Provided is a robot system. The robot system includes a guide rail, a slider configured to move along the guide rail, a first source disposed the slider to move together with the slider, a rotation arm configured to rotate by the first driving source, and a vehicle service robot installed the rotation arm to move by the rotation arm.

Disclosed is a mobile robot. The mobile robot may include a body and a controller. The mobile robot may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and may perform communication with other electronic devices in a 5G communication environment. Accordingly, user convenience can be significantly improved.

Provided is a self-propelled inspection device capable of improving efficiency of system introduction cost, setting, and update work necessary for self-propelling in a self-propelled inspection device expected to be operated outdoors for a long period of time. Therefore, there is provided the self-propelled inspection device that autonomously inspects an inspection object while autonomously traveling an inspection route, the self-propelled inspection device including: a self-position estimation unit that estimates a self-position; a map information database that manages map information for autonomous traveling; a traveling unit including a drive mechanism and a steering mechanism; a sensor that senses the inspection object; a map information update unit that updates the map information based on information sensed by the sensor; and a traveling unit control unit that controls the traveling unit based on the updated map information.

The disclosure herein generally relates to the field of autonomous navigation, and, more particularly, to a diverse trajectory proposal for autonomous navigation. The embodiment discloses a hierarchical network based diverse trajectory proposal for autonomous navigation. The hierarchical 2-stage neural network architecture maps the perceived surroundings to diverse trajectories in the form of trajectory waypoints, that an autonomous navigation system can choose to navigate/traverse. The first stage of the disclosed hierarchical 2-stage Neural Network architecture is a Trajectory Proposal Network which generates a set of diverse traversable regions in an environment which can be occupied by the autonomous navigation system in the future. The second stage is a Trajectory Sampling network which predicts a fine-grained trajectory/trajectory waypoint over the diverse traversable regions proposed by Trajectory Proposal Network.

Systems, methods, computing platforms, and storage media for transporting a mobile inventory transportation unit (MITU) in a communication network are disclosed. Exemplary implementations may include the mobile inventory transportation communication network comprising the MITU, a transportation system, a first and a second central system, in communication with each other, the MITU comprising a housing, an inventory storage device, a power device, a drive device, a navigation device, a sensing device, and a control device. The transportation system may be configured to physically receive and transport the MITU from a first point to a second point, the second central system may be configured to determine an inventory demand at a second or more location and transmit inventory request data to the first central system, and the first central system may be configured to schedule the movement of the MITU and control the delivery of the MITU to a final destination.

A route determination method, a route determination system, and a route determination program are provided for determining a target route where a work vehicle can work while performing automatic traveling without damaging the topsoil of the work site.

According to one embodiment, a vehicle guidance device is to be installed in a vehicle for providing path guidance to the vehicle. The device includes a plurality of receivers that receives a ranging information signal via a ultrasonic-wave ranging sensor, the ranging information signal including an ultrasonic-wave ranging signal on which path guidance information is superimposed, the ultrasonic-wave ranging signal being for measuring a distance to an object; an information extractor that extracts, for each of the receivers, the path guidance information from the ranging information signal; and a path guide that provides the path guidance on the basis of a distance corresponding to the ultrasonic-wave ranging signal and the path guidance information.

The systems and methods described herein disclose detecting obstacles in a vehicular environment using host vehicle input and associated trust levels. As described here, measured vehicles, either manual or autonomous, that detect an obstacle in the environment will operate to respond to the obstacle. As such, those movements can be used to determine if an obstacle exists in the environment, even if the obstacle cannot be detected directly. The systems and methods can include a host vehicle receiving prediction data about an evasive behavior from one or more measured vehicles in a vehicular environment. A trust level can then be established for the measured vehicles. An obscured obstacle can be determined using the evasive behavior and the trust level which can then be mapped in the vehicular environment. A guidance input can then be created for the host vehicle using the obscured obstacle and the trust level.

A method for docking a robot at a charging station includes the following steps: the charging station outputs a first transmitting signal and a second transmitting signal, wherein an overlapping zone and two non-overlapping zones are formed within the signal transmission range of the first and second transmitting signals, and a blank zone forms within a predetermined distance. When the robot needs to move to the charging station, the robot detects its entry into the overlapping zone or one of the two non-overlapping zones, and the robot moves in the direction of the charging station by alternately moving in and out between the overlapping zone and one of the two non-overlapping zones until the robot moves to the blank zone, then the robot either moves directly towards the charging station, or rotates and then moves backwardly towards the charging station, thereby allowing the robot to dock at the charging station.

Techniques for determining information associated with sounds detected in an environment based on audio data are discussed herein. Audio sensors of a vehicle may determine audio data associated with sounds from the environment. Sounds may be caused by objects in the environment such as emergency vehicles, construction zones, non-emergency vehicles, humans, audio speakers, nature, etc. A model may determine a classification of the audio data and/or a probability value representing a likelihood that sound in the audio data is associated with the classification. A direction of arrival may be determined based on receiving classification values from multiple audio sensors of the vehicle, and other actions can be performed or the vehicle can be controlled based on the direction of arrival.