Abstract: A method includes: establishing wireless connection between an unmanned aerial vehicle (UAV) and a user interface; generating, via the user interface, a flight path for the unmanned aerial vehicle; generating, via the user interface, a flight schedule for the unmanned aerial vehicle, the flight schedule being associated with the flight path and include one or more designated times; and initiating, via the user interface, autonomous operation of the unmanned aerial vehicle for the unmanned aerial vehicle to autonomously fly the flight path at the one or more designated times

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

Abstract: Described herein are systems for automated docking of an unmanned aerial vehicle. For example, some systems include an unmanned aerial vehicle including a propulsion mechanism, an image sensor, and processing apparatus; and a dock including a landing surface configured to hold the unmanned aerial vehicle and a fiducial on the landing surface, wherein the processing apparatus is configured to: control the propulsion mechanism to cause the unmanned aerial vehicle to fly to a first location in a vicinity of the dock; access one or more images captured using the image sensor; detect the fiducial in at least one of the one or more images; determine a pose of the fiducial based on the one or more images; and control, based on the pose of the fiducial, the propulsion mechanism to cause the unmanned aerial vehicle to land on the landing surface.

Abstract: Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

Abstract: A dock assembly includes a docking station and a stand or mount coupled to the docking station. The dock assembly may be configured for an unmanned aerial vehicle (UAV). The docking station may include a landing surface configured to interface with the UAV, an extended portion coupled to the landing surface and extending from the landing surface, and a fiducial located on the extended portion.

Abstract: A battery configured to power an unmanned aerial vehicle. The battery includes an enclosure configured to house a power module of the battery. The battery also includes one or more conducting contacts located on the enclosure configured to contact one or more pogo pins of a battery charger located on a docking station of the unmanned aerial vehicle.

Abstract: A computer accesses an input element storage and an output element storage. The computer accesses a symbolic expression for output element storage as a function of the input element storage. The computer computes, using a symbolic computation engine of the computer, a symbolic expression for the tangent space Jacobian of the output element storage with respect to an input tangent space. The computer outputs the computed expression.

Abstract: A computer accesses a first symbolic expression for an output matrix as a function of an input matrix at a computing device comprising processing circuitry and memory. The computer computes a first Jacobian of the input matrix with respect to an input tangent space. The computer computes a second Jacobian of the output matrix with respect to the input matrix. The computer computes a third Jacobian of an output tangent space with respect to the input matrix. The computer applies symbolic matrix multiplication to the first Jacobian, the second Jacobian, and the third Jacobian to obtain a second symbolic expression for the output tangent space with respect to the input tangent space. The computer provides a representation of the second symbolic expression, the second symbolic expression representing a computed tangent-space Jacobian.

Abstract: A computer of an unmanned aerial vehicle (UAV) accesses, from a memory unit, a problem definition comprising cost functions associated with travel of the UAV. The computer causes movement of the UAV based on the cost functions. The computer adjusts one or more of the cost functions during a flight of the UAV. The computer causes further movement of the UAV based on the adjusted one or more of the cost functions.

Abstract: A computer accesses a first symbolic expression for an output value as a function of an input value. The computer computes a first symbolic Jacobian of the input value with respect to an input tangent space from a symbolic Lie group definition. The computer computes a second symbolic Jacobian of the output value with respect to the input value. The computer computes a third symbolic Jacobian of an output tangent space with respect to the input value from the symbolic Lie group definition. The computer applies symbolic matrix multiplication to the first symbolic Jacobian, the second symbolic Jacobian, and the third symbolic Jacobian to obtain a second symbolic expression for the output tangent space with respect to the input tangent space. The computer provides a representation of the second symbolic expression.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may receive location information via the global navigation satellite system (GNSS) receiver and may receive acceleration information via an onboard accelerometer. The UAV may determine a first measurement of acceleration of the UAV in a navigation frame of reference based on information from the accelerometer prior to or during takeoff. In addition, the UAV may determine a second measurement of acceleration of the UAV in a world frame of reference based on the location information received via the GNSS receiver prior to or during takeoff. The UAV may determine a relative heading of the UAV based on the first and second acceleration measurements. The determined relative heading may be used for navigation of the UAV at least one of during or after takeoff of the UAV.

Abstract: Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

Abstract: In some examples, an unmanned aerial vehicle (UAV) may receive location information via the global navigation satellite system (GNSS) receiver and may receive acceleration information via an onboard accelerometer. The UAV may determine a first measurement of acceleration of the UAV in a navigation frame of reference based on information from the accelerometer prior to or during takeoff. In addition, the UAV may determine a second measurement of acceleration of the UAV in a world frame of reference based on the location information received via the GNSS receiver prior to or during takeoff. The UAV may determine a relative heading of the UAV based on the first and second acceleration measurements. The determined relative heading may be used for navigation of the UAV at least one of during or after takeoff of the UAV.

Abstract: Described herein are systems for automated docking of an unmanned aerial vehicle. For example, some systems include an unmanned aerial vehicle including a propulsion mechanism, an image sensor, and processing apparatus; and a dock including a landing surface configured to hold the unmanned aerial vehicle and a fiducial on the landing surface, wherein the processing apparatus is configured to: control the propulsion mechanism to cause the unmanned aerial vehicle to fly to a first location in a vicinity of the dock; access one or more images captured using the image sensor; detect the fiducial in at least one of the one or more images; determine a pose of the fiducial based on the one or more images; and control, based on the pose of the fiducial, the propulsion mechanism to cause the unmanned aerial vehicle to land on the landing surface.

Abstract: The present subject matter relates to systems, devices, and methods for data-driven precision agriculture through close-range remote sensing with a versatile imaging system. This imaging system can be deployed onboard low-flying unmanned aerial vehicles (UAVs) and/or carried by human scouts. Additionally, the present technology stack can include methods for extracting actionable intelligence from the rich datasets acquired by the imaging system, as well as visualization techniques for efficient analysis of the derived data products. In this way, the present systems and methods can help specialty crop growers reduce costs, save resources, and optimize crop yield.

Abstract: The present subject matter relates to systems, devices, and methods for data-driven precision agriculture through close-range remote sensing with a versatile imaging system. This imaging system can be deployed onboard low-flying unmanned aerial vehicles (UAVs) and/or carried by human scouts. Additionally, the present technology stack can include methods for extracting actionable intelligence from the rich datasets acquired by the imaging system, as well as visualization techniques for efficient analysis of the derived data products. In this way, the present systems and methods can help specialty crop growers reduce costs, save resources, and optimize crop yield.