There is provided a cleaning robot including a light source module, an image sensor and a processor. The light source module projects a line pattern and a speckle pattern toward a moving direction. The image sensor captures an image of the line pattern and an image of the speckle pattern. The processor calculates one-dimensional depth information according to the image of the line pattern and calculates two-dimensional depth information according to the image of the speckle pattern.

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.

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.

The present invention relates to a mobile robot and a method for controlling the same. The present invention provides a mobile robot using the rotational force of at least three rotating members as a movement power source and a method for controlling the same, in which the mobile robot is controlled to effectively travel along a set curved driving path and not deviate from the set curved driving path, or to immediately return to the curved driving path when deviating from the set curved driving path.

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.

An obstacle avoidance method for a self-moving robot includes: obtaining and recording, by the self-moving robot, information about an obstacle encountered during traveling, wherein the information about the obstacle includes type information and location information of the obstacle; and receiving, by the self-moving robot, an operation instruction for a specified obstacle, wherein the operation instruction is configured for instructing the self-moving robot, when detecting an obstacle of a same type as the specified obstacle in a region range labeled with the specified obstacle, not to perform an obstacle avoidance operation on the obstacle of the same type.

A method for bypassing impassable objects by a robot through the use of artificial intelligence. A reliable and low-cost bypassing of obstacles taking account of data privacy aspects is achieved in that in the event of a collision of the robot with an obstacle, an optical original recording of the obstacle is produced, artificial duplicates being generated from the original recording, the duplicates being used to train the artificial intelligence. A system has a robot and an IT infrastructure configured to execute the method.