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.

An aerial vehicle system for mapping an object buried in a subterranean surface, the aerial vehicle system including an aerial vehicle, an electronic sensor, a processor, and a memory. The memory includes instructions, which when executed by the processor, cause the system to receive a first input data set by the electronic sensor, the first input data set based on an electromagnetic signal and geographic location data, generate a raw image based on the first input data set, and compare the raw image to a calibration data set, the calibration data set based on material calibration data. The material calibration data is based on unique spectral reflection patterns of an object in a controlled environment at predefined heights and subterranean conditions.

Systems and techniques are provided for charging devices at a property using battery-charging drones. In some implementations, a monitoring system is configured to monitor a property and includes a battery-powered sensor configured to generate sensor data. The system includes a drone that is configured to navigate the property and charge the battery-powered sensor. A monitor control unit is configured to obtain a battery level from the battery-powered sensor and compare the battery level to a battery level threshold. Based on the comparison, the monitor control unit determines that the battery level does not satisfy the threshold. Based on the determination, the monitor control unit generates and transmits an instruction to a drone for the drone to navigate to the battery-powered sensor and charge a battery of the battery-powered sensor. The monitor control unit receives data from the drone that indicates whether the drone charged the battery of the sensor.

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.

Systems and methods are described for using data collected by unmanned aerial vehicles (UAVs) to generate insurance claim estimates that an insured individual may quickly review, approve, or modify. When an insurance-related event occurs, such as a vehicle collision, crash, or disaster, one or more UAVs are dispatched to the scene of the event to collect various data, including data related to vehicle or real property (insured asset) damage. With the insured's permission or consent, the data collected by the UAVs may then be analyzed to generate an estimated insurance claim for the insured. The estimated insurance claim may be sent to the insured individual, such as to their mobile device via wireless communication or data transmission, for subsequent review and approval. As a result, insurance claim handling and/or the online customer experience may be enhanced.

Disclosed herein is a system including a computer programmed to actuate a plurality of drones to first establish one or more electrical connections therebetween and then to provide a jump start to a vehicle.

Systems, devices, and methods for an unmanned aerial vehicle (UAV); a trace gas sensor disposed on the UAV, where the gas sensor is configured to measure a gas point concentration; a wind sensor, where the wind sensor is configured to determine a discrete wind vector corresponding to the gas point concentration measurement; and where the discrete wind vector and gas point concentration measurement are acquired substantially concurrently and co-locally.

UAV airways system generally are disclosed. Such UAV airway systems may comprise UAV cargo transportation systems and UAV surveillance and monitoring systems. Such systems preferably overlay and are commensurate with a system of high-voltage power transmission lines of high-voltage transmission system, and electric field actuated (EFA) generators preferably are utilized in UAVs that travel along the transmission lines, in UAV charging stations located along the transmission lines, or in both. Each EFA generator represents a power supply and comprises first and second electrodes separated and electrically insulated from each other for enabling a differential in voltage at the first and second electrodes resulting from a differential in electric field strength experienced by the first and second electrodes arising from the power transmission lines of the high-voltage transmission system.

We describe an aircraft design, which is capable of vertical takeoff and landing and also high-speed cruise on a fixed wing. The aircraft comprises a fuselage with a probe-deployment mechanism, which deploys a sample-gathering probe, located at a front end of the fuselage. A main wing is coupled to a middle section of the fuselage, wherein a right motor and right propeller are coupled to a right side of the main wing, and a left motor and left propeller are coupled to a left side of the main wing. The right and left propellers are angled with respect to the fuselage enabling the aircraft to pitch up to a vertical-takeoff mode and pitch down a horizontal-cruising mode. A pitch motor and pitch propeller are located at the rear end of the fuselage, wherein the pitch propeller is angled to provide substantially vertical thrust to control a pitch of the fuselage.

The present disclosure relates to a dataflow optimization method for low-power operation of a multicore system, the dataflow optimization method including: a step (a) of creating an FSM including a plurality of system states in consideration of dynamic factors that trigger a transition in system states for original dataflow; and a step (b) of optimizing the original dataflow through optimization of the created FSM.