Systems and methods are provided for adapting automotive mmW radar technology to meet the requirements of autonomous unmanned aerial vehicle (UAV) systems. Embodiments of the present disclosure provide solutions for several design challenges from this adaptation, such as utilizing a limited number of antenna channels to scan in both azimuth and elevation.

Provided is a detection and classification system and method for small unmanned aircraft systems (sUAS). The system and method detect and classify multiple simultaneous heterogeneous RC transmitters/sUAS downlinks from the RF signature using Object Detection Deep Convolutional Neural Networks (DCNNs). The method further utilizes not only passive RF, but may also utilize Electro Optic/Infrared (EO/IR), radar and acoustic sensors as well, with a fusion of the individual sensor classifications. Detection and classification with Identification Friend or Foe (IFF) of individual sUAS in a swarm, multi-modal approach for high confidence classification, decision, and implementation on a low C-SWaP (cost, size, weight and power) NVIDIA Jetson TX2 embedded AI platform is achieved.

The present invention provides a fruit harvesting, dilution and/or pruning system comprising: (a) a computerized system for mapping an orchard or a map of trees position and their contour in a plantation; (b) a management system for autonomous unmanned aircraft vehicle (UAV) fleet management for harvesting, diluting or pruning fruits; and a method for UAV autonomous harvesting, dilution and/or pruning of an orchard.

Autonomous unmanned vehicles (UVs) for responding to situations are described. Embodiments include UVs that launch upon detection of a situation, operate in the area of the situation, and collect and send information about the situation. The UVs may launch from a vehicle involved in the situation, a vehicle responding to the situation, or from a fixed station. In other embodiments, the UVs also provide communications relays to the situation and may facilitate access to the situation by responders. The UVs further may act as decoupled sensors for vehicles. In still other embodiments, the collected information may be used to recreate the situation as it happened.

Presently disclosed subject matter integrates a method of using thousands of semi-autonomous unmanned aerial vehicles, herein called drones, to deliver vastly superior amounts of fire retardant over substantially larger and variably-shaped drop patterns. Each drone is able to swap its own batteries with freshly charged batteries and each drone is able to refill its container with water or fire retardant. Once launched, a swarm of drones can perform repeated trips from the water/retardant source to the fire without human involvement other than the high-level tasking of where to drop the retardant. Once a general drop destination and drop pattern shape is designated, the swarm can transport retardant to that location, form itself into the desired drop shape, and deploy retardant. The drone body is designed to be modular so different components can be attached with ease and no special training or knowledge required.

An unmanned aerial vehicle (UAV) task cooperation method based on an overlapping coalition formation (OCF) game includes: constructing a sequential OCF game model for a UAV multi-task cooperation problem; using a bilateral mutual benefit transfer (BMBT) order that is biased toward the utility of a whole coalition to evaluate a preference of a UAV for a coalitional structure; optimizing task resource allocation of the UAV under an overlapping coalitional structure by using a preference gravity-guided Tabu Search algorithm to form a stable coalitional structure; and optimizing a transmission strategy based on the current coalitional structure, an updated status of a task resource allocation scheme of the UAV, and a current fading environment, so as to maximize task execution utility of a UAV network. The method quantifies characteristics of resource properties of the UAV and a task, and optimizes the task resource allocation of the UAV under the overlapping coalitional structure.

Autonomous unmanned aerial vehicle management service may provide a platform to manage groups of unmanned aerial vehicles to work together on a task simultaneously in an autonomous manner.

A modular flat-packable drone kit includes a plurality of components that can be assembled into a drone. Components of the drone kit include elements that may be cut from a flat sheet of material, thereby enabling low cost manufacturing and compact packaging and may be assembled without specialized tools. A set of drones may operate in a standalone mode or may be coupled together and operated in a group configuration.

A system and method for autonomously controlling a set of unmanned aerial vehicles is provided. The autonomous ground control system may include a communications module and a fleet configuration module in communication with one or more user interface applications. The autonomous ground control system may receive one or more flight commands and generate fleet configuration instructions and safety information. The autonomous ground control system may provide the fleet configuration instructions to each unmanned aerial vehicle in the set in order to carry out the fleet configuration instructions in real time.

An aircraft and method of operating an aircraft include a flight formation control unit configured to automatically transition the aircraft into an automatic flight formation mode in response to the aircraft being within one or both of a predetermined speed or a predetermined range in relation to at least one other aircraft flying within the automatic flight formation mode. The flight formation control unit can be further configured to automatically transition the aircraft out of the automatic flight formation mode in response to detection of a control signal received from one or more flight controls of the aircraft. The flight formation control unit can be further configured to automatically cycle the aircraft and the at least one other aircraft to different positions during the automatic flight formation mode.