Utility vehicles with battery management and autonomous control systems are disclosed. A utility vehicle includes driven wheels, electric motor(s), blade motor(s), at least one battery, battery management system(s), global navigation satellite system receiver(s), and controller(s) communicatively connected to memory. The controller(s) identify whether map data for a mow area is stored in the memory and perform a sparse-mow routine in response to identifying no map data in the memory. To perform the sparse-mow routine, the controller(s) autonomously steer the electric utility vehicle to travel over a sample of each portion of the mow area, collect location data, and collect current discharge data. The controller(s) generate an energy-consumption map for the mow area by correlating the current discharge data with the location data and determine an efficient-mow path for subsequent mowing events of the mow area based on the energy-consumption map.

Robot localization or mapping can be provided without requiring the expense or complexity an at-a-distance sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and dock-based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and/or maintenance. A method can include generating, for a real environment including a real robot having one or more real sensors, a simulated environment modeling the real environment, the simulated environment including a simulated robot corresponding to the real robot, the simulated robot including one or more simulated sensors corresponding to the one or more real sensors, obtaining simulated data based at least on simulated sensor data collected using the one or more simulated sensors, and using the simulated data to control operation of the real robot within the real environment.

An example method is performed by one or more processing devices and includes the following: identifying one or more features based on data obtained from a two-dimensional scan of a space, where the data includes predefined characteristics; identifying physical attributes of the one or more features; performing calculations based on the physical attributes for the one or more features, where the calculations produce one or more possible configurations for one or more candidate transport structures in the space; comparing the one or more possible configurations to one or more predefined configurations for one or more known transport structures; identifying which, if any, of the one or more candidate transport structures is most likely to be a known transport structure based on the comparing; and controlling an autonomous vehicle based on the identifying.

A localization system for a forklift performing inventory management is disclosed. The system may access map data comprising a dimensional layout of a warehouse environment. The method includes receiving from one or more forklift sensors associated with a forklift, forklift operation data, the forklift operation data indicative of one or more forklift operation behaviors performed during an operation period. The method includes receiving from one or more scanning sensors, inventory data, the inventory data indicative of one or more inventory items scanned during the operation period. The method includes, based at least in part on the map data, the forklift operation data, and the inventory data, concurrently generating localization data to localize the forklift in the warehouse environment during the operation period.

A ship docking assistance device includes a position azimuth information acquisition unit; a LIDAR; a map generation updating unit; a high-point acquisition unit; and a position azimuth estimation unit. The LIDAR acquires point-group data three-dimensionally indicating the environment around a ship. The map generation updating unit generates a map around the ship based on the point-group data. The high-point acquisition unit acquires, from within the point-group data, a high point having a prescribed height or more. The position azimuth estimation unit estimates the position and the azimuth of the ship through matching between the position of the high point acquired by the high-point acquisition unit and the position of the high point in the map. The map generation updating unit updates the map by placing the point-group data in the map using, as references, the position and the azimuth of the ship estimated by the position azimuth estimation unit.

Disclosed are an electronic device and a method for controlling thereof. A method of controlling an electronic device includes identifying a first traveling path heading to a preset destination based on a map corresponding to an environment in which an electronic device operates; identifying an object interfering with traveling according to the first traveling path based on at least one sensor while traveling according to the first traveling path; identifying an avoidance path to avoid the object based on at least one of a location and speed of the identified object and traveling according to the avoidance path; and based on the identified object being distant by a preset distance or more based on traveling according to the avoidance path, controlling the electronic device to travel according to the first traveling path based on a current location of the electronic device.

Systems and methods for detecting, mapping, and route planning around cliffs for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot processing a three-dimensional range sensor scan may utilize pre-computed neighboring points to detect cliffs, navigable ramps, and holes in a plane of a map used by the robotic device to navigate.

A global path generation method for a wide-area off-road environment in which an unmanned vehicle performs autonomous driving includes generating an occupancy grid map for a driving area through a sensor, converting the occupancy grid map into a distance map, generating a plurality of nodes by sampling unit grids that are are randomly and uniformly distributed in the driving area of the distance map, generating a plurality of links connecting the plurality of nodes, receiving a destination position of the unmanned vehicle, and generating a global path by connecting optimal links for arriving at the destination position among the plurality of links.

A method and an apparatus for determining a position of a shelf are provided. The method may include: obtaining a number of automated guided vehicles with shelf scanning devices; determining, based on the number of the automated guided vehicles, a scanning area of a place to which each automated guided vehicle belongs; determining, based on the scanning area, a scanning route of the scanning area to which each automated guided vehicle belongs; transmitting the scanning route of the scanning area to which the automated guided vehicle belongs, to the automated guided vehicle; and determining a position of a shelf in the scanning area to which the automated guided vehicle belongs based on scanning information of a shelf scanning device on the automated guided vehicle and position information of the automated guided vehicle.