Aspects of the disclosure relate generally to generating and providing route options for an autonomous vehicle. For example, a user may identify a destination, and in response the vehicle's computer may provide routing options to the user. The routing options may be based on typical navigating considerations such as the total travel time, travel distance, fuel economy, etc. Each routing option may include not only an estimated total time, but also information regarding whether and which portions of the route may be maneuvered under the control of the vehicle alone (fully autonomous), a combination of the vehicle and the driver (semiautonomous), or the driver alone. The time of the longest stretch of driving associated with the autonomous mode as well as map information indicating portions of the routes associated with the type of maneuvering control may also be provided.

A modular control system for controlling an AGV includes an interface, a processor, a memory, and a plurality of programs. The plurality of programs include a task scheduling module, a sensor fusion module, a mapping module, and a localization module. The interface receives a command signal from an AGV management system and sensor signals from a plurality of sensors. The memory stores a surrounding map and the plurality of programs to be executed by the processor. The task scheduling module converts the command signal to generate an enabling signal. The sensor fusion module processes the received sensor signals according to the enabling signal and generates an organized sensor data. The mapping module processes the organized sensor data and the surrounding map to generate an updated surrounding map. The localization module processes the organized sensor data and the updated surrounding map to generate a location and pose signal.

The present disclosure relates to the field of robots, and particularly relates to a robot control method, a robot control system and a modular robot. The robot control method includes the steps of: T1: providing a robot, with at least one wheel and at least one motion posture; T2: regulating the robot to a motion posture, saving motion-posture information corresponding to the motion posture, and generating preset action control information based on the speed of the wheel and the motion-posture information; T3: constructing and forming an operating model based on the preset action control information; and T4: outputting, by the operating model, actual motion control information of a motion according to user's input to control the robot to perform the motion. Thus, it is convenient to set motion modes to meet the diverse needs of users, and the design space of the robot suitable for more scenarios is increased.

Aspects of the disclosure provide a method of providing a destination to an autonomous vehicle in order to enable the autonomous vehicle to collect data according to a targeted driving goal. For instance, a current location of an autonomous vehicle may be received. A set of destinations may be selected from a plurality of predetermined destinations. A route may be determined for each destination. A relevance score may be determined for each destination based on the determined routes and the targeted driving goal. Each destination may be assigned to one of a set of two or more buckets based on the relevance scores. A destination of the set may be selected based on a predetermined sampling probability. The selected destination is sent to the autonomous vehicle in order to cause the autonomous vehicle to travel to the selected destination in an autonomous driving mode.

The method and system disclosed herein presents a method and system for capturing, by a camera disposed on a device moving in an environment, a plurality of image frames recorded in a first coordinate reference frame at respective locations within a portion of the environment in a first time period; capturing, by an inertial measurement unit disposed on the device, sets of inertial odometry data recorded in a second coordinate reference frame; determining a rotational transformation matrix that corresponds to a relative rotation between the first reference frame and the second reference frame; and determining a scale factor from the matching pairs of image frames. The rotational transformation matrix defines an orientation of the device, and the scale factor and the rotational transformation matrix calibrate the plurality of image frames captured by the camera.

A robot management system executes, for a plurality of transport robots, an estimation process for estimating load applied to the transport robots based on a current value during traveling and a traveling distance or a traveling time of the transport robots. The robot management system determines a transport robot to be used from among the transport robots based on an estimation result in the estimation process.

Provided are an autonomous running device, a running control method for the autonomous running device, and a running control program of the autonomous running device that allow the autonomous running device to reach a destination while continuing estimation of its self-position. An autonomous running device includes a first position estimation unit that estimates the position of the autonomous running device on the basis of information about surroundings of the autonomous running device, produces information about the estimated position of the autonomous running device as first positional information, and updates the first positional information, a second position estimation unit that estimates the position of the autonomous running device on the basis of rotation amounts of wheels, produces information about the estimated position of the autonomous running device as second positional information, and updates the second positional information, and a control unit.

A materials handling vehicle includes a camera, odometry module, processor, and drive mechanism. The camera captures images of an identifier for a racking system aisle and a rack leg portion in the aisle. The processor uses the identifier to generate information indicative of an initial rack leg position and rack leg spacing in the aisle, generate an initial vehicle position using the initial rack leg position, generate a vehicle odometry-based position using odometry data and the initial vehicle position, detect a subsequent rack leg using a captured image, correlate the detected subsequent rack leg with an expected vehicle position using rack leg spacing, generate an odometry error signal based on a difference between the positions, and update the vehicle odometry-based position using the odometry error signal and/or generated mast sway compensation to use for end of aisle protection and/or in/out of aisle localization.

The present specification relates to a mobile robot system and a method for generating boundary information of the mobile robot system, wherein the mobile robot system generates first map data for the locations of a plurality of transmitters installed in a driving area on the basis of the result of receiving the transmission signals from the plurality of transmitters, receives second map data for an area corresponding to the driving area from a communication target means in which map information of an area including the driving area is stored, and matches the first map data and the second map data to generate boundary information about a boundary area of the driving area.

The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.