An improved UAV system and methods for operation in an inventory management system. The methods include generating a three dimensional (3D) map and estimating a position and orientation of the UAV based upon this map; autonomously navigating the UAV in the environment by using the generated 3d map in conjunction with the position and the orientation of the UAV; performing static and dynamic obstacle avoidance in the environment using collision avoidance; and finding the optimal path from a source node to a destination node within the environment.

System and method for determining distance in navigation of an electric aircraft is illustrated. The system and method comprise a sensor and a computing device. The sensor is configured to detect a surface and transmit at least a first signal and a first frequency and at least a second signal at a second frequency to a computing device, wherein the first signal and the second signal comprise a corresponding distance. The computing device is configured to receive a returned signal from the sensor, wherein the returned signal comprises an intermodulation product associated to the first signal and the second signal, detect an amplitude of the returned signal as a function of the frequency, identify a distance datum as a function of the amplitude and an amplitude threshold, determine an aircraft adjustment as a function of the distance datum, and transmit the distance datum and aircraft adjustment to a remote device.

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.

A response system may be provided. The response system may include a security system and an autonomous drone. The security system includes a security sensor and a controller. The drone includes a processor, a memory in communication with the processor, and a drone sensor. The processor may be programmed to receive the deployment request from the security system, navigate to the one or more zones of the coverage area included in the deployment request, collect drone sensor data of the one or more zones of the coverage area using the at least one drone sensor, determine that an incident has occurred, and/or transmit the collected drone sensor data and incident verification to the security system, wherein, in response to receiving the collected drone sensor data and incident verification, the security system is configured to generate a command for responding to the incident.

In an embodiment, an optical sensor includes (i) a first lens array including a plurality of first lenses, (ii) a photodetector array including a plurality of photodetectors each aligned with a respective one of the plurality of first lenses, and (iii) a plurality of signal-modifying elements each aligned with a respective one of the plurality of first lenses. The plurality of signal-modifying elements includes (a) a first signal-modifying optical element having a first spatially-dependent transmission function, and (b) a second signal-modifying optical element having a second spatially-dependent transmission function differing from the first spatially-dependent transmission function.

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.

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.

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.

An imaging system includes: an unmanned flight vehicle; an imager that is mounted on the unmanned flight vehicle and takes an image of a robot which performs work with respect to a target object; a display structure which is located away from the unmanned flight vehicle and displays the image taken by the imager to a user who manipulates the robot; and circuitry which controls operations of the imager and the unmanned flight vehicle. The circuitry acquires operation related information that is information related to an operation of the robot. The circuitry moves the unmanned flight vehicle such that a position and direction of the imager are changed so as to correspond to the operation related information.