Browse Topic: Cameras
The empennage of a helicopter is largely responsible for its stability in forward flight. Its performance is mainly determined by its aerodynamics. In this paper, the empennage of a CoAX 2D ultralight research helicopter is analyzed in detail. For this purpose, the helicopter was equipped with flow measurement devices and flight tests were performed, covering different flight conditions. Measurements from a nose boom as well as the pilot’s control inputs and helicopter's position are available for evaluation. For the empennage in particular, seven-hole flow probes were mounted on it and various cameras were used to record the movement of the surface tufts.
In this paper, we develop a new feature-based algorithm using stereo cameras to estimate stochastic ship-deck motion at high sea states. Unlike our previous algorithms, this algorithm is able to estimate the motion of arbitrary ship structures without prior information on the ship's visual appearance or geometry. The algorithm requires an initial pose and suffers from drift over time, which was resolved by fusing it with our previous 2D feature-based vision algorithm. The combined vision algorithm is validated using a simulated ship featuring 3D ship structures and 2D flight deck markings representative of a DDG-51 ship. The results indicate that the algorithm can accurately estimate the pose of a simulated ship undergoing Sea-State 6 motion. The vision algorithm was further validated in a simple free-flight test.
The Shake-The-Box technique was applied to experimentally quantify the time-resolved volumetric flow field around a free-flying quadcopter UAV with an overall span of about 0.5 m. State-of-the-art LED illumination and high-speed camera equipment was combined with modern Lagrangian tracer particle tracking and data assimilation techniques, facilitating a measurement volume larger than 1.5m3. The setup allowed for both hover and limited maneuvering of the quadcopter, while resolving even small details of the complex interactional aerodynamics. In hover out of ground effect, the four individual rotor wakes merged into a single jet within a few rotor radii below the rotor planes. Evaluating the mass and momentum fluxes over suitable control volumes yields accurate estimates for the quadcopter's total thrust, the asymmetric thrust distribution between front and back rotors, and the entrainment of external flow through turbulent mixing. Hover in ground effect decreases the power requirement and induces recirculating flow in the center of the four rotors. The outwash pattern is non-uniform with jets developing between the rotors and pointing in radially outward directions. Forward flight cases result in a skewed, rapidly merging wake flanked by the roll-up of two "super-vortices" similar to the wingtip vortices of fixed-wing vehicles.
The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading of each detected gauge. The contribution of this work is two-fold: (1) enhance rotorcraft safety by developing a DL framework capable of detecting, classifying, and inferring gauge readings for different helicopter types, and (2) boost research in the field by constructing a curated dataset valuable for aviation and machine learning communities.
Collins Aerospace, through its Common Avionics Architecture System (CAAS) and Flight2 avionics management systems for rotary wing aircrafts, provides extensive video processing, internal graphics generation, and overlay capabilities on real time video streamed from onboard EO/IR cameras providing situational awareness to the pilot in clear day-light and reduced visibility/night conditions. This capability has served our customers well in their cargo, assault, and multi-mission roles, improving the effectiveness of their missions. We now realize that more can be done to reduce pilot workload and enhance mission effectiveness by extracting visual intelligence from the video feed using machine vision. In this paper we explore the use of deep learning based computer vision to extract visual intelligence from onboard video feed and use it to automate low risk pilot actions, such as automatic detection and tracking of objects of interest, panning to maintain focus on objects, zooming on to a chosen object and providing contextualized data link message recommendations.
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