Abstract: A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance.

Abstract: A system includes a first dataset of vehicle trajectories including individual vehicle tracks forming a first track density, a desired vehicle track density profile, a spatio-temporal coverage metric, and a track model including Heaviside functions encoding track time origins and durations for the individual vehicle tracks and including locations for the plurality of individual vehicle tracks. Approximations of the Heaviside functions are minimized as a function of the spatio-temporal coverage metric. The first dataset of vehicle trajectories is manipulated to satisfy the desired vehicle track density profile through minimizing an approximation of the Heaviside functions. Each individual vehicle track in the first dataset of vehicle trajectories is shifted as a result of the optimization, thereby generating a second dataset of vehicle trajectories having a second track density.

Abstract: A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance.

Abstract: A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance.

Abstract: Systems and methods for the configuration of service policies for aircraft fleets and gas turbine engines are disclosed. One or more engine performance profiles may be generated based on historical engine performance data. The historical engine performance data may be retrieved based on one or more engine service parameters. A fleet performance profile may be generated based on the engine performance profiles. The engine service parameters may be varied to generate a configured engine performance profile. A configured fleet performance profile may be generated based on the configured engine performance profile.

Abstract: The present disclosure provides a flexibly positioned power drive unit (“PDU) system. The flexibly positioned PDU system may comprise a flexibly positioned PDU comprising a PDU hinged portion and a track coupled to the flexibly positioned PDU, wherein the flexibly positioned PDU is configured to move along a cargo deck and the PDU hinged portion is configured to move a cargo unit from a first location to a second location via the track.

Abstract: An aspect includes space partitioning for vehicle motion planning. A plurality of obstacle data is analyzed to determine a plurality of obstacle locations in a configuration space of a vehicle. A partitioning of the configuration space is performed to compute a skeletal partition representing a plurality of obstacle boundaries based on the obstacle locations. The skeletal partition is used to preferentially place a plurality of samples by a sampling-based motion planner. At least one obstacle-free path is output by the sampling-based motion planner based on the samples.

Abstract: Systems and methods for the configuration of service policies for aircraft fleets and gas turbine engines are disclosed. One or more engine performance profiles may be generated based on historical engine performance data. The historical engine performance data may be retrieved based on one or more engine service parameters. A fleet performance profile may be generated based on the engine performance profiles. The engine service parameters may be varied to generate a configured engine performance profile. A configured fleet performance profile may be generated based on the configured engine performance profile.

Abstract: A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance.

Abstract: An aspect includes space partitioning for vehicle motion planning. A plurality of obstacle data is analyzed to determine a plurality of obstacle locations in a configuration space of a vehicle. A partitioning of the configuration space is performed to compute a skeletal partition representing a plurality of obstacle boundaries based on the obstacle locations. The skeletal partition is used to preferentially place a plurality of samples by a sampling-based motion planner. At least one obstacle-free path is output by the sampling-based motion planner based on the samples.

Abstract: A method of operating an obstacle data model construction system of an aircraft is provided. With a vehicle moving through a vehicle obstacle space from a first to a second position, the method includes scanning the vehicle obstacle space at the first and second positions, generating first and second boundary data sets from results of the scanning at the first and second positions, respectively, and deriving first and second shrouded regions from the first and second boundary data sets, respectively. The method further includes identifying a high confidence occupancy region from intersecting portions of the first and second shrouded regions or identifying an occupancy region from a union of the first and second shrouded regions.

Abstract: A method of route planning for a vehicle proceeding from a current location to a destination in a planning space is provided. The method includes generating a destination-rooted tree from global information that provides cost-to-go routing to the destination from multiple locations in the planning space, generating a vehicle-rooted tree using local information from the current location out to a sensing horizon and determining a local destination at the sensing horizon. The local destination corresponds to minimal cost-to-go routing obtained from the destination-rooted tree.

Abstract: A method of operating an obstacle data model construction system of an aircraft is provided. With a vehicle moving through a vehicle obstacle space from a first to a second position, the method includes scanning the vehicle obstacle space at the first and second positions, generating first and second boundary data sets from results of the scanning at the first and second positions, respectively, and deriving first and second shrouded regions from the first and second boundary data sets, respectively. The method further includes identifying a high confidence occupancy region from intersecting portions of the first and second shrouded regions or identifying an occupancy region from a union of the first and second shrouded regions.

Abstract: A method of route planning for a vehicle proceeding from a current location to a destination in a planning space is provided. The method includes generating a destination-rooted tree from global information that provides cost-to-go routing to the destination from multiple locations in the planning space, generating a vehicle-rooted tree using local information from the current location out to a sensing horizon and determining a local destination at the sensing horizon. The local destination corresponds to minimal cost-to-go routing obtained from the destination-rooted tree.