Provided in this disclosure is a system and methods for wind compensation of an electric aircraft. More specifically, provided in this disclosure is a controller of an aircraft configured to use a plant model for compensating for wind forces. The processor is configured to receive, from the sensor, at least a geographical datum of the electric aircraft.

Unmanned Aerial Vehicles also known as UAVs or Drones, either autonomous or remotely piloted, are classified as drones by the US Federal Aviation Administration (FAA) as weighing under 212 pounds. The system described herein details Autonomous Flight Vehicles (AFV) which weigh over 212 pounds but less than 1,320 pounds which may require either a new classification or a classification such as Sport Light Aircraft, but without the requirement of a pilot due to the safe autonomous flight system such as the Safe Temporal Vector Integration Engine or STeVIE. Safe Autonomous Light Aircraft (SALA) are useful as drone carriers, large scale air package or cargo transport, and even human transport depending on the total lift capability of the platform.

A system and method for autonomous flight control with mode selection an electric aircraft is illustrated. The system comprises an altitude-related sensor and a computing device. The altitude-related sensor is coupled to the electric aircraft and is configured to detect an altitude value. The computing device is communicatively connected to the altitude-related sensor and is configured to receive the altitude value from the altitude-related sensor, to determine a flight mode as a function of the altitude value and an altitude threshold, to determine an aircraft adjustment as a function of a determine flight mode, and to generate an autonomous function configured to enact the determined flight mode and an aircraft adjustment automatically.

Said control system comprises: a remote device comprising: a remote module for acquiring flight plan data, and a remote module for calculating a remote trajectory or a remote setpoint according to the flight plan data; an on-board device comprising: an on-board module for acquiring flight plan data, an on-board module for calculating an on-board trajectory or an on-board setpoint according to the data acquired by the on-board acquisition module.

The remote device comprises a module for validating the trajectory which is configured to: acquire the on-board and remote trajectory or setpoint; validate or reject the on-board trajectory or setpoint according to the remote trajectory or setpoint; transmit the result of the validation to the on-board device.

A method for securing a geographical area encompassing a route. A map of the area is obtained. A weapon is associated with a firing modeling consisting of a probability model of hitting its target when shooting, as a function of the firing distance. Positions of potential shelters of threats on the map are determined by using a trained artificial intelligence device. The modeling is applied for each weapon and potential shelter while relating the shots to the route and summing all the probabilities of hitting its target on each portion of the route. The potential shelters most likely to constitute attack threats along the route are determined. A path is defined in order to address these potential shelters most likely to constitute attack threats.

According to a technical aspect of the invention, there is provided a multiple unmanned aerial vehicles navigation optimization method is performed at a ground base station which operates in conjunction with unmanned aerial vehicles-base stations which are driven by a battery to move and cover a given trajectory point set, the multiple unmanned aerial vehicles navigation optimization method including: calculating an age-of-information metric by receiving an information update from the unmanned aerial vehicles-base stations through communication, when the ground base station is present within a transmission range of the unmanned aerial vehicles-base stations; setting conditions of a trajectory, energy efficiency, and age of information of each of the unmanned aerial vehicles-base stations; and executing Q-learning for finding a trajectory path policy of each of the unmanned aerial vehicles-base stations, so as to maximize total energy efficiency of an unmanned aerial vehicles-base station relay network to which the energy efficiency and the age of information are applied. According to the invention, the following effects are obtained. Age of information (AoI) that is a new matrix used to measure up-do-dateness of data is set, an edge computing environment for a remote cloud environment is provided by using the AoI, and a computing-oriented communications application can be executed by using the edge computing environment.

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.

A control method and device of a movable platform, a movable platform, and a storage medium are provided. The control method may include acquiring a control amount for controlling the movable platform; converting the control amount into control instruction of the movable platform based upon a position of the movable platform and a position of a target object photographed by the movable platform; and controlling the movable platform to move relative to the target object according to the control instruction.

The present invention provides a flying apparatus that can accurately measure a weight of a transported objected in a simple configuration. The flying apparatus 10 includes rotors 11, motors 12, a flight sensor 13, an electric power conversion unit 14, and a computation control unit 15. The flight sensor 13 measures physical quantities acting on a fuselage base portion 16. The computation control unit 15 generates instruction signals based on the physical quantities to cause the fuselage base portion 16 to be at a predetermined position in a predetermined attitude. The electric power conversion unit 14 adjusts amounts of electric power supplied to the motors 121 and the like based on the received instruction signals. Moreover, the computation control unit 15 calculates an estimated weight that is an estimation value of a weight of the transported object, based on magnitudes of the instruction signals.