This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.

A reading device includes a first support member extending vertically, a scanner attached to the first support member and including a window facing a first direction and through which an item can be scanned by an operator, an extension member attached to an upper portion of the first support member and extending in the first direction above the scanner, a camera attached to an end of the extension member in the first direction and configured to capture an image of the operator, and a controller configured to issue a notification signal when the controller detects from the captured image that the operator is carrying out a predetermined operation.

A system includes an autonomous vehicle (AV) comprising a sensor, a control subsystem, and an operation server. The control subsystem receives sensor data comprising location coordinates of the AV from the sensor. The operation server detects an unexpected event from the sensor data, comprising at least one of an accident, an inspection, and a report request. The operation server receives a message from a user comprising a request to access particular information regarding the AV and location data. The operation server associates the AV with the user if the location coordinates of the AV match location data of the user. The operation server establishes a communication path between the user and a remote operator for further communications.

An information processing apparatus, an information processing method, and an information medium control motion of a moving body according to position information corresponding to a predetermined array pattern. An information processing apparatus includes an information acquisition unit acquires position information from a sensor configured to read a predetermined array pattern, and a motion control unit controls motion of a first moving body including movement in a real space based on the position information.

A system for estimating a trajectory of a vehicle includes vehicle state sensors detecting a state of movement of the vehicle and environment sensors monitoring an environment of the vehicle. A free space module determines a free space corridor based on the detected state of movement of the vehicle and the monitored environment of the vehicle. A goal point determination module determines at least one goal point for the trajectory to be estimated based on the free space corridor. A trajectory planning module estimates a reference trajectory based on the at least one goal point and calculates a deviation between an actual trajectory and the reference trajectory of the vehicle. A trajectory control module minimizes the deviation between the actual trajectory and the reference trajectory of the vehicle and outputs optimal control parameters for the movement of the vehicle based on the minimized deviation.

A method for fusing a first occupancy map and a second occupancy map comprises: determining at least one fusion parameter representing a potential dissimilarity between the first occupancy map and the second occupancy map and determining a fused occupancy map representing free and occupied space around the vehicle. The fused occupancy map is determined based on the first occupancy map, the second occupancy map, and a fusion rule. The fusion rule is configured to control the influence of the first occupancy map and/or the second occupancy map on the fused occupancy map based on the at least one fusion parameter.

Disclosed is a transporting robot which executes a mounted artificial intelligence (AI) algorithm and/or machine learning algorithm and communicates with different electronic devices and external servers in a 5G communication environment. The transporting robot includes a wheel driver, a loading box, and a robot controller. The transporting robot is provided such that a transporting service using an autonomous robot may be provided.

A vehicle control device includes a communication unit configured to communicate with a plurality of autonomous driving vehicles configured to perform autonomous traveling, and a processor. The processor is configured to, when an abnormality occurs in or around at least one first autonomous driving vehicle among the plurality of autonomous driving vehicles, determine a travel instruction for controlling traveling of the first autonomous driving vehicle, transmit the travel instruction to the first autonomous driving vehicle via the communication unit, set a priority representing the degree of the priority in which an instruction operator is notified of the travel instruction in the order determined according to the content of the abnormality, notify any one of at least one instruction terminal of the determined travel instruction in the order of highest priority, and receive a result of checking the determined travel instruction from the instruction terminal.

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 soil measure, such as a soil cone index, and a vehicle index indicating the amount of force the vehicle exerts on the ground as it travels over the ground, are obtained and compared to identify a soil damage score. The soil damage score can be mapped over a field and an agricultural vehicle can be controlled based upon the soil damage score. In another example, a detector detects a fill level of a material storage compartment on an agricultural vehicle. The inflation pressure of tires on the agricultural vehicle is controlled based upon the detected fill level.