Sikorsky Aircraft Corporation

Jason C Derenick

Christopher Stathis

Igor Cherepinsky

Harshad S Sane

Xuchu Ding

Hongcheng Wang

Brendan Englot

Joshua M Leland

Ziyou Xiong

Thomas A Frewen

Brigid Blakeslee

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 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

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

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.

Space partitioning for motion 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

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

According to an aspect of the invention, a method of probabilistic safe landing area determination for an aircraft includes receiving sensor data indicative of current conditions at potential landing areas for the aircraft. Feature extraction on the sensor data is performed. A processing subsystem of the aircraft updates a probabilistic safe landing area map based on comparing extracted features of the sensor data with a probabilistic safe landing area model. The probabilistic safe landing area model defines probabilities that terrain features are suitable for safe landing of the aircraft. A list of ranked landing areas is generated based on the probabilistic safe landing area map.

Probabilistic safe landing area determination

A method for determining a suitable landing area for an aircraft includes receiving signals indicative of Light Detection And Ranging (LIDAR) information for a terrain via a LIDAR perception system; receiving signals indicative of image information for the terrain via a camera perception system; evaluating, with the processor, the LIDAR information and generating information indicative of a LIDAR landing zone candidate region; co-registering in a coordinate system, with the processor, the LIDAR landing zone candidate region and the image information; segmenting, with the processor, the co-registered image and the LIDAR landing zone candidate region to generate segmented regions; classifying, with the processor, the segmented regions into semantic classes; determining, with the processor, contextual information in the semantic classes; and ranking and prioritizing the contextual information.

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Hamden, CT, United States of America

Jason C Derenick