Systems and methods for providing trajectory amendments are provided. In one embodiment, a computing system can identify a plurality of aircraft operators. The system can provide for display in a user interface, to one or more computing devices of each of the plurality of aircraft operators, a first set of data identifying an arrival time slot associated with a landing area. The system can receive one or more second sets of data indicating that one or more of the aircraft operators has selected the arrival time slot. The system can select a first aircraft operator of the one or more aircraft operators for the arrival time slot. The system can provide, to a computing device of the first aircraft operator, an output indicating that the first operator has been selected for the arrival time slot.

A method of determining a flight path for an aerial vehicle, includes controlling the aerial vehicle to fly along a first route, identifying, during the flight along the first route and with aid of one or more processors, a change in a state of signal transmission occurring at a first location, in response to identifying the change, determining, by the one or more processors, a second location different from the first location, determining a second route to the second location, and controlling, by the one or more processors, the aerial vehicle to fly to and land at the second location. The change of the state of signal transmission indicates an abnormal state in a signal transmission between the aerial vehicle and a control device.

A flight processor that calculates a 4-dimensional trajectory having a sequence of two or more position, time and cost (x, y, z, t, c) tuples that minimize a defined cost. Some embodiments generate cost-optimized trajectories with simple or complex constraints and bounds such as fixed AGL altitude; minimum AGL altitude; maximum AGL altitude; minimum MSL; maximum MSL; avoidance of restricted airspace; adherence to non-restricted airspace such as easements; adherence to ground-based guideways, if applicable; and the constraint to maintain adequate radio frequency signal-to-noise needed for communications to the ground station or backhaul systems. Constraint-enabled minimization of trajectory cost may leverage the aircraft's energy model; current atmospheric data (most notably wind vector data along the trajectory path); continuous-time and/or event-based risk models and fault trees; blacklisted and white-listed geo-fence boundaries; defined easements; and known or estimated RF signal-to-noise (SNR) minimum values needed for one or two-way communications.

An unmanned aerial vehicle riding route processing method, apparatus and device, and a readable storage medium, the method includes: determining candidate ride vehicles according to an autonomous flight route of an unmanned aerial vehicle from a flight start point to a flight destination; determining a riding flight route of the unmanned aerial vehicle according to current locations of the candidate ride vehicles; and controlling the unmanned aerial vehicle to ride at least one of the candidate ride vehicles to travel from the flight start point to the flight end point according to the riding flight route.

A method of harvesting and managing energy from air currents, by small unmanned aircraft systems (UAS) having a plurality of powered and unpowered rotors, to increase the aircraft's flight time, especially where the mission requires extensive hovering and loitering, is provided. Conventional powered rotors create lift for the aircraft. Unpowered rotors can either be: 1) Free-wheeling rotors which increase the plan form area of aircraft as they rotate, increasing lift, and reducing the power draw on the battery, and/or 2) Rotors connected to micro-generators, which serve as a brake on the unpowered rotors, create electrical power to charge the aircraft batteries or directly power the aircraft's electronics. The invention's folding rotor arm design results in a compact package that is easily transported by a single user (man portable). The aircraft can be removed from its protective tube, unfolded and launched for flight in less than a minute. Extended flight times, compact easily transported design, and ability to host flight software on a user's tablet/PC result in low total cost of ownership.

Provided is a flight path calculating method for high altitude long endurance of an unmanned aerial vehicle based on regenerative fuel cells and solar cells according to an exemplary embodiment of the present invention may include a modeling step, a simulation step, and an analyzing step, and may be configured in a program form executed by an arithmetic processing means including a computer. a flight path searching method and a flight path searching apparatus for performing continuous flight path re-searching on the basis of information measured in real time during a flight of the unmanned aerial vehicle in the stratosphere to change a flight path so that the unmanned aerial vehicle may permanently perform long endurance in the stratosphere is provided.

A method for calculating vertical trajectory values is provided. The method obtains aircraft performance data for a particular aircraft and atmospheric condition data associated with an air route; calculates a cost-efficient vertical trajectory for the air route, based on the aircraft performance data and the atmospheric condition data, wherein the cost-efficient vertical trajectory comprises a plurality of altitude values for minimizing cost for the particular aircraft during travel of the air route; obtains an altitude consistency threshold; and adjusts the cost-efficient vertical trajectory using the altitude consistency parameter, to create an optimized vertical trajectory for the aircraft route.

Power management method and system for an unmanned air vehicle, wherein the unmanned air vehicle comprises a plurality of power demanding subsystems and a plurality of power sources. The invention establishes mission oriented fixed parameters. A fuzzy logic power management unit, comprised in the system, automatically calculates and assigns priorities for delivering power to the subsystems. It also automatically calculates and assigns amounts of power delivered to each subsystem and automatically decides which of the power sources to deliver power to which subsystem. The fuzzy logic power management system calculates and assigns the priorities and loads in function of a plurality of internal variables, external variables and the mission oriented fixed parameters.

In examples, a fleet of vehicles comprises a hub vehicle and a set of remote vehicles, where a hub vehicle aggregates and disseminates information among remote vehicles. For example, the hub vehicle may specify a geofence and/or any of a variety of other vehicle configuration information, which may be provided to remote vehicles accordingly. Further, a remote vehicle may monitor a power source state and ensure that it does not travel outside of a range to the hub vehicle, thereby reducing instances where the remote vehicle has traveled too far to be replenished. The hub vehicle may also facilitate cross-vehicle communication between operators. For example, communications may be relayed among other vehicles of the fleet. In other examples, the hub vehicle may provide task management functionality, where a shared task list is synchronized among the vehicles, thereby enabling operators to view tasks, add tasks, and remove tasks, among other functionality.

Provided are a flight simulation and control method of a unmanned aerial vehicle with regenerative fuel cells and solar cells for high altitude long endurance, and a control apparatus thereof. The high altitude long endurance simulation method for an unmanned aerial vehicle based on regenerative fuel cells and solar cells includes: a variable inputting step of inputting design variables of the unmanned aerial vehicle based on regenerative fuel cells and solar cells; a modeling step of performing modeling of the unmanned aerial vehicle based on regenerative fuel cells and solar cells using the design variables input in the variable inputting step; and an analyzing step of analyzing a modeling result in the modeling step to perform a high altitude long endurance simulation while controlling any one of the design variables input in the variable inputting step.