Disclosed herein are system, method, and computer program product embodiments for locating, identifying, and tracking a known criminal, fugitive, missing person, and/or any other person of interest. An embodiment operates by deploying an unmanned aerial vehicle, determining the mode of operation of the UAV, operating the UAV in accordance with the mode of operation of the UAV, determining whether a subject has been detected, capturing a first voice sample associated with the subject, authenticating the identity of the subject, and transmitting the GPS location of the unmanned aerial vehicle to a computing device.

The method of managing unmanned aerial vehicle (UAV) patrols is a method for organizing and managing a fleet of UAVs for patrolling a geographic region for the detection of threats. During patrols, the UAVs visit two types of waypoints: critical waypoints and strategic waypoints. To establish the strategic waypoints, a set of seed points are generated using a beta probability distribution. Voronoi tessellation is applied to produce the set of strategic waypoints within the region of interest by using the set of seed points as an input. Each of the strategic waypoints has a set of coordinates on the three-dimensional occupancy map associated therewith. The set of coordinates associated with each of the strategic waypoints is a centroid of a corresponding Voronoi cell produced by the Voronoi tessellation. A priority score is assigned to each of the critical waypoints and each of the strategic waypoints.

A method for performing an autonomous inspection. The method comprises traversing, by an autonomous sensor apparatus, a path through a site having three-dimensional objects located therein. The site includes three-dimensional objects located therein. The method comprises obtaining, by a plurality of sensors on-board the autonomous sensor apparatus, one or more data sets throughout the path. Each of the one or more data sets are associated with an attribute of one or more three-dimensional objects. The method comprises generating, by the first, second, or third processor, a working model from a collocated data set; and comparing, by the first, second, or third processor, the working model with one or more pre-existing models; to determine the presence and/or absence of anomalies. The presence and/or absence of anomalies are communicated as human-readable instructions.

A method for selecting an altitude changing phase route for an aircraft is presented. The method comprises receiving sequences of multivariate flight data from at least one prior flight, receiving a set of flight parameters for the aircraft including at least a total altitude change and a takeoff weight, and receiving a set of candidate altitude changing phase routes having candidate step profiles. For each candidate altitude changing phase route, a sequence of fuel burn quantities is predicted for the respective candidate step profile based on at least the sequences of multivariate flight data and the set of flight parameters. The fuel burn quantities are summed over the candidate altitude changing phase route to obtain an estimated fuel burn. A preferred candidate altitude changing phase route having a lowest estimated fuel burn is indicated.

A drone system for synthetic aperture radar (SAR) operation to control and operate an aerial vehicle mounted with an SAR may comprise: a flight control module configured to control a low level of the aerial vehicle; a high-level control module configured to perform communication for swarm control of the aerial vehicles, receive flight information from the flight control module, and transmit a command to the flight control module; a link module configured to link the flight control module and the high-level control module; and a data acquisition board connected to the high-level control module and configured to store a flight log from the high-level control module and radar data from a radar module provided with the SAR.

A method for detecting anomalies in a swarm system comprises: collecting first movement data from multiple vehicles moving as a swarm in a first scenario; generating first training data based on positioning data and second training data based on multi-channel inertial sensor data from the first movement data; training a first learning model using the first training data and multiple second learning models using the second training data for each vehicle; receiving real-time second movement data from vehicles moving as a swarm in a second scenario; generating first input data based on positioning data from the second movement data; inputting the first input data into the first learning model to detect abnormal vehicles in real-time; generating second input data for abnormal vehicles based on inertial sensor data from the second movement data; and inputting the second input data into the corresponding second learning model to identify abnormal channels in the inertial measurement unit of abnormal vehicles.

Present implementations can display 3D illuminations using Flying Light Specks (FLS). Each FLS can include a miniature (hundreds of micrometers) sized drone with one or more light sources to generate colors and textures with adjustable brightness. The FLS can be network enabled with a processor and local storage. Synchronized swarms of cooperating FLSs can render static and motion illumination of virtual objects in a pre-specified 3D volume, an FLS display. Present implementations can consider the limited flight time of an FLS on a fully charged battery and the duration of time to charge the FLS battery. Present implementations can accommodate failure of FLS as a norm of operation, rather than an exception. A hardware and software architectures for an FLS-display can compute flight paths of FLSs for illumination. With motion illuminations, one technique can minimize overall distance traveled by the FLSs significantly.

A method for managing a position of a remotely-controlled vehicle, referred to as a drone, relative to a control entity, the control entity being configured to control the drone by means of a stream of control data transmitted indirectly within a communication established between the control entity and the drone via a communication network, the method including: for at least one time instant of the communication, determining at least one parameter relating to a distance between the drone and the control entity; when the distance-related parameter does not lie within an interval of authorised values for a given piloting mode, applying at least one given management rule for the drone.

An intelligent system for a photovoltaic cleaning robot includes: a control terminal, an intelligent control center, a stain detection database, and an intelligent device; where the intelligent device includes a camera shooting module, a motion flight module, and a cleaning module; the intelligent device and the intelligent control center are integrated on the photovoltaic cleaning robot; the intelligent control center and the control terminal are connected by a wireless communication; the intelligent control center is connected to the stain detection database through the wireless communication. In the intelligent system for the photovoltaic cleaning robot, even in a distributed photovoltaic power station with a harsh environment and terrain, it can also do the unmanned intelligent cleaning operation and maintenance work of the photovoltaic plate, where the operator only needs to operate the control terminal according to the attenuation of power generation of the power station to issue simple instructions.

A paraglider controller for controlling a paraglider drive, in particular an electric ascent aid, for a paraglider. The paraglider controller comprises a UI connection interface for sending and/or receiving user commands comprising a voice signal. Optionally, the ascent aid control further comprises a flight data interface for receiving flight data. Furthermore, the ascent aid control comprises an evaluation unit for evaluating received user commands and optionally flight data, wherein a user signal is output via the UI connection interface and/or a control signal is output via a control interface. An input and/or an output of user commands is thereby carried out using the UI connection interface by means of the speech signal.