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: Obtaining location data in a novel way is considered to be a part of this invention. An exemplary method for doing so includes identifying first positioning information for a data delivery vehicle based at least in part on a plurality of positioning signals received at the data delivery vehicle from a set of satellites of a global navigation satellite system (GNSS). The example method comprises obtaining relative position information associated with a position vector from the data delivery vehicle to a remote vehicle, the remote vehicle being unable to receive the plurality of positioning signals from the set of satellites. The example method comprises determining second positioning information for the remote vehicle based at least in part on the first positioning information and the relative position information. The example method comprises transmitting the second positioning information to the remote vehicle.

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.

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.

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: A real-time crop processing management system is disclosed. In one embodiment, a yield description of a load of harvested crop and a location of the load of harvested crop is received at a computer system. An indication of the capacity to process the load of harvested crop by a receiving facility is also received by the computer system. A message conveying a delivery assignment for the load of harvested crop is then generated by the computer system.

Abstract: In embodiments, an application vehicle includes an application system for applying a product during an agricultural application. The application system (used in conjunction with the application vehicle) includes a priming module that determines a priming location. When the application vehicle is in proximity to the priming location, the priming module primes aspects of the application system so that it is ready to apply the product when the application vehicle reaches a region in which the product is to be applied.

Abstract: A real-time crop processing management system is disclosed. In one embodiment, a yield description of a load of harvested crop and a location of the load of harvested crop is received at a computer system. An indication of the capacity to process the load of harvested crop by a receiving facility is also received by the computer system. A message conveying a delivery assignment for the load of harvested crop is then generated by the computer system.

Abstract: A boom is attached to an application vehicle for applying a product during an agricultural application. The boom includes one or more sections that can be dynamically adjusted to satisfy one or more adjustment criteria. A boom controller communicates actuation commands to a boom adjustment system to dynamically adjust the shape of the boom.

Abstract: A boom is attached to an application vehicle for applying a product during an agricultural application. The boom includes one or more sections that can be dynamically adjusted to satisfy one or more adjustment criteria. A boom controller communicates actuation commands to a boom adjustment system to dynamically adjust the shape of the boom.

Abstract: In embodiments, an application vehicle includes an application system for applying a product during an agricultural application. The application system (used in conjunction with the application vehicle) includes a priming module that determines a priming location. When the application vehicle is in proximity to the priming location, the priming module primes aspects of the application system so that it is ready to apply the product when the application vehicle reaches a region in which the product is to be applied.

Abstract: System and method for providing guidance control for machines. In one embodiment, a method includes detecting an implement, by a guidance controller, coupled to a machine. The method may further include determining a characteristic of the implement which affects a control setting of the machine, configuring the control setting of the machine by the guidance controller based, at least in part, on the characteristic of the implement and controlling operation of the machine based on the control setting.

Abstract: System and method for configuring guidance controllers. In one embodiment, a method includes detecting an implement, by a guidance controller, coupled to a machine. The method may further include determining a characteristic of the implement which affects a calibration parameter of the guidance controller and configuring the calibration parameter of the guidance controller based, at least in part, on the characteristic of the implement. An operational path of the machine may be controlled based on the calibration parameter.

Abstract: System and method for providing guidance control for machines. In one embodiment, a method includes detecting an implement, by a guidance controller, coupled to a machine. The method may further include determining a characteristic of the implement which affects a control setting of the machine, configuring the control setting of the machine by the guidance controller based, at least in part, on the characteristic of the implement and controlling operation of the machine based on the control setting.

Abstract: System and method for location based configuration of guidance controllers for machines. In one embodiment, a method includes detecting an implement, by a guidance controller, coupled to a machine. The method may further include detecting location data for the machine by the guidance controller, determining a characteristic of the location which affects a calibration parameter of the guidance controller, configuring the calibration parameter of the guidance controller based, at least in part, on the characteristic of the location, and controlling operation of the machine and implement based on the calibration parameter.

Abstract: System and method for configuring guidance controllers. In one embodiment, a method includes detecting an implement, by a guidance controller, coupled to a machine. The method may further include determining a characteristic of the implement which affects a calibration parameter of the guidance controller and configuring the calibration parameter of the guidance controller based, at least in part, on the characteristic of the implement. An operational path of the machine may be controlled based on the calibration parameter.