Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to represent a vehicle as a vehicle boundary; represent an object as an object polygon including object vertices and object sides; determine a turning center of a turning radius of the vehicle; select the object vertices projected to intersect the vehicle boundary; for the selected object vertices, determine respective object central angles with respect to the turning center between the respective object vertex and an object intersection with the vehicle boundary; select a smallest central angle from a set including the object central angles; and actuate a component of the vehicle based on the smallest central angle.

Abstract: A control method for a robot performing a task on a workpiece conveyed by a belt conveyor having a moving belt is provided. The control method includes: causing a first image pickup device to detect an inclination between a first direction serving as a reference for traveling of the belt and a direction in which the belt is traveling, and a first distance that is a distance between the belt and a first reference position in a second direction orthogonal to the first direction at the first reference position; calculating a position of the workpiece as of when the workpiece is moved by the belt conveyor from a first position to a second position away from the first position by a second distance in the first direction, based on the inclination and the first distance detected by the first image pickup unit; and causing the robot to perform a task on the workpiece at the second position that is calculated.

Abstract: A starting point, a set of one or more way points, and a destination point of a route along which the ADV is to be driven are determined. All lane segments near the starting point, the set of way points, and the destination point within a predetermined threshold distance are determined respectively. A set of route candidates are determined with an A-star (A*) searching algorithm based on a set of nodes representing all the lane segments near the starting point, the set of way points, and the destination point respectively. The route is selected from the set of route candidates based on respective costs of the set of route candidates. The ADV is being controlled to drive along the selected route autonomously.

Abstract: An action of a robot included in a robot system is controlled based on a learning result of a machine learning device. The machine learning device includes a behavior observing unit, a displaced amount observing unit, and a learning unit. The behavior observing unit observes a behavior pattern of the robot picking out a workpiece from a container. The displaced amount observing unit observes, based on pieces of image data captured before and after the pick-out action of the robot and output from an image capturing device, a workpiece displaced amount indicating a displaced amount of an untargeted workpiece in the container caused by picking out a targeted workpiece from the container. The learning unit learns an influence rate on the untargeted workpiece with an associated behavior pattern of the robot for picking out the targeted workpiece from the container, the influence rate depending on the workpiece displaced amount.

Abstract: A system that uses 3D scanning and a process agnostic pointing device that is used in conjunction with user input to create a robot program.

Abstract: A robot motion planning technique for component assembly operations. Inputs to the motion planning technique include geometric models of the components being assembled, and initial and target configurations. The method begins at a tightly-constrained target or final configuration and plans in the direction of a loosely-constrained initial configuration. A randomly-sampled waypoint configuration is proposed, followed by a local search for feasible configurations which generates nodes that extend a path toward the initial configuration while sliding through the tightly-constrained region. The local search can be repeated multiple times for a given randomly-sampled configuration. When a completed path is found, the action sequence is trimmed to eliminate unnecessary extraneous motions in the loosely-constrained region. The disclosed method dramatically reduces the number of unproductive configurations evaluated and finds assembly solutions much faster in comparison to known tree-based motion planning methods.

Abstract: A computer-implemented method includes receiving data regarding an area to be cleaned, the data comprising data regarding movement of people or machines through the area to be cleaned; training a machine learning cleaning model based on the data; creating a floor cleaning plan using the machine learning cleaning model, the floor cleaning plan identifying a cleaning time to minimize a likelihood of a robotic cleaning device encountering a person or a machine while cleaning; and transmitting the floor cleaning plan to a robotic cleaning device.

Abstract: A system for unmanned driving of a vehicle includes an autonomous driving kit with a detachable terminal having a modularized autonomous driving sensor and configured to support the autonomous driving function of the vehicle. The autonomous driving kit is configured to grasp a vehicle system including various controllers and various sensors applied to the vehicle through a vehicle communication unit of the vehicle and to control autonomous driving adapted to multiple vehicle types and options for each vehicle through cooperative control of the vehicle system and the autonomous driving sensor. A control server is configured to recognize the vehicle to which the autonomous driving kit is attached, provide a moving route of the vehicle, collect a moving state and surrounding conditions of the vehicle based on image information of cameras and transmit the information to the autonomous driving kit.

Abstract: A control system controls an operation mode of a mobile robot that autonomously moves in a predetermined area, and includes a feature detection unit, a classifying unit, and a system controller. The feature detection unit detects features of a person who is present in the vicinity of the mobile robot. The classifying unit classifies the person into a predetermined first group or a predetermined second group based on the features. The system controller selects a first operation mode when the person who belongs to the first group is present in the vicinity of the mobile robot and selects a second operation mode that is different from the first operation mode when the person who belongs to the first group is not present in the vicinity of the mobile robot, thereby controlling the mobile robot.