The devices and methods described herein integrate security and/or recording mechanisms into unmanned vehicle interdiction devices to prevent unauthorized use and to record information related to operation of the unmanned vehicle interdiction device, identification of an unmanned vehicle, location of the unmanned vehicle, and operation of the unmanned vehicle.

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.

Techniques are disclosed for collaborative map construction using multiple vehicles. Such a system may include a ground vehicle including a first computing device and a first scanning sensor, and an aerial vehicle including a second computing device and a second scanning sensor. The ground vehicle can obtain a first real-time map based on first scanning data using the first scanning sensor, and transmit a first real-time map and position information to the aerial vehicle. The aerial vehicle can receive the first real-time map and the position information from the first computing device, obtain a second real-time map based on second scanning data collected using the second scanning sensor, and obtain a third real-time map based on the first real-time map and the second real-time map.

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.

According to one embodiment, a position and attitude estimation device includes a processor. The processor is configured to acquire time-series images continuously captured by a capture device installed on a mobile object, estimate first position and attitude of the mobile object based on the acquired time-series images, estimate a distance to a subject included in the acquired time-series images and correct the estimated first position and attitude to a second position and attitude based on an actual scale, based on the estimated distance.

System and method can support a servo system. The servo system comprises a motor with a rotor and a stator, wherein said rotor is arranged internally to said stator. Furthermore, said rotor, which is rotatable relative to said stator, can be configured to receive at least a portion of a functional module. Additionally, the servo system can be used for supporting a payload stabilization system, such as a gimbal system.

Embodiments include devices and methods for navigating an unmanned autonomous vehicle (UAV) based on a measured magnetic field vector and strength of a magnetic field emanated from a charging station. A processor of the UAV may navigate to the charging station using the magnetic field vector and strength. The processor may determine whether the UAV is substantially aligned with the charging station, and the processor may maneuver the UAV to approach the charging station using the magnetic field vector and strength in response to determining that the UAV is substantially aligned with the charging station. Maneuvering the UAV to approach the charging station using the magnetic field vector and strength may involve descending to a center of the charging station. The UAV may follow a specified route to and/or away from the charging station using the magnetic field vector and strength.

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.