Drone-based inventory management method and systems. One embodiment provides a drone-based inventory management system including one or more unmanned aerial vehicles (UAVs), and a central management system having an electronic processor, and a transceiver configured to communicate with the one or more UAVs. The electronic processor is configured to determine a discrepancy in inventory and select a UAV for verification. The electronic processor is also configured to determine whether weather permits UAV operation and operate the UAV in a pre-determined route when the weather permits UAV operation. The electronic processor is further configured to capture images using the UAV and determine new inventory based on captured images. The electronic processor is also configured to update inventory based on the new inventory.

Systems, computer readable medium and methods for autonomous drone navigation based on vision are disclosed. Example methods include capturing an image using an image capturing device of the autonomous drone, processing the image to identify an object, and navigating the autonomous drone relative to the object for a period of time. After the period of time a second type of navigation is used based on determining structure from motion navigation. Images are captured during the period of time to transition to the second type of navigation. The second type of navigation uses a downward pointing navigation camera and other sensors.

Embodiments for a terrain following (TF) radar configured for use in an airborne system are generally described herein. In some embodiments, a radar return comprising dual polarimetry radar data is processed to determine a Correlation Coefficient (CC), a Differential Reflectivity (ZDR), and a Specific Differential Phase (KDP). Discriminator logic is applied to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain. Further signal processing may be performed on the radar return when the radar return does not comprise solely rain. When the radar signal comprises solely rain, the radar return is tagged as a rain return. Applying the discriminator logic may include applying linear and/or quadratic functions to the CC, the ZDR and the KDP to determine whether the radar return comprises solely rain.

A system and method for reliably and efficiently monitoring and arbitrating the performance of one or more UTM infrastructure systems are provided herein. The method for monitoring and arbitrating a plurality of UTM infrastructure networks involves monitoring and arbitrating a plurality of unmanned traffic management (UTM) infrastructure networks comprising integrating a UTM arbitration system between the plurality of UTM infrastructure networks, wherein the UTM arbitration system is operably configured to simultaneously monitor the UTM infrastructure networks; monitoring information and/or data associated with one or more UTM systems associated with the UTM infrastructure networks; detecting the presence or absence thereof of one or more inconsistencies in the data and/or information associated with the one or more UTM systems; and initiating a reconciliation activity in response to detecting the presence of at least one inconsistency in the data and/or information associated with the one or more UTM systems.

An electric aircraft with flight trajectory planning. The electric aircraft includes a sensor. The sensor is coupled to the electric aircraft. The sensor is configured to detect a plurality of weather measurements. The electric aircraft includes a processor. The processor is communicatively connected to the sensor. The processor is configured to receive, from the sensor, a weather measurement of the plurality of weather measurements. The processor is configured to receive, from a user, a destination datum and a desired altitude datum. The processor is configured to determine an optimal trajectory of the electric aircraft as a function of the destination datum, weather datum, and altitude datum.

The present invention is a method and system for generating an area of interest for unmanned aerial vehicle (UAV) missions. Using radar and weather data, a mission area may be generated for flights which will maximize efficiency by pre-generating flight paths based on atmospheric and other data. The UAV may include artificial intelligence (AI) capabilities for processing imaging and other sensed data. Post-processing of the data may include additional AI training and processing.

Disclosed are methods, systems, and non-transitory computer-readable medium for controlling a vehicle. In one embodiment, a first route plan having a starting point and an ending point may be generated, wherein the first route plan is based on at least one parameter and wherein the first route plan is configured to cause the vehicle to generate an overpressure event over areas where it is undesirable to have the environmental impact of the overpressure event. An operator input may then be received to change at least one operating parameter of the vehicle, and a second route plan may be generated based, at least in part, on inputs. The first route plan and the second route plan may be displayed on a display. Upon receiving inputs to select a route plan displayed on the display, actuator instructions are generated to control the vehicle to follow the selected route plan.

A method includes separating a flight plan of a vehicle into a number of portions with each portion including a particular length that is determined based on a complexity of an environment where the flight plan takes place. The complexity of the environment is based on at least one of a set of factors including at least one of a terrain of the environment, one or more obstacles, one or more no-fly zones, or one or more no-landing zones within the environment. The method also includes determining an escape route for each portion of the flight plan. The escape route includes a route to a safe landing site in response to a failure of a system onboard the vehicle. The method additionally includes generating an escape route plan for the flight plan in response to all portions of the flight plan being assigned at least one escape route.

An electric aircraft with flight trajectory planning. The electric aircraft includes a sensor. The sensor is coupled to the electric aircraft. The sensor is configured to detect a plurality of weather measurements. The electric aircraft includes a processor. The processor is communicatively connected to the sensor. The processor is configured to receive, from the sensor, a weather measurement of the plurality of weather measurements. The processor is configured to receive, from a user, a destination datum and a desired altitude datum. The processor is configured to determine an optimal trajectory of the electric aircraft as a function of the destination datum, weather datum, and altitude datum.

Systems and methods are described herein for determining an optimized flight route for an aerial vehicle. In some examples, weather conditions for the aerial vehicle during a flight can be predicted based on weather data. At least two flight route segments based on the predicted weather data can be determined. The at least two flight route segments can include one of a solar flight route segment and a thermal flight route segment. A respective flight route segment of the at least two flight route segments can be discarded that can cause the aerial vehicle to violate a flight constraint. A replacement flight route segment for the respective discarded flight route segment can be determined. An optimized flight route for the aerial vehicle can be generated based on the replacement flight route segment and at least one remaining flight route segment of the at least two flight route segments.