A vehicle control device includes: a storage portion in which map information is stored, the map information showing a position where a roadside machine configured to transmit a radio signal including predetermined information is provided; a route setting portion configured to set a route where an autonomous driving vehicle is to travel when a current position, of the autonomous driving vehicle, that is measured by a positioning portion provided in the autonomous driving vehicle is included within a predetermined distance from the position of the roadside machine on the map information and the autonomous driving vehicle approaches the position where the roadside machine is provided, the route being set so that a communication portion provided in the autonomous driving vehicle can receive the radio signal; and a vehicle controlling portion configured to control the autonomous driving vehicle so that the autonomous driving vehicle travels along the route.

A control method performed by a controller includes calculating times required for a worker to load, on an automated guided vehicle, multiple articles placed in multiple locations regarding multiple cases among which a stop situation of the automated guided vehicle where the worker loads the articles varies and determining a position in which the automated guided vehicle is to be stopped, based on the calculated times.

This work vehicle has: a positioning unit for measuring the current position and the current direction of the vehicle body using a satellite positioning system; and an automatic travel control unit for executing automatic travel control based on positioning information from the positioning unit. The positioning unit comprises: a plurality of positioning antennas provided on the vehicle body; a plurality of positioning units for measuring the positions of the positioning antennas; a calculation unit for calculating the current position and the current direction of the vehicle body on the basis of positioning information from the positioning units; and a positioning state determination unit for determining whether or not the positioning state of the positioning units is a high-accuracy positioning state. When at least two positioning units are in the high accuracy positioning state, the positioning state determination unit permits the start of the automatic travel control.

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 system and a method are disclosed for determining a heading of a vehicle and a heading of an implement of a farming machine when the farming machine is stationary or moving at a speed below a threshold speed. The vehicle and the heading are attached together via a pivot hitch. A farming machine management system receives coordinates from a first location sensor coupled to the vehicle and a second location sensor coupled to the implement. The farming machine management system determines intersection points between a first circle centered at the first location sensor and a second circle centered at the second location sensor. The farming machine management system selects one of the intersection points based on an output of a machine learning model. The farming machine management system determines the headings of the vehicle and the implement and generates instructions for operating the farming machine based on the headings.

A method for specifying a cleaning area to a cleaning robot without an in-built map provides a hand-held mobile device capturing a two-dimensional code label arranged on a top of a cleaning robot parked on a charging base, and obtaining a positional relationship between the mobile device and the cleaning robot through the captured image. The cleaning robot is controlled to enter a cleaning mode under the guidance of the mobile device. With captured images, a user can specify an area within the environment for cleaning, and through a touch display screen can control the cleaning robot to go to the specified cleaning area for cleaning. The mobile device and the cleaning robot employing the method are also disclosed.

A driving robot device is provided. The driving robot device may include a plurality of suspensions configured to absorb a shock applied by a driving surface on which the driving robot device drives; a first driving part that includes a motor and is configured to adjust a strength of the plurality of suspensions; and at least one processor configured to control the first driving part to adjust the strength of the plurality of suspensions based on driving surface information with respect to a state of driving surface, and based on food information with respect to a state of food carried by the driving robot device.

A robot repositioning method is provided. A position deviation caused by excessive accumulation of a walking error of a robot may be corrected to implement repositioning by taking a path that the robot walks along an edge of an isolated object as a reference, so that the positioning accuracy and walking efficiency of the robot during subsequent navigation and walking are improved.

A sensor data processing system for an autonomous vehicle receives inertial measurement unit (IMU) data from one or more IMUs of the autonomous vehicle. Based at least in part on the IMU data, the system identifies an IMU data offset from a deficient IMU of the one or more IMUs, and generates an offset compensation transform to compensate for the IMU data offset from the deficient IMU. The system dynamically executes the offset compensation transform on the IMU data from the deficient IMU to dynamically compensate for the IMU data offset.

In an autonomous vehicle system, data received from one or more autonomous vehicles (AVs) can be aggregated to generate aggregated data. From this aggregated data, a vector-field map can be generated that includes a plurality of cells. Each of the cells can include a corresponding vector. The vector field map can be analyzed to identify one or more vectors of the plurality of cells that exceed one or more predetermined threshold values. The analysis can include a magnitude analysis and/or a frequency analysis. Based on the analysis, traffic and/or road conditions can be determined, which can provide prior knowledge about the driving behavior of other vehicles. Advantageously, aspects of the disclosure improve predictive motion models and enhance navigation algorithms.