Abstract: The present invention is for an autonomous aerial vehicle that enables near real-time computation of harvest yield data. Generally, the autonomous aerial vehicle receives combine harvest data from a harvesting vehicle, generates high-resolution yield data based on sensor suite that is on-board the autonomous vehicle, obtains edge compute data from an edge computing device at the edge of the network, and segments the received combine harvest data, the generated high-resolution yield data, and the obtained edge compute data. The aerial vehicle applies data normalization models to the segmented data and computes a normalized harvest yield for at least a portion a land tract. In this manner, the data delivery vehicles computes normalized data that otherwise can by noisy and unreliable.

Abstract: The present invention is for an autonomous aerial vehicle that enables near real-time and offline data processing among heterogenous devices that are in unreliable or unconnected network service areas, wherein the heterogenous devices are associated with heavy industrial systems. The autonomous aerial vehicle may obtain data from a first physical asset, and segment the obtained data as suitable for a local area compute node and/or a cloud compute node. The autonomous aerial vehicle may identify a location associated with the one or more destination devices and may compute a flight path to the destination location. The aerial device may thereafter travel to the destination location and upload relevant data to the at least one destination upon arrival.

Abstract: Disclosed herein are systems and methods for enabling at least one autonomous device to traverse a three-dimensional space. An example system may comprise a first autonomous vehicle comprising a first sensor suite. The first autonomous vehicle may traverse the three-dimensional space in accordance with a path planned vector. The path planned vector may be dynamically updated based on a first vector associated with a first object identified based on data received from the first sensor suite. The example system may comprise a first aerial vehicle comprising a second sensor suite. The first aerial vehicle may traverse the three-dimensional space based on at least one of data provided by the second sensor suite, the first sensor suite, and other aerial vehicles. The path planned vector may be dynamically updated based on data received from at least one of the first sensor suite, the second sensor suite, and cloud interface data.

Abstract: The present invention is for an autonomous aerial vehicle that enables near real-time computation of harvest yield data. Generally, the autonomous aerial vehicle receives combine harvest data from a harvesting vehicle, generates high-resolution yield data based on sensor suite that is on-board the autonomous vehicle, obtains edge compute data from an edge computing device at the edge of the network, and segments the received combine harvest data, the generated high-resolution yield data, and the obtained edge compute data. The aerial vehicle applies data normalization models to the segmented data and computes a normalized harvest yield for at least a portion a land tract. In this manner, the data delivery vehicles computes normalized data that otherwise can by noisy and unreliable.