The present invention relates to a mobile security robot equipped with a micro flight device, which uses a camera mounted on the mobile security robot to patrol a predetermined area by the mobile security robot capable of autonomous driving and to patrol an area where the mobile security robot cannot move by the mounted micro flight device. Accordingly, there is an advantage in that it can efficiently patrol a much wider area compared to the patrol using only the mobile security robot.

This disclosure describes systems and methods for a multipoint cable cam (MPCC) of an aerial vehicle. A method includes operations of receiving user input associated with a predetermined path and correlating the received user input with stored global positioning satellite (GPS) data to generate one or more virtual waypoints along the predetermined path. The method includes processing the one or more virtual waypoints to generate a spline-based flight path. The method may include storing the spline-based flight path and transmitting the spline-based flight path to the aerial vehicle.

An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.

A controller for an unmanned aerial vehicle (UAV) comprising an image capture means, the controller comprising: inputs arranged to receive: positional data relating to the UAV, a vehicle and a user device; image data captured by the image capture means; a processor arranged to process the received positional data to determine the relative locations of the UAV, vehicle and user device; an output arranged to output a control signal for controlling the UAV and to output an image signal comprising captured image data; wherein the processor is arranged to: generate the control signal for the UAV such that the image data captured by the image capture means comprises at least an image of an obscured portion of the vehicle that is obscured from a field of view of a user of the user device.

A method for controlling a movable object is provided. A user input that includes a first parameter corresponding to a first coordinate system is received and an operation mode is determined. In response to determining the operation mode being a first operation mode, a second parameter corresponding to a second coordinate system is generated and the movable object is controlled to move based on the second parameter. In response to determining the operation mode being a second operation mode, the first parameter is translated to a third parameter corresponding to the second coordinate system and the movable object is controlled to move based on the third parameter.

Many different types of systems are utilized and tasks are performed in a marine environment. The present invention provides various configurations of castable devices that can be operated and/or controlled for such systems or tasks. One or more castable devices can be integrated with a transducer assembly, such as a phased array, that emits sonar beams and receives sonar returns from the underwater environment. Processing circuitry may receive the sonar returns, process the sonar returns, generate an image, and transmit the image to a display.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

An aerial vehicle is programmed or configured to respond to reports of events or conditions within spaces of a facility. The aerial vehicle travels to a location of a reported event or condition and captures data using onboard sensors. The aerial vehicle independently determines whether the reported event or condition is occurring, or is otherwise properly addressed by resources that are available at the location, using images or other data captured by the onboard sensors. Alternatively, the aerial vehicle transmits a request for additional resources to be provided at the location, where necessary. A map of the location generated based on images or other data captured by the onboard sensors may be utilized for any purpose, such as to make one or more recommendations of products that are appropriate for use at the facility.

A shelf space allocation management device manages products allocated on shelves in a store by use of an imaging device. The shelf space allocation management device acquires an image including a position assumed to be changed in allocation status of each product on each shelf; it determines whether each product reflected in the image matches one of pre-recorded images, thus executing a product allocation inspection. Herein, the shelf space allocation management device specifies a position at which a person causes any change in the allocation status of each product on each shelf, and therefore it may control the imaging device to capture an image including the position. It is possible to carry out a product allocation inspection for each period determined in advance depending on the type of each product, or it is possible to carry out a product allocation inspection being triggered by a customer purchasing each product.