A communication optimization system/method for mobile networks uses a server that generates waypoints based on a first communication network within a route to be travelled by an aerial vehicle, the aerial vehicle comprising a communication hub configured to communicate with at least one communication node, a communication hub controller configured control movement of a steerable antenna, and an aerial vehicle controller configured control movement of the aerial vehicle. The server then transmits the waypoints to the aerial vehicle controller; periodically monitors networks not connected to the communication hub; when a second communication network not connected to the communication hub satisfies a threshold, transmits causes the communication controller to steer the steerable antenna in a direction of the second communication network, further causing the communication hub to communicate and connect with the second communication network.

An unmanned aerial vehicle comprising at least one rotor motor. The rotor motor is powered by a micro hybrid generation system. The micro hybrid generator system comprises a rechargeable battery configured to provide power to the at least one rotor motor, a small engine configured to generate mechanical power, a generator motor coupled to the small engine and configured to generate AC power using the mechanical power generated by the small engine, a bridge rectifier configured to convert the AC power generated by the generator motor to DC power and provide the DC power to either or both the rechargeable battery and the at least one rotor motor, and an electronic control unit configured to control a throttle of the small engine based, at least in part, on a power demand of at least one load, the at least one load including the at least one rotor motor.

Example navigation aids for increasing the accuracy of a navigation system are disclosed herein. An example method disclosed herein identifying, with an aircraft intent description language (AIDL) aid, an AIDL instruction as associated with a first dynamic activity level of a plurality of dynamic activity levels and determining, with the AIDL aid, an aircraft state to be affected by the AIDL instruction. The example method also includes changing, with a navigation filter, a weighting scheme for a measurement of the aircraft state obtained by an inertial navigation system (INS) of the aircraft and estimating, with the navigation filter, a trajectory of the aircraft based on the weighting scheme and the measurement.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

A test flight system includes a test aircraft, and an atmospheric conditions-detecting vehicle that is separate and distinct from the test aircraft. The atmospheric conditions-detecting vehicle is configured to be deployed during a test flight of the test aircraft to detect atmospheric conditions of an environment in which the test aircraft operates during the test flight.

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.

Embodiments are provided for controlling a fleet of vehicles and drones. The vehicles are directed to respectively drop off passenger groups at multiple locations. Routes are calculated routes for the vehicles to respectively pick up the passenger groups from the multiple locations based on predicted pick-up times, passenger group sizes and available vehicle capacities. One or more assign drones are assigned to each passenger group at each location. Each drone is configured to broadcast a current location of the passenger group in the location and a corresponding one of the predicted pick-up times and delay the corresponding passenger group in the location based on one of the vehicles assigned as a pick-up vehicle for the passenger group being delayed.

Methods and systems described herein relate to power generation control for an aerial vehicle. An example method may involve determining an asynchronous flight pattern for two or more aerial vehicles, where the asynchronous flight pattern includes a respective flight path for each of the two or more aerial vehicles; and operating each of the aerial vehicles in a crosswind flight substantially along its respective flight path, where each aerial vehicle generates electrical power over time in a periodic profile, and where the power profile of each aerial vehicle is out of phase with respect to the power profile generated by each of the other aerial vehicles.

Systems and methods are provided for imposing physical actions, by endpoints, based on activities by users. One such method includes imposing a physical actions via a drone, by an endpoint associated with the drone, based on an activity undertaken by a user. The method includes receiving, by a computing device, an activity message including data indicative of an activity of a user and retrieving at least on rule from a data structure based on the data indicative of the activity where the at least one rule includes a physical action for said activity of the user. The method then includes identifying, by the computing device, the physical action from the at least one rule and transmitting, by the computing device, an order for the physical action to an endpoint, whereby the endpoint commands a drone to perform the physical action.

Systems and methods for automatically identifying and ascertaining an estimated amount of damage at a location by utilizing one or more autonomous vehicles, e.g., “drone” devices, to autonomously capture data of the location and utilizing Artificial Intelligence (AI) logic modules to analyze the captured data and construct a 3-D model of the location.