A cleaning machine and a path planning method of the cleaning machine are provided. According to one embodiment of the invention, a cleaning machine for cleaning a surface is provided. The cleaning machine includes a sensing module and a control system. The sensing module senses an environment of the cleaning machine to obtain map data. The control system divides the map data into multiple blocks, and controls the cleaning machine to perform a first cleaning process and a second cleaning process in a current block of the blocks, and then controls the cleaning machine to move to a next block of the blocks.

A method of inspecting a building construction site using a mobile robotic system includes a mobile platform and a sensor system mounted on the mobile platform and configured to generate one or more types of sensor data. The method includes: receiving object identification information identifying at least one building object to be inspected by the mobile robotic system in the building construction site; obtaining a robot navigation map covering the at least one building object based on a building information model for the building construction site; and determining at least one goal point in the robot navigation map for the at least one building object, each goal point being a position in the robot navigation map for the mobile robotic system to navigate autonomously to for inspecting corresponding one or more building objects of the at least one building object. A corresponding inspection system is also provided.

A mobile robot and a method of controlling the same are provided, and more specifically, a technology of automatically generating a map of a lawn working area by a lawn mower robot. The mobile robot includes one or more tags configured to receive a signal from one or more beacons, a vision sensor configured to distinguish and recognize a first area and a second area on a travelling path of the mobile robot and acquire position information of a boundary line between the first area and the second area, and at least one processor configured to determine position coordinates of the mobile robot based on pre-stored position information of the one or more beacons, determine position coordinates of the boundary line based on the determined position coordinates of the mobile robot and the acquired position information of the boundary line, and generate a map of the first area while travelling along the determined position coordinates of the boundary line.

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 information processing apparatus according to an embodiment of the present technology includes a sub-goal setting unit, a local-route planning unit, and a speed planning unit. The sub-goal setting unit sets a sub-goal on the basis of environment information including position information of a set destination, map information of a travel environment of a mobile apparatus, and self-position information of the mobile apparatus. The local-route planning unit plans a movement route of the mobile apparatus on the basis of the sub-goal. The speed planning unit plans a speed of the mobile apparatus at an end point of a route extended by a predetermined length from the sub-goal.

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.

A method for controlling an autonomous, mobile robot which is designed to navigate independently in a robot deployment area, using sensors and a map. According to one embodiment, the method comprises detecting obstacles and calculating the position of detected obstacles based on measurement data received by the sensors, and controlling the robot to avoid a collision with a detected obstacle, the map comprising map data that represents at least one virtual blocked region which, during the control of the robot, is taken into account in the same way as an actual, detected obstacle.

An autonomous traveling body includes a vehicle body, a mover, an obstacle detector, a traveling controller, and a storage. The mover causes the vehicle body to travel. The traveling controller controls the mover based on a detection result of the obstacle by the obstacle detector. The storage stores an obstacle detection area around the vehicle body. The obstacle detection area includes a stop area having a predetermined width with the traveling direction of the vehicle body as an axis, and first and second deceleration areas excluding the stop area. When at least a portion of the obstacle is included in the stop area, the traveling controller stops the vehicle body. When at least a portion of the obstacle is included in the first deceleration area or the second deceleration area, the traveling controller reduces the traveling speed of the vehicle body.

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.

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.