Embodiments of the present disclosure may include a method for optimizing flight of an unmanned aerial vehicle (UAV) including a payload, the method including receiving one or more human-initiated flight instructions. Embodiments may also include determining a UAV context based at least in part on Inertial Measurement Unit (IMU) data from the UAV. Embodiments may also include receiving payload identification data. Embodiments may also include accessing a laden flight profile based at least in part on the payload identification data. Embodiments may also include determining one or more laden flight parameters. In some embodiments, the one or more laden flight parameters may be based at least in part on the one or more human-initiated flight instructions, the UAV context, and the laden flight profile.

In a method of transporting cargo suspended by multiple UAVs, each of the multiple UAVs flies such that a relative positional relationship of each of the multiple UAVs to the cargo is maintained. This method of transporting cargo is achieved by one or both of a ground position control mode in which flight of each of the multiple UAVs is controlled to follow a change in a position of the cargo relative to the ground, and an interval control mode in which the flight of each of the multiple UAVs is controlled so as to maintain an interval of each of the multiple UAVs to the cargo.

Various examples are provided related to generating a map of an environment. In one example, a method includes obtaining a coarse global mapping of an environmental space; determining a robotic traversal path within the environmental space using the coarse global mapping, the robotic traversal path maintaining a targeted distance to a nearest structure within the environmental space; initiating traversal of a robot along the determined robotic traversal path, the robot obtaining depth sensor measurements of the nearest structure during traversal along the determined robotic traversal path; and refining the robotic traversal path during traversal by the robot along the determined robotic traversal path based upon the depth sensor measurements, where the robotic traversal path is refined online to achieve targeted resolution and local accuracy of the depth sensor measurements. A refined map of the environmental space can be generated using the depth sensor measurements.

The present invention relates to a method for filtering inputs to a localization method of an autonomous vehicle (10) in an operating environment (U), comprising the steps of: providing a predefined occupancy map which represents known objects (W) present in the operating environment (U); generating an optimized data structure which represents occupied cells in a display of the occupancy map in a coordinate system; detecting the operating environment (U) of the vehicle (10) by means of at least one sensor unit (12) which is configured to detect objects in the operating environment (U) and their distances from the vehicle (10), wherein each detected object is assigned a measured value (M1, M2) in the coordinate system; and for each measured value (M1, M2) searching within a search radius around the measured value (M1, M2) for an occupied cell in the optimized data structure; if an occupied cell is found within the search radius, forwarding the measured value (M1) to a subsequent localization method, and, if no occupied cell is found within the search radius, discarding the measured value (M2).

Provided are an apparatus and method for rail detection and control of a motion of a mobile robot for safely switching driving of the mobile robot between a flat area and a rail area in an environment in a greenhouse in which a rail is provided for pipe heating. For more accurate rail detection, accurate three-dimensional (3D) point cloud data is obtained using a tilting laser scanner and is analyzed to detect a position of the rail and control a motion of a mobile robot for rail docking. The apparatus includes a sensor configured to be mounted in a mobile robot, and a rail detection unit configured to obtain 3D point cloud data using the sensor and detect the 3D point cloud data.

Systems, apparatuses and methods are provided herein for providing surveying premises of a customer. In one embodiment, a system for surveying premises of a customer comprises: an unmanned aerial vehicle (UAV) comprising a three dimension (3D) scanner and an image sensor, and a control circuit comprising a communication device configured to communicate with the UAV. The control circuit being configured to: receive, from a customer, a premises location, instruct the UAV to travel to the premises location to collect a set of data, form a 3D point cloud model of the premises based on 3D data collected by the 3D scanner of the UAV, identify one or more features of the premises based on the 3D point cloud model and image data collected by the image sensor of the UAV, and generate a recommendation to the customer based on the one or more features of the premises.

There is provided a computerized method of controlling ascent of an underwater vehicle (UV) from a safety depth to a water surface, the method comprising: at safety depth, controlling the UV to collect, from a passive sonar associated with the UV, first data indicative of first locations of surface targets within a first surface area of interest; controlling ascent of the UV to an intermediate depth in accordance with the first data; at the intermediate depth, controlling the UV to collect second data indicative of second locations of surface targets within a second surface area of interest, wherein the second data comprises one or more of: data from a passive sonar, data from one or more magnetic sensors, data from an active sonar, data from a light detection and ranging (LIDAR) scanner; and controlling ascent of the UV to a periscope depth in accordance with the second data.

A robot includes: a light emitter configured to output light; a camera; and at least one processor configured to: obtain first information about an object using the camera while the light emitter is outputting the light, obtain second information about the object using the camera while the light emitter is not outputting the light, obtain third information about the object based on the first information and the second information, obtain information about an external light area based on at least one from among the first information, the second information, and the third information, and generate a driving path of the robot based on the information about the external light area.

A system for intralogistics comprising a self-propelled load bearing cart and a remote controlled or autonomous self-propelled guide unit. The self-propelled load bearing cart comprises a drive unit comprising at least one drive wheel for propelling the self-propelled load bearing cart, a mechanical connection, and a computing unit connected to the drive unit. The computing unit comprises a transceiving unit for communicating with the remote controlled or autonomous self-propelled guide unit, and the remote controlled or autonomous self-propelled guide unit comprises a mechanical connection configured to connect to the mechanical connection of the self-propelled load bearing cart, such that a mechanical interconnection can be created between the remote controlled or autonomous self-propelled guide unit and the self-propelled load bearing cart.

The present disclosure provides a method for operating an autonomous mobile device and a storage medium. The method includes: performing re-localization for the autonomous mobile device; detecting a first feature of an identifier; calculating a first relative location of an object having the identifier relative to the autonomous mobile device; calculating, based on the first relative location and a real time location of the autonomous mobile device, a current global location of the object; retrieving a prior global location of a charging station; comparing the current global location and the prior global location; when a difference between the current global location and the prior global location is greater than or equal to a predetermined difference threshold: detecting a second feature of the object and determining whether the object is the charging station; when the object is the charging station, setting a temporary restricted zone.