In an artificial intelligence server for providing information to a user, the artificial intelligence server includes a communication unit configured to communicate with a plurality of artificial intelligence apparatuses deployed in a service area and a processor configured to receive at least one of speech data of the user or terminal usage information of the user from at least one of the plurality of artificial intelligence apparatuses, generate intention information of the user based on at least one of the received speech data or the received terminal usage information, generate status information of the user using the plurality of artificial intelligence apparatuses, determine an information providing device among the plurality of artificial intelligence apparatuses based on the generated status information of the user, generate output information to be outputted from the determined information providing device, and transmit a control signal for outputting the generated output information to the determined information providing device.

An autonomous robotic exploration method based on a reduced approximated generalized Voronoi graph, the method including: 1) constructing a reduced approximated generalized Voronoi topological map based on a morphological method; 2) obtaining an Next-Best-View and planning a global path from the robot to the Next-Best-View; and 3) navigating to the Next-Best-View along the global path R={r.sub.0, r.sub.1, r.sub.2, . . . , p.sub.NBV} based on a visual force field (VFF) algorithm.

A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

A travel processing unit causes a work vehicle to travel manually on the basis of an operator's manual travel operation. A route-generation processing unit generates a target route, which is a route for the work vehicle to travel automatically, when the operator's route-generation instruction operation to the operation unit is accepted while the work vehicle is traveling manually. A travel processing unit causes the work vehicle to travel automatically along the target route when the operator's automatic-travel start operation to the operation unit is accepted after the route-generation instruction operation.

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 device for identifying a signaling unit on a road on which a vehicle is traveling is described. The device is configured to determine an arrangement of one or more signal signs of the signaling unit on the basis of environmental data of one or more environmental sensors of the vehicle. The device is further configured to assign the one or more signal signs to one or more corresponding grid cells of a signal sign grid based on assignment logic.

Systems and methods for dynamically reprioritizing pick jobs in a fulfillment center are described herein. The example systems can be configured to periodically classify pick jobs in order to optimize throughput of the fulfillment center. The classifying can include determining an estimated completion time for pick jobs and identifying at-risk jobs that may complete after their associated due dates. The at-risk jobs can be assigned to autonomous vehicles based primarily on their associated due dates. Other pick jobs that are not at-risk can be assigned to autonomous vehicles based primarily on efficiency. The at-risk pick jobs can be assigned to autonomous vehicles before the other pick jobs.

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.

A method of controlling a mobile robot includes a first basic learning process of generating a first basic map based on environment information acquired in a traveling process, a second basic learning process of generating a second basic map based on environment information acquired in a separate traveling process, and a merging process of merging the first basic map and the second basic map to generate a merged map.