of notifying a peripheral vehicle or a person of a travel plan of an own vehicle at an appropriate timing, and providing a safe automatic driving system, a travel plan information distribution system is characterized by including a travel plan information compilation unit that, based on information output by a positional information and frontal road shape information acquisition unit and a sensor information acquisition unit, compiles travel plan information including a travel plan category and a time at which travel is to be started or a position from which a travel plan is to be started, a wireless communication device that transmits travel plan information to a peripheral vehicle, and a travel plan information distribution control unit that selects and outputs necessary details of travel plan information at a safe and necessary timing.

Abstract: In a vehicle control apparatus, an entry determiner determines, while one of the following traveling mode and the constant-speed traveling mode is performed by the vehicle control apparatus, whether an entry condition is satisfied in response to determination that there is at least one other crossing vehicle. The entry condition enables the at least one other crossing vehicle to cross in front of the own vehicle to thereafter enter the enterable space without colliding with the own vehicle. An autonomous driving controller switches, upon the entry condition being determined to be satisfied, the performed one of the following traveling mode and the constant-speed traveling mode to a crossing mode that controls traveling of the own vehicle to ensure a predetermined space in front of the own vehicle. The predetermined space enables the at least one other crossing vehicle to cross in front of the own vehicle.

Abstract: A method and a device for operating a vehicle assistance system in that the environment of the vehicle and objects located in the vehicle are determined using detected signals from an environment sensor system. Using the method and device it may be determined whether the roadway lying ahead of the vehicle driving in an automated driving mode is snow-covered and has tire tracks. In the event the tire tracks are there, and depending on the determined course of the tire tracks for the vehicle, a decision is made as to whether a journey along these tire tracks is critical.

Abstract: Autonomous vehicle trailer loading with smart trailer are disclosed herein. An example method includes receiving a request to activate a self-loading procedure for an autonomous vehicle, executing the self-loading procedure by an autonomous vehicle controller, the self-loading procedure including causing the autonomous vehicle to navigate to a transportation platform, determining a visual identifier of the transportation platform using output from a sensor platform of the autonomous vehicle, and causing the autonomous vehicle to navigate onto or into the transportation platform and park at a parking spot of the transportation platform designated for the autonomous vehicle.

Abstract: To obtain a global optimal navigation track, a contour line matching method based on sliding window data backtracking includes the steps of determining sliding window parameters according to the calculation performance of a real-time multi-task operating system and aided navigation precision requirements, constructing a sliding window data backtracking framework by using historical physical field value matching data, obtaining a rotation transformation matrix from an indication track point set to a closest reference point set by adopting a matrix eigenvalue and eigenvector decomposition method, and moving a sliding window and performing forward and reverse cyclic matching to achieve a global track constraint, thereby improving the matching precision and robustness.

Abstract: A control method includes an input step for inputting information concerning a setting angle for a robot arm of a robot, the robot including the robot arm including an arm and a driving section including a servomotor that drives the arm, a calculating step for calculating, based on a first servo parameter corresponding to setting at a first setting angle for the robot arm and a second servo parameter corresponding to setting at a second setting angle different from the first setting angle for the robot arm, a third servo parameter corresponding to the setting angle for the robot arm.

Abstract: Systems, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.

Abstract: Systems and methods for virtual vehicle parking assistance are disclosed herein. An example method includes determining a current vehicle position and vehicle dimensions of a vehicle, determining parking space dimensions of a parking space, receiving a desired parking position for the vehicle through an augmented reality interface, the augmented reality interface including a three-dimensional vehicle model based on the vehicle dimensions, the augmented reality interface being configured to allow a user to virtually place the three-dimensional vehicle model in the parking space to determine the desired parking position, determining a virtual parking procedure for the vehicle based on the desired parking position selected by the user and the parking space dimensions of a parking space and causing the vehicle to autonomously park based on the virtual parking procedure.

Abstract: Systems and methods for utilizing interactive Gaussian processes for crowd navigation are provided. In one embodiment, a system for a crowd navigation of a host is provided. The system includes a processor, a statistical module, and a model module. The processor receives sensor data. The statistical module identifies a number of agents in a physical environment based on the sensor data. The statistical module further calculates a set of Gaussian processes. The set of Gaussian processes includes a Gaussian Process for each agent of the number of agents. The statistical module further determines an objective function based on an intent and a flexibility. The model module generates a model of the number of agents by applying the objective function to the set of Gaussian processes. The model includes a convex configuration of the number of agents in the physical environment.

Abstract: A driving assist system executes risk avoidance control for reducing a risk of collision with a target existing ahead of a vehicle. A vehicle state parameter includes imaginary relative position and velocity of the vehicle. A target state parameter includes expected direction and speed of movement the target. A risk value is expressed by a function of an estimated collision speed between the vehicle defined by the vehicle state parameter and the target defined by the target state parameter. The driving assist system sets multiple patterns of the target state parameter and sets a probability of each target state parameter. A partial risk value is the risk value when each target state parameter is used. A final risk value applied to the risk avoidance control is a sum of products of the probability and the partial risk value with respect to the multiple patterns of target state parameter.

Abstract: A horizontal articulated robot includes a base, a force detection unit provided in the base, a first arm coupled to the base and pivoting about a first pivot axis, a second arm coupled to the first arm and pivoting about a second pivot axis, a third arm coupled to the second arm, pivoting about a third pivot axis, and moving in an axial direction of the third pivot axis, a control unit that controls an action of the first arm, the second arm, or the third arm based on a detection value of the force detection unit, and an operation unit having a third arm operation part for operation of the third arm and a teaching point registration operation part for operation of registration of a position of a control point as a teaching point using the control unit, and provided in the second arm.

Abstract: A system includes a robotic manipulator including a serial chain comprising a first joint, a first link, and a second link. The second link is between the first joint and the first link in the serial chain. The system further includes a processing unit including one or more processors. The processing unit is configured to receive first link data from a first sensor system located at the first link, generate a first joint state estimate of the first joint based on the first link data and a kinematic model of the robotic manipulator, and control the first joint based on the first joint state estimate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

Abstract: A friction compensation device of the present disclosure includes a drive torque calculation unit that calculates output torque of a transmission mechanism from a motor's position, velocity, and acceleration, the transmission mechanism being connected to a motor via a shaft to transmit the driving force of the motor, and a friction estimate value calculation unit that calculates a friction estimate value that is an estimate value of a friction force on the shaft. The friction estimate value calculation unit includes a friction correction value calculation unit that calculates a friction correction value to correct the friction force on the shaft, in accordance with the output of the drive torque calculation unit.

Abstract: An autonomous driving control method carried out by an autonomous driving control system having an autonomous driving control unit that executes an autonomous driving control for causing a host vehicle to travel along a target travel route generated on a map, comprising setting one or a plurality of target passage gates through which the host vehicle is scheduled to pass during passage through a toll plaza, determining the presence or absence of a preceding vehicle that has the predicted passage gate that matches the target passage gate of the host vehicle from among a plurality of preceding vehicles, and carrying out following travel using the preceding vehicle that has the predicted passage gate that matches the target passage gate as a follow target.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: In a driving assistance control apparatus for a vehicle, an acquisition unit is configured to acquire a detected traveling state of the vehicle and a detected traveling environment of the vehicle. A control unit is configured to, when a curvature radius of a travel trajectory of the vehicle is equal to or less than a predetermined radius threshold, cause a driving assistance unit to perform collision avoidance assistance using, as an activation area of the collision avoidance assistance, a reduced activation area obtained by reducing a reference activation area, and when determining that the vehicle is making a constant turn, cause the driving assistance unit to perform the collision avoidance assistance by using the traveling state of the vehicle and the traveling environment of the vehicle and the reference activation area even if the curvature radius of the travel trajectory is equal to or less than the radius threshold.

Abstract: A method and apparatus for controlling a vehicle is disclosed. The method may include: determining center points of at least two frames of point clouds collected for an identified obstacle during travelling of the vehicle; performing curve fitting based on the determined center points to obtain a fitted curve; determining a moving velocity of the obstacle based on the fitted curve; predicting whether the vehicle is to be collided with the obstacle when the vehicle continues travelling at a current velocity, based on the moving velocity of the obstacle, the traveling velocity of the vehicle, and a distance between the obstacle and the vehicle; and sending control information to the vehicle, in response to predicting that the vehicle is to be collided with the obstacle when the vehicle continues travelling at the current velocity, the control information being used to control the vehicle to avoid collision with the obstacle.

Abstract: A substrate transport device includes an arm, an end effector coupled to the arm, a driver configured to lift the arm so that the end effector receives a substrate, and a controller configured to control an output of the driver to change a lifting speed of the arm. While lifting the arm at a first speed to lift the end effector toward the substrate, the controller changes the lifting speed to a second speed that is lower than the first speed when the end effector starts to raise a height position of the substrate.

Abstract: A mobile robot includes a body, a propulsion module, an ultrasound sensor module that is configured to detect a boundary of a cleaning area using a sound wave, and a controller configured to control the propulsion module based on the determined boundary. The ultrasound sensor module may include an ultrasonic sensor unit and a boundary detector. The sensor unit may emit the sound wave, receive the reflected sound wave from a target, and output a sound wave signal. And, the boundary detector may analyze the sound wave signal to detect the boundary of the cleaning area.