Abstract: The technology relates to real-time state estimation and sensor management. A method for real-time state estimation and sensor management may include receiving telemetry from a fleet of aerial vehicles, storing telemetry in a telemetry buffer, generating a real-time state estimate of an aerial vehicle in the fleet using a group of estimators, the estimators being of one or more types, such as a sensor management estimator, a bias and noise management estimator, and a physical modeling estimator, and providing the real-time state estimate of the aerial vehicle to a job in a fleet management system.

Abstract: The technology relates to health and lifetime estimation for a lighter-than-air (LTA) vehicle. An LTA vehicle health and lifetime estimation system may include a processor and a memory storing instructions executable by the processor to cause the processor to implement an estimation service for determining a remaining lifetime output and a simulator for simulating a terminal event based on the remaining lifetime output. The estimation service may include a thermal model configured to determine a gas temperature, a gas and air estimator configured to estimate a gas amount and an air amount remaining in a balloon of the LTA vehicle, a leak rate estimator configured to estimate a leak rate, and a zero pressure estimator configured to determine the remaining lifetime output based on the leak rate. The system also may include an air flow estimator configured to determine an air mass flow rate based on the air amount.

Abstract: The technology relates to health and lifetime estimation for a lighter-than-air vehicle. A method for vehicle health and lifetime estimation may include receiving flight data inputs associated with a vehicle, determining a gas temperature based on the flight data inputs, estimating a gas amount remaining in a balloon envelope of the vehicle, estimating a gas leak rate based on the gas amount, estimating a component lifetime representing an estimated lifetime of a component of the vehicle, and determining a remaining lifetime output based on one or both of the gas leak rate or the component lifetime, the remaining lifetime output indicating a remaining lifetime estimate for the vehicle. The method also may include simulating a terminal event and causing the vehicle to take an action based on the remaining lifetime estimate.

Abstract: The technology relates to health and lifetime estimation for a lighter-than-air vehicle. A method for vehicle health and lifetime estimation may include receiving flight data inputs associated with a vehicle, determining a gas temperature based on the flight data inputs, estimating a gas amount remaining in a balloon envelope of the vehicle, estimating a gas leak rate based on the gas amount, and determining a remaining lifetime output based on the gas leak rate, the remaining lifetime output indicating a remaining lifetime estimate for the vehicle. The method also may include simulating a terminal event and causing the vehicle to take an action based on the remaining lifetime estimate.

Abstract: The technology described here relates to LTA vehicle launch configuration and in-flight optimization. A method for automated ballast dropping by an LTA vehicle in flight may include receiving, by an in-flight ballast model, an initial lift gas fill amount and an initial ballast amount, generating altitude ranges based on a remaining lift gas amount and a current system mass of the LTA vehicle to determine whether the remaining lift gas amount is within a ballast drop lift gas range, determining whether a convergence criterion for the remaining lift gas amount is met, the convergence criterion indicating a convergence between a remaining lift gas estimate and a remaining lift gas model, determining that dropping a ballast increment will not decrease an overall ballast amount below a target ballast amount corresponding to the remaining lift gas amount, and causing the LTA vehicle to drop the ballast increment.

Abstract: The technology described here relates to LTA vehicle launch configuration and in-flight optimization. A method for optimizing for an objective of an LTA vehicle launch may include receiving a desired objective, receiving known parameters of the LTA vehicle, including a pressure threshold, performing probabilistic calculations based on the desired objective and the known parameters, the probabilistic calculations configured to model setup parameters and to output probabilities for the setup parameters, the output indicating probabilities that a simulated vehicles would achieve the desired objective. The method also includes selecting a setup parameter value based on a high probability indicated in the output. Also described is an LTA vehicle launch configuration system implementing a thermal model, a physics model, and a fill and ballast tool, including an altitude range estimator, a gas-air estimator, and a pre-flight ballast model.

Abstract: Methods and systems for controlling a group of aerial vehicles to meet a connectivity service objective are provided. A method may include causing aerial vehicles in the group to arrive at a target service area during a given arrival time window associated with the connectivity service objective, which may indicate a desired probability of service coverage of the target service area. The method further includes calculating a probability of service coverage of the target service area for the vehicles for a time period after the vehicles are expected to arrive at the target service area, determining whether the probability of service coverage meets a threshold, and causing the vehicles to operate according to a station seeking flight policy during the time period.

Abstract: The technology relates to recovery and test dispatchers for fleet management and flight planning systems. A method for dispatching to a decommissioned vehicle zone includes receiving a first input comprising state data of an aerial vehicle and a second input comprising an objective; determining, using the first input, the aerial vehicle meets a set of criteria associated with the objective; selecting a dispatcher configured to dispatch a vehicle to a decommissioned vehicle zone; allocating the aerial vehicle to the dispatcher; and selecting, by the dispatcher, the decommissioned vehicle zone to which the aerial vehicle is to be dispatched, wherein the objective comprises a test objective or recovery objective, and wherein the dispatcher comprises a test dispatcher or recovery dispatcher.

Abstract: The technology relates to service dispatchers for fleet management and flight planning systems. A method for dispatching vehicles for service includes receiving, by a service dispatcher, a first input comprising a plurality of state data of a fleet of aerial vehicles and a second input comprising a service objective; determining, using the first input, that one or more aerial vehicles in the fleet meets a set of criteria associated with performing the service objective; submitting a bid on the one or more aerial vehicles, the bid configured to induce a vehicle allocator to allocate the one or more aerial vehicles to the service dispatcher; receiving an allocation of some or all of the one or more aerial vehicles; generating a plan to use the allocation to perform the service objective; and dispatching the allocation according to the plan.

Abstract: Methods and systems for controlling a group of aerial vehicles to meet a connectivity service objective are provided. A method may include causing aerial vehicles in the group to arrive at a target service area during a given arrival time window associated with the connectivity service objective, which may indicate a desired probability of service coverage of the target service area. The method further includes calculating a probability of service coverage of the target service area for the vehicles for a time period after the vehicles are expected to arrive at the target service area, determining whether the probability of service coverage meets a threshold, and causing the vehicles to operate according to a station seeking flight policy during the time period.

Abstract: The technology relates to controllers for lighter-than-air vehicles using deep reinforcement learning. A method for generating an objective-directed controller for an aerial vehicle includes defining an action space for an objective-directed controller, providing a set of feature vectors and the action space as input to a simulation module, training, by a learning module, the objective-directed controller according to a reward function correlated with the desired objective, evaluating the trained objective-directed controller, and storing high performing trained objective-directed controller.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving prevailing wind pattern data regarding winds at the location and the altitude of the aerial vehicle, selecting a heading for the aerial vehicle based on the prevailing wind pattern data, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the selected heading.

Abstract: The technology relates to risk assessments associated with a life cycle of one or more airborne objects. For instance, this may comprise running Monte Carlo simulations using operational parameters of the airborne object for a predetermined time period and on a plurality of processing devices distributed on a network and over ranges of values for the operational parameters. A risk threshold is then calculated for at least one of the Monte Carlo simulations and used in adjusting a flight component of the airborne object.

Abstract: The technology relates to generating current and future estimated weather models for predicting current and future estimated weather data. This may include indexing observations including weather data on a first grid based on locations of the observations. The first grid may have a plurality of cells each representing a volume of space for an area of the earth. A second cell of a second grid having a plurality of second cells each representing a volume of space for an area of the earth may be identified. The dimensions of the first cell may be increased. A set of indexed observations may be identified by selecting ones of the set of indexed observations that are indexed to any of the plurality of first cells having geographic areas that at least partially overlap with the increased dimensions. The set of indexed observations may be used to train a model.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving data indicating a destination of the aerial vehicle, receiving prevailing wind pattern data regarding winds at the location and altitude of the aerial vehicle, determining that the aerial vehicle is within a predetermined distance of the destination, determining a speed at which the aerial vehicle is moving, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the prevailing wind pattern data and the determined speed.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving data indicating a destination of the aerial vehicle, determining a vector from the location of the aerial vehicle to the destination of the aerial vehicle, receiving prevailing wind pattern data regarding winds at the location and altitude of the aerial vehicle, planning a path for the aerial vehicle to move along the vector based on the prevailing wind pattern data, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the prevailing wind pattern data and the planned path.

Abstract: An example system includes a battery, a controllable heating element arranged to heat the battery, a load coupled to the battery, and a controller configured to: determine a current temperature of the battery; determine a SOC of the battery; based on the current temperature of the battery, select a setpoint profile from a plurality of setpoint profiles for the battery, where each of the plurality of setpoint profiles has a temperature association, and where each of the plurality of profiles comprises one or more temperature setpoints; based on a comparison of the current SOC and a SOC of the selected setpoint profile, determine a current temperature setpoint from the one or more temperature setpoints of the selected setpoint profile; and in response to determining the current temperature setpoint of the battery, cause the current temperature of the battery to adjust according to the current temperature setpoint of the battery.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving data indicating a destination of the aerial vehicle, determining a vector from the location of the aerial vehicle to the destination of the aerial vehicle, receiving prevailing wind pattern data regarding winds at the location and altitude of the aerial vehicle, planning a path for the aerial vehicle to move along the vector based on the prevailing wind pattern data, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the prevailing wind pattern data and the planned path.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving data indicating a destination of the aerial vehicle, receiving prevailing wind pattern data regarding winds at the location and altitude of the aerial vehicle, determining that the aerial vehicle is within a predetermined distance of the destination, determining a speed at which the aerial vehicle is moving, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the prevailing wind pattern data and the determined speed.

Abstract: Disclosed are systems, devices, and methods for controlling an aerial vehicle. An exemplary method may include receiving data indicating a location and an altitude of the aerial vehicle, receiving data indicating an objective of the aerial vehicle, receiving prevailing wind pattern data, selecting a heading for the aerial vehicle based on the location, the altitude, the objective, and the prevailing wind pattern data, and causing the aerial vehicle to adjust the altitude of the aerial vehicle based on the selected heading.