Component-Level Electronic-Assembly Repair (CLEAR) Operational Concept

This Component-Level Electronic-Assembly Repair (CLEAR) Operational Concept document was developed as a first step in developing the Component-Level Electronic-Assembly Repair (CLEAR) System Architecture (NASA/TM-2011-216956). The CLEAR operational concept defines how the system will be used by the Constellation Program and what needs it meets. The document creates scenarios for major elements of the CLEAR architecture. These scenarios are generic enough to apply to near-Earth, Moon, and Mars missions. The CLEAR operational concept involves basic assumptions about the overall program architecture and interactions with the CLEAR system architecture. The assumptions include spacecraft and operational constraints for near-Earth orbit, Moon, and Mars missions. This document addresses an incremental development strategy where capabilities evolve over time, but it is structured to prevent obsolescence. The approach minimizes flight hardware by exploiting Internet-like telecommunications that enables CLEAR capabilities to remain on Earth and to be uplinked as needed. To minimize crew time and operational cost, CLEAR exploits offline development and validation to support online teleoperations. Operational concept scenarios are developed for diagnostics, repair, and functional test operations. Many of the supporting functions defined in these operational scenarios are further defined as technologies in NASA/TM-2011-216956

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Evolved transistor array robot controllers

For the first time a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium

Neural Network for Electronic Nose using Field Programmable Analog Arrays

Electronic nose is a device detecting odors which is designed to resemblethe ability of the human nose, usually applied to the robot. The process ofidentification of the electronic nose will run into a problem when the gaswhich is detected has the same chemical element. Misidentification due tothe similarity of chemical properties of gases is possible; it can be solvedusing neural network algorithms. The attendance of Field ProgrammableAnalog Array (FPAA) enables the design and implementation of ananalog neural network, while the advantage of analog neural networkwhich is an input signal from the sensor can be processed directly by theFPAA without having to be converted into a digital signal. Direct analogsignal process can reduce errors due to conversion and speed up thecomputing process. The small size and low power usage of FPAA are verysuitable when it is used for the implementation of the electronic nose thatwill be applied to the robot. From this study, it was shown that theimplementation of analog neural network in FPAA can support theperformance of electronic nose in terms of flexibility (resource componentrequired), speed, and power consumption. To build an analog neuralnetwork with three input nodes and two output nodes only need twopieces of Configurable Analog Block (CAB), of the four provided by theFPAA. Analog neural network construction has a speed of the process0.375 μs, and requires only 59 ± 18mW resources.DOI:http://dx.doi.org/10.11591/ijece.v2i6.150

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Lunar Surface Systems Supportability Technology Development Roadmap

The Lunar Surface Systems Supportability Technology Development Roadmap is a guide for developing the technologies needed to enable the supportable, sustainable, and affordable exploration of the Moon and other destinations beyond Earth. Supportability is defined in terms of space maintenance, repair, and related logistics. This report considers the supportability lessons learned from NASA and the Department of Defense. Lunar Outpost supportability needs are summarized, and a supportability technology strategy is established to make the transition from high logistics dependence to logistics independence. This strategy will enable flight crews to act effectively to respond to problems and exploit opportunities in an environment of extreme resource scarcity and isolation. The supportability roadmap defines the general technology selection criteria. Technologies are organized into three categories: diagnostics, test, and verification; maintenance and repair; and scavenge and recycle. Furthermore, "embedded technologies" and "process technologies" are used to designate distinct technology types with different development cycles. The roadmap examines the current technology readiness level and lays out a four-phase incremental development schedule with selection decision gates. The supportability technology roadmap is intended to develop technologies with the widest possible capability and utility while minimizing the impact on crew time and training and remaining within the time and cost constraints of the program

Flight Avionics Hardware Roadmap

As part of NASA's Avionics Steering Committee's stated goal to advance the avionics discipline ahead of program and project needs, the committee initiated a multi-Center technology roadmapping activity to create a comprehensive avionics roadmap. The roadmap is intended to strategically guide avionics technology development to effectively meet future NASA missions needs. The scope of the roadmap aligns with the twelve avionics elements defined in the ASC charter, but is subdivided into the following five areas: Foundational Technology (including devices and components), Command and Data Handling, Spaceflight Instrumentation, Communication and Tracking, and Human Interfaces

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches

Implementation of Electronic Nose in Omni-directional Robot

Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas.DOI:http://dx.doi.org/10.11591/ijece.v3i3.253