A system comprises a GNSS sensor onboard an aerial vehicle; a monitor warning system (MWS) that determines whether the vehicle is in a GNSS denied environment; and a flight management system that includes a landing guidance module, and a database having location coordinates of landing sites. Onboard vision sensors and a radar velocity system (RVS) communicate with the guidance module. When the MWS determines that the vehicle is in a GNSS denied environment, the guidance module calculates an optimal flight path by receiving image data from the vision sensors; receiving position, velocity and altitude data from the RVS; receiving location coordinates of a landing site; processing the image data, and the position, velocity and altitude data, to determine a location of the vehicle and provide 3D imaging of a route to the landing site; and calculating a flight path angle to the landing site, using vehicle and landing site coordinates.

Systems, methods, and devices that can be used to augment and address various deficiencies such as vision system impairment in autonomous robotic systems are described herein. A system may include at least one sensing device that is used to monitor data and trigger corrective operations in response to detected low visibility or obstructed conditions such as a sun glare condition.

A method is provided for simplifying a takeover of control of a motor vehicle by a vehicle-external operator. In the method, images of the surroundings of the vehicle are captured from the vehicle and semantically segmented. Errors in a corresponding segmentation model are predicted on the basis of at least one such image each. If a corresponding error prediction triggering a request for the takeover of control is made, an image-based visualization is automatically generated in which exactly one region corresponding to the error prediction is visually highlighted. The request and the visualization are then sent to the vehicle-external operator.

The present disclosure discloses a mobile robot positioning method and system based on wireless ranging sensors, and a chip. The mobile robot positioning method adopts a manner of controlling a mobile robot to traverse two target positions successively to acquire a distance between the mobile robot at each traversed position and a fixed positioning base station, rather than calculate distances between the robot at the same position and different base stations, such that the trouble of arranging a plurality of base stations in a positioning area is reduced.

An electronic apparatus is disclosed. The electronic apparatus includes a communication interface including circuitry, a sensor, a memory stored with data including a plurality of movement patterns at a normal operation of a robot device, and at least one processor electrically coupled with the memory, and the at least one processor is configured to obtain a movement pattern of the robot device based on at least one from among a signal strength received from the robot device through the communication interface or sensing data of the sensor, and identify an operating state of the robot device according to the obtained movement patterned based on the data as a normal operation or an abnormal operation.

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 travel map creating apparatus includes a position sensor obtaining the positional relationship of an adjacent object relative to the apparatus; a floor map creator creating a floor map based on the positional relationship; a self-position calculator calculating the self-position on the floor map; a marker identifier identifying a marker; a marker position calculator calculating the relative position of the marker to the apparatus; a mode switcher switching the mode of the apparatus between a floor map creation mode and a marker identification mode; a restricted access information generator, based on the floor map, the self-position, and the relative position of the marker, defining a boundary of a restricted area to which the entry of an autonomous mobile robot is prohibited and generating restricted access information including the boundary; and a travel map creator creating a travel map including the defined restricted area based on the restricted access information.

In a marker detection system for a vehicle including a magnetic sensor to detect a magnetic marker laid in a road surface, the magnetic sensor can measures, for each axis, magnitudes of magnetic components acting along an axis in a vertical direction and an axis in a forwarding direction, and a detection unit identifies a candidate zone to which a possibility that the magnetic marker belongs is high, based on a change in a forwarding direction of the vehicle of a magnetic measurement value along any of the axes and determines whether the magnetic marker has been detected in accordance with a degree of synchronization between a first signal indicating a change of a magnetic measurement value regarding one axis in the candidate zone and a second signal indicating a change of a magnetic measurement value regarding the other axis in the candidate zone.

Embodiments described herein may relate to an unmanned aerial vehicle (UAV) navigating to a target in order to provide medical support. An illustrative method involves a UAV (a) determining an approximate target location associated with a target, (b) using a first navigation process to navigate the UAV to the approximate target location, where the first navigation process generates flight-control signals based on the approximate target location, (c) making a determination that the UAV is located at the approximate target location, and (d) in response to the determination that the UAV is located at the approximate target location, using a second navigation process to navigate the UAV to the target, wherein the second navigation process generates flight-control signals based on real-time localization of the target.

A computer-implemented system and associated method of operating a Small Uncrewed Aircraft System (SUAS) including at least one Small Uncrewed Aircraft or drone. The method comprises capturing data during operation of the SUAS from a number of sensors of different types, performing analysis on the captured data using one or more Artificial Intelligence/Machine Learning (AI/ML) models that have been trained on data sets including historical SUAS data and SUAS system fault data, to predict or identify a potential SUAS failure mode, and when a potential failure mode is predicted or identified, providing a course of action for further operation of the SUAS based on a severity and predicted timing of the SUAS failure mode.