Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Development of a fiber-based shape sensor for navigating flexible medical tools

Robot-assisted minimally invasive surgical procedure (RAMIS) is a subfield of minimally invasive surgeries with enhanced manual dexterity, manipulability, and intraoperative image guidance. In typical robotic surgeries, it is common to use rigid instruments with functional articulating tips. However, in some operations where no adequate and direct access to target anatomies is available, continuum robots can be more practical, as they provide curvilinear and flexible access. However, their inherent deformable design makes it difficult to accurately estimate their 3D shape during the operation in real-time. Despite extensive model-based research that relies on kinematics and mechanics, accurate shape sensing of continuum robots remains challenging. The state-of-the-art tracking technologies, including optical trackers, EM tracking systems, and intraoperative imaging modalities, are also unsuitable for this task, as they all have shortcomings. Optical fiber shape sensing solutions offer various advantages compared to other tracking modalities and can provide high-resolution shape measurements in real-time. However, commercially available fiber shape sensors are expensive and have limited accuracy. In this thesis, we propose two cost-effective fiber shape sensing solutions based on multiple single-mode fibers with FBG (fiber Bragg grating) arrays and eccentric FBGs. First, we present the fabrication and calibration process of two shape sensing prototypes based on multiple single-mode fibers with semi-rigid and super-elastic substrates. Then, we investigate the sensing mechanism of edge-FBGs, which are eccentric Bragg gratings inscribed off-axis in the fiber's core. Finally, we present a deep learning algorithm to model edge-FBG sensors that can directly predict the sensor's shape from its signal and does not require any calibration or shape reconstruction steps. In general, depending on the target application, each of the presented fiber shape sensing solutions can be used as a suitable tracking device. The developed fiber sensor with the semi-rigid substrate has a working channel in the middle and can accurately measure small deflections with an average tip error of 2.7 mm. The super-elastic sensor is suitable for measuring medium to large deflections, where a centimeter range tip error is still acceptable. The tip error in such super-elastic sensors is higher compared to semi-rigid sensors (9.9-16.2 mm in medium and large deflections, respectively), as there is a trade-off between accuracy and flexibility in substrate-based fiber sensors. Edge-FBG sensor, as the best performing sensing mechanism among the investigated fiber shape sensors, can achieve a tip accuracy of around 2 mm in complex shapes, where the fiber is heavily deflected. The developed edge-FBG shape sensing solution can compete with the state-of-the-art distributed fiber shape sensors that cost 30 times more

Fine-grained Energy and Thermal Management using Real-time Power Sensors

With extensive use of battery powered devices such as smartphones, laptops an

Analysis of spacecraft anomalies

The anomalies from 316 spacecraft covering the entire U.S. space program were analyzed to determine if there were any experimental or technological programs which could be implemented to remove the anomalies from future space activity. Thirty specific categories of anomalies were found to cover nearly 85 percent of all observed anomalies. Thirteen experiments were defined to deal with 17 of these categories; nine additional experiments were identified to deal with other classes of observed and anticipated anomalies. Preliminary analyses indicate that all 22 experimental programs are both technically feasible and economically viable

Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures

An architecture for intelligent health assessment enabled IEEE 1451 compliant smart sensors

As systems become increasingly complex and costly, potential failure mechanisms and indicators are numerous and difficult to identify, while the cost of loss is very expensive - human lives, replacement units, and impacts to national security. In order to ensure the safety and long-term reliability of vehicles, structures, and devices attention must be directed toward the assessment and management of system health. System health is the key component that links data, information, and knowledge to action. Integrated Systems Health Management (ISHM) doctrine calls for comprehensive real-time health assessment and management of systems where the distillation of raw data into information takes place within sensors and actuators. This thesis develops novel field programmable health assessment capability for sensors and actuators in ISHM. Health assessment and feature extraction algorithms are implemented on a sensor or actuator through the Embedded Routine Manager (ERM) API. Algorithms are described using Health Electronic Datasheets (HEDS) to provide more flexible run-time operation. Interfacing is accomplished through IEEE Standard 1451 for Smart Sensors and Actuators, connecting ISHM with the instrumentation network of the future. These key elements are validated using exemplar algorithms to detect noise, spike, and flat-line events onboard the ISHM enabled Methane Thruster Testbed Project (MTTP) at NASA Stennis Space Center in Mississippi

Effect of different extraction methods on vitamin B12 from edible green seaweed, Ulva lactuca by 2-Level Factorial Design

Ulva lactuca has been known for its valuable biological properties such as antioxidant and antiviral due to the presence of several bioactive compounds like vitamins, polysaccharides, and lipids. In the present study, different extraction procedures of vitamin B12 were compared. Four types of dried U. lactuca (oven-dried, sun-dried, air-dried, freeze-dried) were subjected to three different extraction methods each (boiling, orbital shaking, and ultrasonic-assisted extraction (UAE)). Also, the effect of solvent:solvent (MeOH:H2O), solute:solvent ratio, and pH on the total vitamin B12 content were screened and statistically analysed using 2-Level Factorial design from RSM. The retention time (RT) from HPLC of all crude extracts were corresponded with the RT of standard cyanocobalamin (CN-Cbl) at 1.9 minutes. The extraction of CN-Cbl by orbital shaking gave insignificant (p > 0.05) result on the yield of CN-Cbl, compared to the boiling and UAE methods when oven-dried U. lactuca were extracted (p < 0.05). The highest concentration of CN-Cbl (0.0236 mg/mL) was recorded when extracted by UAE method at parameters of 25:75 % MeOH:H2O, pH 3, 3 g: 60 mL solute:solvent ratio. Statistically, the concentration of CN-Cbl increased significantly (p < 0.05) with decreasing MeOH:H2O, solute:solvent ratios and pH when U. lactuca was subjected to oven-dried drying method, and extracted using UAE method. Hence, these factors may be further optimised in the future research to extract a better yield of CN-Cbl

Evaluating Post-fire Vegetation Recovery in Canadian Mixed Prairie Using Remote Sensing Approaches

This study investigated a wildfire occurred in April 2013 at Grasslands National Park, aiming to quantify vegetation's post-fire recovery with both field and remote sensing approaches. Biophysical parameters and hyperspectral reflectances were collected through field surveys conducted one year prior to the fire as well as five continuous years post-fire at growing seasons. These data were processed into burned and unburned samples followed by significance test to reveal biophysical differences across samples. Results indicated an overall recovery of the grassland within 4-5 years, with different vegetation forms recovering at various post-fire growing seasons. Green grass was the most resilient component that fully recovered one year post-fire, followed by forbs at two years post-fire, with shrubs and soil organic crust taking longer than four years to recover compared to the adjacent unburned communities. Hyperspectral dataset was used to establish the utility of remote sensing approaches in grasslands fire-study. Results suggested the potential of satellite remote sensing data in such application. Furthermore, Landsat dataset were processed and significance test was repeated to further prove the sensitivity of Landsat product (especially NDVI) in distinguishing burned and unburned samples, as well as good agreement with conclusions established from field data analysis. Finally, major driving factors were analyzed with ANOVA and results indicated the significant role of meteorological variables and topography in vegetation's post-fire recovery. Findings from this research contribute to a better understanding of fire's effect on the under-studied Canadian northern mixed prairie. Also, the successful validation of RS based approaches can provide as the theoretical basis for potential future RS applications in modelling grassland post-fire recovery in the mixed prairie

Thermal mapping, geothermal source location, natural effluents and plant stress in the Mediterranean coast of Spain

Data obtained by HCMM satellite over a complex area in eastern Spain were evaluated and found to be most useful in studying macrostructures in geology and in analyzing marine currents, layers, and areas (although other satellites provide more data). The upper scale to work with HCMM data appears to be 1:2.000.000. Techniques used in preprocessing, processing, and analyzing imagery are discussed as well as methods for pattern recognition. Surface temperatures obtained for soils, farmlands, forests, geological structures, and coastal waters are discussed. Suggestions are included for improvements needed to achieve better results in geographic areas similar to the study area