12,153 research outputs found
Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services
This paper presents the development of ICAROUS-2 (Independent Configurable Architecture for Reliable Operation of Unmanned Systems with Distributed Onboard Services), the second generation of a software architecture that integrates several algorithms as distributed onboard services to enable robust autonomous UAS applications. In particular, the ICAROUS architecture defines a framework to perform detect and avoid, geofencing, path monitoring, path planning, and autonomous decision making to ensure safety and mission progress. Most of the core algorithms implemented in ICAROUS are formally verified using an interactive theorem prover. These algorithms are composed together using a plan execution engine, whose operational semantics is formally specified. A description of the integrated architecture, services currently available, and flight test results highlighting the capability of ICAROUS are presented
Calibrating a high-resolution wavefront corrector with a static focal-plane camera
We present a method to calibrate a high-resolution wavefront-correcting
device with a single, static camera, located in the focal plane; no moving of
any component is needed. The method is based on a localized diversity and
differential optical transfer functions (dOTF) to compute both the phase and
amplitude in the pupil plane located upstream of the last imaging optics. An
experiment with a spatial light modulator shows that the calibration is
sufficient to robustly operate a focal-plane wavefront sensing algorithm
controlling a wavefront corrector with ~40 000 degrees of freedom. We estimate
that the locations of identical wavefront corrector elements are determined
with a spatial resolution of 0.3% compared to the pupil diameter.Comment: 12 pages, 12 figures, accepted for publication in Applied Optic
TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes
We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware
A Specialized Processor for Track Reconstruction at the LHC Crossing Rate
We present the results of an R&D study of a specialized processor capable of
precisely reconstructing events with hundreds of charged-particle tracks in
pixel detectors at 40 MHz, thus suitable for processing LHC events at the full
crossing frequency. For this purpose we design and test a massively parallel
pattern-recognition algorithm, inspired by studies of the processing of visual
images by the brain as it happens in nature. We find that high-quality tracking
in large detectors is possible with sub-s latencies when this algorithm is
implemented in modern, high-speed, high-bandwidth FPGA devices. This opens a
possibility of making track reconstruction happen transparently as part of the
detector readout.Comment: Presented by G.Punzi at the conference on "Instrumentation for
Colliding Beam Physics" (INSTR14), 24 Feb to 1 Mar 2014, Novosibirsk, Russia.
Submitted to JINST proceeding
Transferable Pedestrian Motion Prediction Models at Intersections
One desirable capability of autonomous cars is to accurately predict the
pedestrian motion near intersections for safe and efficient trajectory
planning. We are interested in developing transfer learning algorithms that can
be trained on the pedestrian trajectories collected at one intersection and yet
still provide accurate predictions of the trajectories at another, previously
unseen intersection. We first discussed the feature selection for transferable
pedestrian motion models in general. Following this discussion, we developed
one transferable pedestrian motion prediction algorithm based on Inverse
Reinforcement Learning (IRL) that infers pedestrian intentions and predicts
future trajectories based on observed trajectory. We evaluated our algorithm on
a dataset collected at two intersections, trained at one intersection and
tested at the other intersection. We used the accuracy of augmented
semi-nonnegative sparse coding (ASNSC), trained and tested at the same
intersection as a baseline. The result shows that the proposed algorithm
improves the baseline accuracy by 40% in the non-transfer task, and 16% in the
transfer task
- …