Bioinspired engineering of exploration systems for NASA and DoD

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers

Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation

In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions. A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth\u27s magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance and effectiveness of the proposed Geomagentic architecture in diverse and realistic geomagnetic scenarios. The results of this research demonstrate the potential of Machine Learning algorithms in improving the performance of the sensor fusion filter for geomagnetic navigation, and introduces a novel approach for resolution enhancing of available geomagnetic models, which provides a better description of the magnetic features within these models. The integration leads to more accurate and robust inertial guidance in airborne missions, thus paving the way for advanced, reliable navigation systems for a variety of aerial vehicles. Overall, this dissertation contributes to the state-of-the-art in geomagnetic navigation research by offering a novel approach to integrating machine learning techniques with traditional estimation methods, with a novel technique to obtain more accurate geomagnetic models required within these navigation architectures. The findings of this work hold promise for the development of advanced, adaptive navigation systems for both civilian and military aviation applications

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Bird wings act as a suspension system that rejects gusts

Musculoskeletal systems cope with many environmental perturbations without neurological control. These passive preflex responses aid animals to move swiftly through complex terrain. Whether preflexes play a substantial role in animal flight is uncertain. We investigated how birds cope with gusty environments and found that their wings can act as a suspension system, reducing the effects of vertical gusts by elevating rapidly about the shoulder. This preflex mechanism rejected the gust impulse through inertial effects, diminishing the predicted impulse to the torso and head by 32% over the first 80 ms, before aerodynamic mechanisms took effect. For each wing, the centre of aerodynamic loading aligns with the centre of percussion, consistent with enhancing passive inertial gust rejection. The reduced motion of the torso in demanding conditions simplifies crucial tasks, such as landing, prey capture and visual tracking. Implementing a similar preflex mechanism in future small-scale aircraft will help to mitigate the effects of gusts and turbulence without added computational burden

An Outdoor Stereo Camera System for the Generation of Real-World Benchmark Datasets with Ground Truth

In this report we describe a high-performance stereo camera system to capture image sequences with high temporal and spatial resolution for the evaluation of various image processing tasks. The system was primarily designed for complex outdoor and traffic scenes which frequently occur in the automotive industry, but is also suited for other applications. For this task the system is equipped with a very accurate inertial measurement unit and global positioning system, which provides exact camera movement and position data. The system is already in active use and has produced several terabyte of challenging image sequences which are available for download

FLUID FLOW CHARACTERIZATION IN RAPID PROTOTYPED COMMON ILIAC ARTERY ANEURYSM MOLDS

The goal of this project was to determine whether i) fused deposition modeling could be employed to manufacture molds for vascular constructs, ii) whether vascular constructs could be created from these molds, and iii) to verify practical equivalence between observed fluid velocities. Dye tracking was to be employed to characterize fluid velocity profiles through the in vitro vascular constructs, including a half-vessel model and a full vessel model of an iliac artery aneurysm. A PDMS half-vessel construct was manufactured, and the movement of dye through the construct was tracked by a cellphone camera. Thresholds were applied to each video in HSB or YUV mode in ImageJ, and analyzed to determine the velocity of the fluid through the construct. COMSOL simulations of the half-vessel were conducted for comparison to the empirical observations. Plots describing the flow velocities along the maximum streamline path length were generated, and a one sample t-test was conducted at a 5% significance level to determine whether there was a significant difference between velocity values obtained by dye tracking and the COMSOL simulations. It was determined that the empirical dye tracking trials failed to demonstrate agreement between the measured and predicted flow rates. A full vessel construct was not completed due to unforeseen time constraints. Dye tracking was not determined to be reliable as a means of measuring the maximum velocity of fluid. Discrepancies between the empirical observations and the COMSOL simulation are discussed. The discrepancy was attributed to limitations in the experimental protocol; low frame rate, poor control over lighting conditions, and the subjectivity involved in image processing. Methods of improving upon the manufacturing and experimental protocols used for the half-vessel are proposed for future work, such as improving control over lighting conditions, choosing a camera with a higher frame rate, constructing a more stable fixture, exploring PIV. Additionally, the technical problems leading to the failure to complete the full vessel model are discussed, and changes in the manufacturing process are proposed to allow dissolution or removal of the aneurysm model

Modeling multiphase flow and substrate deformation in nanoimprint manufacturing systems

Nanopatterns found in nature demonstrate that macroscopic properties of a surface are tied to its nano-scale structure. Tailoring the nanostructure allows those macroscopic surface properties to be engineered. However, a capability-gap in manufacturing technology inhibits mass-production of nanotechnologies based on simple, nanometer-scale surface patterns. This gap represents an opportunity for research and development of nanoimprint lithography (NIL) processes. NIL is a process for replicating patterns by imprinting a fluid layer with a solid, nano-patterned template, after which ultraviolet cure solidifies the fluid resulting in a nano-patterned surface. Although NIL has been demonstrated to replicate pattern features as small as 4 nm, there are significant challenges in using it to produce nanotechnology. Ink-jet deposition methods deliver the small fluid volumes necessary to produce the nanopattern, and drop volumes can be tuned to what the pattern requires. However the drops trap pockets of gas as they merge and fill the template, and due to relatively slow gas dissolution, reduce processing throughput. Capillary forces that arise from the gas-liquid interfaces drive non-uniform gap closure and the resulting variations in residual layer reduces process yield or degrades product performance. This thesis develops reduced-order models for fluid flow and structural mechanics of the imprint process for NIL. Understanding key phenomena of gas trapping and residual layer non-uniformity drives model development to better understand how throughput and yield can be improved. Reynolds lubrication theory, the \textit{disperse} type of multiphase flow, and a lumped-parameter model of dissolution unite to produce a two-phase flow model for NIL simulations of 10,000 drops per $\text{cm}^2$. Qualitative agreement between simulation and experiment provides a modicum of validation of this model for flow in NIL simulations. The two-phase model simulations predicts that both dissolution and viscous resistance affect throughput. The coupling of a reduced-order model for 3D structural mechanics with the two-phase flow model enables simulations of drop merger on a free-span tensioned web. Challenges in improving the structural model lead to formulation of a 2D model for which sources of instability are more easily discovered and understood. Inextensible cylindrical shell theory and lubrication theory combine into a model for the elastohydrodynamics of a rolling-imprint modality of NIL. Foil-bearing theory describes the lubrication layer that forms between a thin, tensioned web moving past another surface. Reproduction of the results of foil-bearing theory validates this coupled model and reveals a highly predictable region of uniformity that provides low shear stress conditions ideal for UV-cure. These results show theoretical limitations that are used to construct a processing window for predicting process feasibility

Reconstruction and analysis of dynamic shapes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 122-141).Motion capture has revolutionized entertainment and influenced fields as diverse as the arts, sports, and medicine. This is despite the limitation that it tracks only a small set of surface points. On the other hand, 3D scanning techniques digitize complete surfaces of static objects, but are not applicable to moving shapes. I present methods that overcome both limitations, and can obtain the moving geometry of dynamic shapes (such as people and clothes in motion) and analyze it in order to advance computer animation. Further understanding of dynamic shapes will enable various industries to enhance virtual characters, advance robot locomotion, improve sports performance, and aid in medical rehabilitation, thus directly affecting our daily lives. My methods efficiently recover much of the expressiveness of dynamic shapes from the silhouettes alone. Furthermore, the reconstruction quality is greatly improved by including surface orientations (normals). In order to make reconstruction more practical, I strive to capture dynamic shapes in their natural environment, which I do by using hybrid inertial and acoustic sensors. After capture, the reconstructed dynamic shapes are analyzed in order to enhance their utility. My algorithms then allow animators to generate novel motions, such as transferring facial performances from one actor onto another using multi-linear models. The presented research provides some of the first and most accurate reconstructions of complex moving surfaces, and is among the few approaches that establish a relationship between different dynamic shapes.by Daniel Vlasic.Ph.D