An automated guided vehicle system in which an automated guided vehicle autonomously moves inside a building is provided. The automated guided vehicle includes: an internal sensor having an encoder and a gyro sensor and acquiring information about an own position; an external sensor casting light onto a reflection board arranged in the building, receiving reflected light from the reflection board, and acquiring information about an own position; and a control unit correcting the own position based on the internal sensor, based on the information acquired by the external sensor. The own position via the internal sensor has a margin of error such that the automated guided vehicle can travel for a predetermined period, using only the internal sensor. The control unit performs own-position correction via the external sensor, using one of the reflection boards.

A division line recognition apparatus including a detection part configured to detect an external situation around a subject vehicle, and an electronic control unit including a microprocessor and a memory connected to the microprocessor. The microprocessor is configured to perform generating a map including a division line information on a division line on a road based on the external situation detected by the detection part, setting an area of an external space detectable by the detection part, determining whether an end of the division line on the map is located at a boundary of the area of the external space, and adding a boundary information to the division line information when it is determined that the end of the division line is located at the boundary.

A system for an autonomous vehicle by providing lane markers on the road for which a vehicle will read and navigate the road. The vehicle transmits a discovery signal and is returned from the marker to indicate the position on the road and how to proceed on the road. The system uses either an autonomous control system or 3D map navigation database to determine the direction of the vehicle in real time.

An indoor mobile industrial robot system is configured to provide a weight to a detected object within an operating environment, where the weight relates to how static the feature is. The indoor mobile industrial robot system includes a mechanism configured to translate reflected light energy and positional information into a set of data points representing the detected object having at least one of Cartesian and/or polar coordinates, and an intensity. If any discrete data point within the set of data points representing the detected object has an intensity at or above a defined threshold the entire set of data points is converted into a weight and potentially classified representing a static feature, otherwise such set of data points is classified as representing a dynamic feature having a lower weight.

A method can include sending, via a processing device, a signal to at least two of a plurality of location indicators from an autonomous vehicle in motion and transporting equipment or passengers. The method can further include receiving signals from the at least two location indicators. The method can further include determining a location of the autonomous vehicle within an indoor facility based on the received signals. The method can further include comparing the determined location to a corresponding pre-determined location. The method can further include, in response to the determined location being different than the pre-determined location, adjusting a direction of the autonomous vehicle along a predetermined path within the indoor facility.

Provided is a mobile robot including a driving unit for moving a main body; an image acquisition unit for obtaining an image of a periphery; and a controller for analyzing the image obtained by the image acquisition unit and determining whether a reflector exists in the vicinity of the main body, wherein the controller determines whether a reflector exists in the vicinity of the main body based on similarity between the image obtained by the image acquisition unit and the pre-stored image of the main body.

Autonomous or automated mobile robots can be configured for transport and logistics applications. Systems for docking of such robots with wheeled carts can be used in methods for operation of such robots.

The present disclosure relates to an autonomous forklift truck capable of recognizing a location of the autonomous forklift truck and a location of an obstacle in a work area, and the truck includes a location recognition sensor to detect the location of the autonomous forklift truck through a laser emitted and reflected from a reflective marking equipped in a structure, a first sensor to detect an obstacle near a work area floor, a second sensor to detect an obstacle at a predetermined height from the floor, a fork laser sensor to measure a distance from a rack where a pallet is loaded or a distance from the pallet, a first fork photoelectric sensor and a second fork photoelectric sensor, and a control unit to process sensing signals inputted from all the sensors and control the driving and attachments of the autonomous forklift truck.

A method for generating a new hybrid map by at least one of extending and modifying a first hybrid map with a second hybrid map, the hybrid map being used for the navigation of a vehicle (10) in a navigation area and including a plurality of information categories, one information category comprising a trajectory of the vehicle (10) to be driven, which trajectory is predefined by a trail (12), and one information category comprising a surrounding contour (24) of the trail (12), wherein a transfer decision is made for each of the information categories as to whether information from the first hybrid map, the second hybrid map, or both the first hybrid map and the second hybrid map is transferred to the new hybrid map.

A transport system including a load table with an index and a traveling body configured to transport an object placed on the load table. The traveling body includes a memory, an environment detector, and circuitry. The memory is configured to store map information including a position of the load table and self-position information indicating a position of the traveling body. The environment detector is configured to detect the index and acquire data indicating a relative position between the index and the traveling body. The circuitry is configured to update the self-position information based on the relative position and the map information.