An apparatus for use in harvesting airborne moisture includes an unmanned aerial vehicle (UAV), a woven mesh, supported by the UAV, for collecting liquid droplets upon contact with a fog bank, a camera, a processor, a non-transitory, tangible computer readable memory storing software instructions executable by the processor, a fog identification engine, and a flight controller on board the UAV. The fog identification engine is executable on the processor according to the software instructions and is configurable to capture a digital image via the camera, detect an absence of features in the digital image, and identify a fog bank as a function of the absence of features in the digital image. The flight controller directs the UAV to enter a fog bank identified by the fog identification engine.

The present invention relates to a drone for measuring odor concentration, characterized by a specific configuration which allows samples to be collected in locations which would otherwise be hard to access or inaccessible, and under conditions in which the drive means do not affect the measurement. Additionally, the invention is characterized by the correct marking of the spatial location of each sample collection and measurement, even if the sample collection and measurement requires a transport time of the suctioned air.

Apparatuses, systems, and methods for open path laser spectroscopy with mobile platforms. An example system may include a first mobile platform and a second mobile platform, each of which supports a payload. A light beam directed from one payload to another may define a measurement path, which may be at a particular height above the ground. The payloads may determine a gas concentration along the measurement path. Wind information at the measurement height may be used to determine a gas flux. One or both of the mobile platforms may then move to a new location, and take a measurement along a new measurement path. By combining the measurement paths, gas flux through a flux surface may be determined.

Aspects of the present disclosure relate generally to reduction in environmental pollution and, more particularly, to systems and method of carbon dioxide (CO.sub.2) sequestration in the atmosphere. For example, a computer-implemented method includes receiving, by a computing device, locations of an atmospheric pollutant; determining, by a computing device, a location of a target area of the atmospheric pollutant for sequestration; determining, by the computing device, positioning and flight path data for airborne sequestration devices to sequester the atmospheric pollutant at the location of the target area of the atmospheric pollutant; and deploying, by the computing device, the airborne sequestration devices with reagent according to the positioning and the flight path data to sequester the atmospheric pollutant at the location of the target area of the atmospheric pollutant.

A computer-implemented method includes receiving a first input associated with an incident location of an incident. A second input associated with a measurement zone surrounding the incident location is received. The method further includes producing, via a display monitor, a set of waypoints associated with a flight path of an unmanned aerial vehicle (UAV) based on the first input and the second input. The set of waypoints is displayed on a satellite aerial map including the incident location.

A computer-implemented method includes obtaining a worksite condition corresponding to a worksite operation of a mobile work machine, generating a machine performance metric representing operational performance of the mobile work machine for the worksite operation, generating a machine impact metric based on a comparison of the machine performance metric and a base performance metric, generating a correlation between the machine impact metric and the worksite condition, and generating a machine control signal based on the correlation.

Embodiments of a system, device and method improve weather modeling approaches for use by air vehicles to mitigate weather hazards and/or optimize air vehicle operations. Various embodiments collect a set of independent measurements of atmospheric parameters of interest, collect a set of onboard measurements from one or more non-traditional atmospheric sensors onboard an air vehicle, process the collected measurements to identify values of selected atmospheric parameters and generate a model for relating raw sensor outputs from the non-traditional atmospheric sensors to the atmospheric parameters of interest.

A sensor-enabled aerial vehicle carries spatial electric and magnetic field detection sensors and a processing system that employs algorithmic methods to process sensor data. The sensors effectively detect electric and magnetic fields that emanate from energized objects, and the algorithmic methodology analyzes the sensor data to distinguish whether the energized object is unintentionally energized. The aerial propulsion system is enclosed within two electrically conducting hemispheres that are electrically insulated from each other. Alternatively, the hemispheres may be suspended below the aerial vehicle. The hemispheres function as a large area electric-field collection surface, wherein a small alternating current (AC) developed between the two hemispheres is proportional to the collected AC electric fields.

Images captured by a camera system of an unmanned aerial vehicle (UAV) can be used to determine a weather condition in an environment of the UAV. The camera system of the UAV can capture one or more images, and a characteristic of at least one image of the one or more images can be determined from image data associated with the at least one image. A weather condition of the environment of the UAV can be determined based at least in part on the characteristic of the at least one image.

Sets of drones are deployed to create an ad-hoc 5G network in a physical environment to collect sensor data and generate a map of the physical environment in real time. Master drones configured with 5G capabilities are deployed to the physical area to create the 5G ad-hoc network, and swarm drones configured with sensors are deployed to gather environmental data on the physical environment. The gathered data is transmitted to the master drones to generate a map. The deployable 5G network is leveraged to identify precise locations for the swarm drones and each instance of sensor data collected by the swarm drones in order to create an accurate and detailed map of the environment. The map can include information regarding the structural layout of the space and environmental characteristics, such as temperature, the presence of smoke or other gases, etc.