A robot includes: a plurality of wheels; a plurality of motors; at least one sensor; a memory configured to store first information on a size of the robot; and a processor. The processor is configured to: acquire image data of an escalator from the at least one sensor, acquire second information on a size of a plurality of steps included in the escalator based on the image data, based on the first information and the second information, identify both a boarding position available for the robot to board the escalator among the plurality of steps, and a posture of the robot configured to allow the robot to board at the boarding position, acquire control information for controlling the robot to board at the boarding position in the posture when the boarding position and the posture have been identified, and control the plurality of motors based on the control information.

A robot includes: a plurality of wheels; a plurality of motors; at least one sensor; a memory configured to store first information on a size of the robot; and a processor. The processor is configured to: acquire image data of an escalator from the at least one sensor, acquire second information on a size of a plurality of steps included in the escalator based on the image data, based on the first information and the second information, identify both a boarding position available for the robot to board the escalator among the plurality of steps, and a posture of the robot configured to allow the robot to board at the boarding position, acquire control information for controlling the robot to board at the boarding position in the posture when the boarding position and the posture have been identified, and control the plurality of motors based on the control information.

An information processing system according to an embodiment of the present disclosure includes a first information processing device to be provided to a movable body and a second information processing device to be provided to a portion that differs from the movable body. The first information processing device includes a sensor portion, a generation portion, a control portion, and an integration portion. The sensor portion senses a first external environment. The generation portion uses sensor data acquired from the sensor portion to generate a first map. The control portion controls motion of a manipulator on the basis of the first map. The integration portion uses position information of inside the first external environment, with which portion the manipulator is in contact, integrates the first map and a second map acquired from the second information processing device with each other, and generates an integration map.

An automated vehicle comprising: a control unit configured to control movement of the automated vehicle to a location adjacent an estimated location of a drill hole; a scanning portion including one or more scanning devices configured to scan an area of terrain in the vicinity of the estimated location of the drill hole in order to determine an actual location of the drill hole, and to generate a point cloud representing at least a portion of the interior of the drill hole; at least one arm associated with the scanning portion, the at least one arm configured to move the scanning portion between a home position and one or more scanning positions; and an end effector associated with the at least one arm, the end effector being configured to perform one or more operations;
wherein, upon generating the point cloud, the at least one arm is configured, based on the point cloud, to position the end effector in substantial alignment with the drill hole so that the end effector can perform the one or more operations.

A system and method is provided for delivering electric energy to an electric vehicle via electric charging stations or kiosks where an energy delivery point is configured to provide energy to the electric vehicle via a connector or a wireless energy source. The method involves charging an electric vehicle by detecting, using a RFID tag reader associated with an electric vehicle, signals emanating from a marker positioned on the ground, where the marker includes one or more RFID tags, and where the RFID tag reader is able to recognize the signals despite weather conditions where the ground is covered by snow.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

A control system uses visual odometry (VO) data to identify a position of the vehicle while moving along a path next to the row and to detect the vehicle reaching an end of the row. The control system can also use the VO image to turn the vehicle around from a first position at the end of the row to a second position at a start of another row. The control system may detect an end of row based on 3-D image data, VO data, and GNSS data. The control system also may adjust the VO data so the end of row detected from the VO data corresponds with the end of row location identified with the GNSS data.

A control system uses visual odometry (VO) data to identify a position of the vehicle while moving along a path next to the row and to detect the vehicle reaching an end of the row. The control system can also use the VO image to turn the vehicle around from a first position at the end of the row to a second position at a start of another row. The control system may detect an end of row based on 3-D image data, VO data, and GNSS data. The control system also may adjust the VO data so the end of row detected from the VO data corresponds with the end of row location identified with the GNSS data.

Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.

Disclosed herein is a robot generating a map based on multi sensors and artificial intelligence and moving based on the map, the robot according to an embodiment including a controller generating a pose graph that includes a LiDAR branch including one or more LiDAR frames, a visual branch including one or more visual frames, and a backbone including two or more frame nodes registered with any one or more of the LiDAR frames or the visual frames, and generating orodometry information that is generated while the robot is moving between the frame nodes.