A robot includes a torso, a head coupled to the torso so as to be rotatable with respect to the torso, and at least one processor. The at least one processor determines whether the torso is inclined from a horizontal direction and, in a case where a determination is made that the torso is inclined from the horizontal direction, controls an actuator to rotate the head with respect to the torso such that the head faces the horizontal direction.

A patrol robot including a plurality of magnetic sensors for detecting a magnetic marker laid on a traveling road has at least two or more magnetic sensors arrayed on a sensor array line linearly extending along any direction. In the patrol robot, two sensor array lines are formed, and since at least any one sensor array line can cross with respect to a relative moving direction of the magnetic marker with a movement of the patrol robot, the magnetic marker can be detected with high reliability, irrespective of the moving mode.

In a ship control system of one embodiment, a route command unit outputs a route command including a target ship position and a target bow direction of a ship. An information detector detects ship information including an actual ship position and an actual bow direction of a ship. An external force vector estimation unit estimates an external force vector received by a ship, using the ship information and a hull motion model related to a hull motion of the ship. A control command unit controls both rotational frequency of a main engine and a rudder angle of a ship, based on the route command, the ship information, and the external force vector.

A self-moving device, including: a moving module, a task execution module, a control module. The control module is electrically connected to the moving module and the task execution module, controls the moving module to actuate the self-moving device to move, controls the task execution module to execute a working task. The self-moving device further includes a satellite navigation apparatus, electrically connected to the control module and configured to receive a satellite signal and output current location information of the self-moving device. The control module determines whether quality of location information output by the satellite navigation apparatus at a current location satisfies a preset condition, controls, if the quality does not satisfy the preset condition, the moving module to actuate the self-moving device to change a moving manner, to enable quality of location information output by the satellite navigation apparatus at a location after the movement to satisfy the preset condition.

A system and a method for generating three-dimensional scan data of areas of interest, the method comprising a user defining the areas of interest using a mobile device in the environment, and a scanning device performing a scanning procedure at each defined area of interest to generate the scan data of the respective area of interest, wherein defining the areas of interest comprises, for each area of interest, generating identification data, wherein generating the identification data at least comprises generating image data of the respective area of interest, and the scanning procedure at each defined area of interest is performed by a mobile robot comprising the scanning device and being configured for autonomously performing a scan of a surrounding area using the scanning device, the mobile robot having a SLAM functionality for simultaneous localization and mapping and being configured to autonomously move through the environment using the SLAM functionality.

Provided is a method of operating an unmanned mobile vehicle for detecting an indoor environment. The method according to an embodiment of the present disclosure includes obtaining first motion information using a LiDAR sensor provided on the unmanned mobile vehicle, obtaining second motion information using an inertial sensor provided on the unmanned mobile vehicle, performing correction on the first motion information and the second motion information on the basis of error models corresponding to the LiDAR sensor and the inertial sensor, and determining final position information of the unmanned mobile vehicle on the basis of the correction.

A system to remotely operate a vehicle in the presence of limited bandwidth and high latency is disclosed. A remote driver station calculates and transmits a trajectory to a vehicle. The vehicle relocalizes and then follows said trajectory. Images captured by cameras on the vehicle are processed and then compressed for transmission over the limited bandwidth connection to the remote driver. Other sensors on the vehicle collect information which is transmitted to the remote driver as well as aiding in the processing and compression of visual information.

A method for selecting a direction, a mower, and an electronic equipment are provided. With the method, a boundary of a target region is identified via a mower. If a distance between the mower and the boundary is determined to be within a first preset range according to the boundary as identified, the mower is controlled to move along the boundary in a movement direction. The movement direction is either a leftward direction along the boundary or a rightward direction along the boundary, which direction has a less angle with respect to an orientation of a head of the mower.

A gyro unit mounted on a steered object for performing steering based on a steering signal received from the outside comprises a gyro sensor, a calculation portion and a controller. The calculation portion is configured to perform calculations for posture control of the steered object based on the steering signal and a detection signal of the gyro sensor. The controller is configured to perform control such that a control direction of the posture control switches when the steered object moves forward and backward.

The group control system controls a plurality of mobile objects capable of autonomously traveling in a predetermined area. The group control system includes a position information estimation unit that estimates position information of each mobile object, a route planning unit that creates a route plan of each mobile object based on the estimated position information, an acceleration data acquisition unit that acquires acceleration data acquired from the acceleration sensor, and a mobile object position acquisition unit that acquires an actual position of each mobile object using the position sensor, and learns the deep reinforcement learning model so as to correct a deviation amount between an actual position of the mobile object acquired using the position sensor and an estimated position of the mobile object based on the acquired acceleration data.