The present invention relates to an automated guided vehicle for transporting and placing a load, comprising a primary environmental sensor and at least one secondary environmental sensor, wherein the transport vehicle is configured, first, using the primary environmental sensor, to detect a drop-off location for the load and the region between the drop-off location and the transport vehicle and to check said drop-off location and said region for obstacles; if no obstacle is recognized, to travel to the drop-off location; during the journey to the drop-off location, to check the route and the drop-off location for obstacles using the secondary environmental sensor.

A submersible vessel comprises a hull. The hull contains a plurality of subsystems. The subsystems comprise a sensor subsystem configured to sense potential target information regarding a potential target, a database subsystem configured to store target characterization information, a processing subsystem coupled to the sensing subsystem and to the database subsystem, and an ordnance subsystem. The processing subsystem is configured to process the potential target information according to the target characterization information to confirm the potential target as being a confirmed target. The ordnance subsystem comprises an ordnance magazine configured to store ordnance. The ordnance is deployable against the confirmed target, wherein the confirmed target is an aircraft.

In accordance with an embodiment, a method of operating a vehicle within a vehicle swarm includes: receiving a sortie specification, the sortie specification specifying a desired behavior the vehicle swarm is to perform; obtaining a position identification within the vehicle swarm; and calculating a set of waypoints based on the received sortie specification and the position identification.

A monitoring and emergency control system for vector psyllid of citrus huanglongbing (HLB) is disclosed. The system includes a video acquisition module, an image recognition module, a control console, an early warning module and an unmanned aerial vehicle (UAV)-based flight prevention module. The video acquisition module acquires images by using 360-degree dead-angle-free cameras, the cameras are arranged at a plurality of points at a periphery and interior of an orchard, and each camera is numbered. The video acquisition module acquires real-time images, and transmits the real-time images to the image recognition module in real time for recognition and determination. The control console determines whether to send out warning information to an orchard manager and a flight prevention instruction according to a feedback result. The UAV-based flight prevention module receives the instruction, and then carries a pesticide box to take off to a region to kill the psyllid by applying pesticides.

A method for predictive fire control, comprising identifying a plurality of wildfire risk points on a power grid, calculating a route for an unmanned aerial vehicle (UAV) to intersect with a maximum number of points as a function of a range of the UAV, controlling the UAV to traverse the route, monitoring one or more sensors for an indication of a wildfire event and controlling the UAV to release a fire retardant on the fire.

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.

A system and method employing an autonomous aerial vehicle programmed to stimulate a pet to engage in playful activities exercising the pet. The system is capable of executing a variety of rapid movements and emitting specific sounds designed to elicit reactions from the pet, such as barks, whines, and human utterances. In a learning mode, the system analyzes pet reactions to various stimuli. In a normal mode, it prioritizes movements and sounds that generate the most intense responses. The system includes safety measures to maintain a safe distance between the vehicle and the pet, ensuring that the vehicle automatically retreats if the pet comes too close. The system autonomously returns to its launch position when the pet disengages or when battery levels are low.

In a delivery system S including an UAV 1 and a management server 2, the UAV 1 controls at least a position and/or an orientation of the UAV 1 so that a package placed in a release location and a peripheral region of the package fall within an angle of view of a camera. And then, the UAV 1 saves, as an image that proves delivery completion of the package, an image of the peripheral region including the package captured by the camera in a storage unit 15 of the UAV 1 or a storage unit 22 of the management server 2.

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.

Systems, tools and methods for deploying a mesh sensor network. The system comprises one or more aircraft configured to carry one or more drop pods into an environment and the deploying of the drop pods at points of interest. The aircraft and drop pods may comprise arrays of sensors for monitoring the areas that they are operating in. The aircraft and drop pods may include mesh radio communication devices and operate as nodes in the mesh network. The location at which each drop pod is to be deployed may be determined based on the type of sensors carried by the drop pod.