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.

A method is provided for calculating the prediction of required navigation performance for a trajectory associated with a list of segments of a flight plan. A method is also provided for displaying the navigation performance as a corridor trajectory and adapted to guarantee compliance with the navigation performance requirements while offering immediate viewing of the navigation latitude in a corridor.

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.

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.

An aircraft control device and a remote controller aircraft are disclosed. The aircraft control device includes a first channel configured to receive first control information outputted by a remote controller and transmit the first control information to an aircraft; a second channel configured to receive second control information outputted by the remote controller and transmit the second control information to a camera and/or a gimbal; and a switch unit configured to switch that the first control information is received by the second channel and transmitted to the aircraft when the first channel is disturbed or a distance between the remote controller and the aircraft is larger than a distance threshold. The present invention is able to switch the transmission route of the first control information between the first channel and the second channel in accordance with situations of the first channel and the second channel.

According to the present invention there is provided a drone (1) comprising one or more propellers (2) and one or more actuators (3) for actuating said one or more propellers (2) to generating a thrust force which enables the drone (1) to fly; a controller (4) which is configured such that it can control the flight of the drone (1), wherein the controller (4) comprises a memory (6) having stored therein a plurality of predefined sets of positions which define a virtual rail which can be used to guide the flight of the drone (1) so that the drone can avoid collision with an subject; and wherein the controller further comprises a mathematical model (7) of the drone; wherein the controller (4) is configured to control the flight of the drone by performing at least the following steps, (a) approximating lag error based on the position of the drone (1) measured by a sensor (5) and the virtual rail, wherein the lag error is the distance between a point along the virtual rail which is closest to the drone (1) and an estimate of said point along the virtual rail which is closest to the drone (1); (b) approximating a contour error based on the position of the drone (1) as measured by a sensor (5) and the virtual rail, wherein the contour error is the distance between a point along the virtual rail which is closest to the drone (1) and the position of the drone (1); (c) defining a cost function which comprises at least said approximation of the lag error and said approximation of the contour error; (d) minimizing the defined cost function, while also respecting at least limitations of the drone which are defined in said mathematical model, to determine a plurality of control inputs over a predefined time period into the future, and (e) applying the first control input only to the one or more actuators (3). There is further provided a corresponding method for controlling the flight of a drone.

Example implementations relate to a method of dynamically updating a transport task of a UAV. The method includes receiving, at a transport-provider computing system, an item provider request for transportation of a plurality of packages from a loading location at a given future time. The method also includes assigning, by the transport-provider computing system, a respective transport task to each of a plurality of UAVs, where the respective transport task comprises an instruction to deploy to the loading location to pick up one or more of the plurality of packages. Further, the method includes identifying, by the transport-provider system, a first package while or after a first UAV picks up the first package. Yet further, the method includes based on the identifying of the first package, providing, by the transport-provider system, a task update to the first UAV to update the respective transport task of the first UAV.

An unmanned aerial vehicle (UAV) logs first UAV information at a first frequency. The UAV triggers a camera associated with the UAV to capture an image. In response to triggering the camera to capture the image, the UAV logs second UAV information at a second frequency that is higher than the first frequency. A device that is separate from the UAV identifies a location of the UAV corresponding to the image based on a capture timestamp of the image received from the camera, the first UAV information, and the second UAV information. The device generates a geo-rectified imagery based on the image and the location of the UAV.

A method for monitoring an unmanned aerial vehicle (UAV) includes a processor obtaining a datagram based on monitoring data for a UAV-detector communication between the UAV and one or more detectors. The monitoring data indicates at least one of a location of the UAV or a location of a control station in communication with the UAV. The method further includes determining a risk level by retrieving pre-stored risk information associated with the UAV based on the datagram.