An adaptive filter system and a method for controlling the adaptive filter system are described herein. The system can includes one or more filters to attenuate incoming light. The one or more filters can be moved by one or more actuators. The method can capture image data from an imaging device through the one or more filters. Information can be determined from the captured image data. The one or more filters can be moved to a position for capturing image data based on the information.

A robot that is able to move about an environment determines a wait location in the environment to wait at when not otherwise in use. The wait location may be selected based on various factors including position of objects, next scheduled use, previous usage of the robot, availability of wireless connectivity, user traffic patterns, user presence, visibility of the surrounding environment, and so forth. The robot moves to that location and maintains a pose at that location, such as orienting itself to allow onboard sensors a greatest possible view of the environment. If a wait location is occupied, the robot may move to another wait location.

An own-position estimating device for estimating an own-position of a moving body by matching a feature extracted from an acquired image with a database in which position information and the feature are associated with each other in advance, includes an evaluation result acquiring unit acquiring an evaluation result obtained by evaluating matching eligibility of the feature in the database, and a processing unit processing the database on the basis of the evaluation result acquired by the evaluation result acquiring unit.

The present disclosure provides a mobile robot. The mobile robot includes a body, a pair of spin mops rotatably mounted to the body, a mop motor configured to provide a driving force to the pair of spin mops, an optical flow sensor configured to obtain bottom-view image information using light at a regular time interval, and a controller configured to determine whether the material of the floor is a troublesome material based on the bottom-view image information sensed by the optical flow sensor and to control, upon determining that the material of the floor is a troublesome material, the mop motor to perform an entry restriction operation.

A controlling method for an artificial intelligence moving robot according to an aspect of the present disclosure includes: checking nodes within a predetermined reference distance from a node corresponding to a current position; determining whether there is a correlation between the nodes within the reference distance and the node corresponding to the current position; determining whether the nodes within the reference distance are nodes of a previously learned map when there is no correlation; and registering the node corresponding to the current position on the map when the nodes within the reference distance are determined as nodes of the previously learned map, thereby being able to generate a map in which the environment of a traveling section and environmental changes are appropriately reflected.

A method, a system, and non-transitory computer readable medium for controlling an autonomous vehicle are provided. The method includes identifying a trend for an autonomous vehicle based on autonomous vehicle profiles associated with one or more vehicles within a network, identifying optimal driving conditions for the autonomous vehicle based on the trend; and controlling one or more subsystems of the autonomous vehicle based on the identified optimal driving conditions.

Systems and methods enable an autonomous vehicle to perform an iterative task of transferring material from a source location to a destination location, such as moving dirt from a pile, in a more efficient manner, using a combination of reinforcement learning techniques to select a motion path for a particular iteration and visual servo control to guide the motion of the vehicle along the selected path. Lifting, carrying, and depositing of material by the autonomous vehicle can also be managed using similar techniques.

A vehicle control system including: a vehicle configured to be movable along a predetermined route, the vehicle including at least an image capturing apparatus; a display apparatus configured to display a video image captured by the image capturing apparatus, the display apparatus being installed in a remote place that is remote from the vehicle; a first operation terminal installed in the remote place; a control apparatus configured to control movement of the vehicle based on first operation information input through the first operation terminal; and a second operation terminal installed in a place that is closer to the vehicle than to the remote place, in which the control apparatus is configured to assist, based on second operation information input through the second operation terminal, the control of the movement of the vehicle that is based on the first operation information.

Provided are methods and apparatus for environment-adaptive robotic disinfecting. In an example, provided is a method that can include (i) creating, from digital images, a map of a structure; (ii) identifying a location of a robot in the structure; (iii) segmenting, using a machine learning-based classifying algorithm trained based on object affordance information, the digital images to identify potentially contaminated surfaces within the structure; (iv) creating a map of potentially contaminated surfaces within the structure; (v) calculating a trajectory of movement of the robot to move the robot to a location of a potentially contaminated surface in the potentially contaminated surfaces; and (vi) moving the robot along the trajectory of movement to position a directional decontaminant source adjacent to the potentially contaminated surface. Other methods, systems, and computer-readable media are also disclosed.

A system is provided that includes a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive an image depicting a parking spot, determine a length of the parking spot based on a classified endpoint of the parking spot, compare the length to an average length, and determine an endpoint of the parking spot when the length is less than the average length, wherein the determined endpoint is distal to the classified endpoint.