Sikorsky Aircraft Corporation

Christopher Stathis

Igor Cherepinsky

Jason C Derenick

Harshad S Sane

Joshua M Leland

Hongcheng Wang

Xuchu Ding

Ziyou Xiong

George Nicholas Loussides

Alan Matthew Finn

Michael Aaron Connor

Brendan Englot

Garrett Pitcher

Matthew Zywiak

Brigid Blakeslee

Michael Aaron Conner

Shuo Zhang

Brigid A Blakeslee

A method for imaging a moving object includes scanning a predetermined area with at least one distance sensor to form an image of a structure of a moving object using a safe sensing time window to periodically refresh the image. The images of the structure are compared to a known model of the structure to estimate rates of motion. A refined time window is determined based on the estimated rates of motion to monitor the moving object with increased accuracy and/or range compared to the safe time window.

Adaptive point cloud window selection

According to an aspect of the invention, a method of optimal safe landing area determination for an aircraft includes accessing a probabilistic safe landing area map that includes a plurality of probabilistic indicators of safe landing areas for the aircraft. A processing subsystem that includes one or more processing resources generates a list of candidate safe landing areas based on the probabilistic safe landing area map and one or more constraints. At least two of the candidate safe landing areas are provided to a path planner. The list of candidate safe landing areas is ranked based on results from the path planner indicating an estimated cost to reach each of the candidate safe landing areas. Based on the ranking, an indicator of an optimal safe landing area is output as a desired landing location for the aircraft.

Optimal safe landing area determination

A system for registering a target includes a first sensor, a second sensor, and a processor. The first sensor measures a plurality of ranges from a source to a target, and the second sensor obtains a plurality of location measurements of the source. The system further includes a processor configured for determining one or more weighting criteria associated with each one of the plurality of location measurements based on an estimated reliability of each one of the plurality of location measurements. The processor calculates a plurality of target location values based on the plurality of ranges measured by the first sensor and the plurality of locations measured by the second sensor and calculates an estimated target location value based on the plurality of target location values weighted according to the weighting criteria.

Multi-sensor target location registration

According to an aspect of the invention, a method of flight initiation proximity warning for an autonomous vehicle is provided. A flight initiation request is detected at a processing system of the autonomous vehicle. A preflight proximity scan is performed for any obstacles within a predetermined distance from the autonomous vehicle based on the flight initiation request. An alert is sent to a control station based on detecting at least one obstacle within the predetermined distance. Flight initiation of the autonomous vehicle is inhibited until an acknowledgement of the alert is received at the processing system of the autonomous vehicle.

Flight initiation proximity warning system

A method for augmenting a synthetic vision system of a vehicle includes receiving, with a processor, signals indicative of real-time sensor data of a terrain for the vehicle via one or more acquisition devices; creating, with the processor, a terrain mesh of the terrain in response to the receiving of the sensor data; correlating, with the processor, the terrain mesh with preloaded terrain data of the terrain; creating, with the processor, a multispectral image of the terrain in response to the correlating the terrain mesh with the preloaded data; and texturing, with the processor, the terrain mesh for display on a display device.

Synthetic vision augmented with multispectral sensing

A method of cooperative communication link mapping and classification is provided. Location data are received defining a position of a communication node. Signal data of a communication link are monitored at the communication node with respect to a second communication node and characteristics of the signal data are identified. A location stamp of the position is applied to the characteristics of the signal data. The communication link is classified based on the characteristics of the signal data, and a map is populated classifying the communication link at the position indicated by the location stamp.

Cooperative communication link mapping and classification

An electronic landing platform state module is configured to generate a state estimation of a platform surface at sea includes a plurality of electronic platform state process modules configured to receive an output from a respective spectral sensor. The plurality of electronic platform state process modules are further configured to output a monitored spectral platform state signal in response to applying a spectral process on a respective output. Each spectral process corresponds to a particular spectral modality of the respective spectral sensor. The electronic landing platform state module further includes an electronic platform state estimator module configured to determine a corrected dynamic state of the platform in response to fusing together the individual monitored spectral platform state signals.

Multispectral sensor fusion system for platform state estimation

A method of performing a cooperative safe landing area determination includes receiving perception sensor data indicative of conditions at a plurality of potential landing areas. A processing subsystem of a vehicle updates a local safe landing area map based on the perception sensor data. The local safe landing area map defines safe landing area classifications and classification confidences associated with the potential landing areas. One or more remotely-generated safe landing area maps are received from one or more remote data sources. The one or more remotely-generated safe landing area maps correspond to one or more additional potential landing areas and non-landing areas. The local safe landing area map and the remotely-generated safe landing area maps are aggregated to form a fused safe landing area map. The fused safe landing area map is used to make a final safe landing area determination.

Cooperative safe landing area determination

A method and system for sharing information between independent agents having a processor and memory having instructions stored thereon that, when executed by the processor, cause the system to receive signals indicative of a fault of a sensor system of a first independent agent; transmit signals indicative of a request for sensor information to a second independent agent; receive signals indicative of state information for the second independent agent in response to the receiving of the request for sensor information; and apply the state information to a navigation system of the first independent agent in response to the receiving of the state information.

Cooperative perception and state estimation for vehicles with compromised sensor systems

According to an aspect of the invention, a method of trajectory-based sensor planning for a vehicle includes receiving an indication of a planned change in a trajectory of the vehicle. A processing subsystem determines a current field of view of a directional sensor and a planned adjustment in the current field of view of the directional sensor relative to the vehicle to align with the planned change in the trajectory of the vehicle. The planned adjustment in the current field of view of the directional sensor is initiated prior to changing the trajectory of the vehicle.

Trajectory-based sensor planning

An online sensor calibration verification system includes at least one sensor configured to extract a calibration feature included in a field of view of the sensor. The online sensor calibration verification system further includes an electronic calibration verification module configured to determine a static reference feature model, and to verify a calibration of the at least one sensor based on a positional relationship between an extracted calibration feature and the static reference feature model.

Online sensor calibration verification system

A system and method for determining the distance between at least one point on a vehicle and at least one projected area off of the vehicle includes receiving, with a processor, sensor signals indicative of LIDAR data for the projected area off the vehicle; applying, with the processor, a linear estimation algorithm to filter out noise within the LIDAR data and define a surface plane for the projected area; evaluating, with the processor, the LIDAR data against a vehicle state model; determining, with the processor, the distance between the at least one point on the vehicle and the at least one projected area off the vehicle; and commanding a response in the vehicle controls.

LIDAR-based vehicle distance measurement system

According to an aspect, a method of performing context-aware landing zone classification for an aircraft includes accessing a landing zone map, by a context-aware landing zone classification system of the aircraft, to identify potential landing zones. A database on the aircraft includes land cover map data and impervious surface map data. The database is queried to extract context data. The context data include land cover characteristics and impervious surface characteristics associated with locations corresponding to the landing zone map. The context-aware landing zone classification system of the aircraft evaluates the potential landing zones in view of the context data to adjust classifications of the potential landing zones and produce a context-aware landing zone classification of the potential landing zones. The context-aware landing zone classification of the potential landing zones is provided to landing zone selection logic of the aircraft to select a final landing zone.

Context-aware landing zone classification

A system and method for state estimation of a surface of a platform at sea, includes receiving sensor signals indicative of LIDAR data for the platform; applying a Bayesian filter to the LIDAR data for a plurality of hypotheses; determining vertical planes representing azimuth and elevation angles for the LIDAR data; applying a robust linear estimation algorithm to the vertical planes; and determining candidate points in response to the applying of the robust linear estimation algorithm.

LIDAR-based shipboard tracking and state estimation for autonomous landing

A method of autonomous landing of an aircraft in a landing area includes receiving, with the processor, sensor signals related to the landing area via a sensor device; obtaining, with the processor, a template of the landing area in response to the receiving of the sensor signals; matching, with the processor, one or more features of the template with the features of the acquired images of the landing area; and controlling, with the processor, each of the sensor device and aircraft control system independently based on said matching.

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Hamden, CT, United States of America

Christopher Stathis