Understanding Skill in EVA Mass Handling

Key attributes of skilled mass handling were identified through an examination of lessons learned by the extravehicular activity operational community. These qualities were translated into measurable quantities. The operational validity of the ground-based investigation was improved by building a device that increased the degrees of freedom of extravehicular mobility unit motion on the Precision Air-Bearing Floor. The results revealed subtle patterns of interaction between motions of an orbital replacement unit mockup and mass handler that should be important for effective performance on orbit. The investigation also demonstrated that such patterns can be measured with a variety of common instruments and under imperfect conditions of observation

Understanding Skill in EVA Mass Handling

This report describes the theoretical and operational foundations for our analysis of skill in extravehicular mass handling. A review of our research on postural control, human-environment interactions, and exploratory behavior in skill acquisition is used to motivate our analysis. This scientific material is presented within the context of operationally valid issues concerning extravehicular mass handling. We describe the development of meaningful empirical measures that are relevant to a special class of nested control systems: manual interactions between an individual and the substantial environment. These measures are incorporated into a unique empirical protocol implemented on NASA's principal mass handling simulator, the precision air-bearing floor, in order to evaluate skill in extravehicular mass handling. We discuss the components of such skill with reference to the relationship between postural configuration and controllability of an orbital replacement unit, the relationship between orbital replacement unit control and postural stability, the relationship between antecedent and consequent movements of an orbital replacement unit, and the relationship between antecedent and consequent postural movements. Finally, we describe our expectations regarding the operational relevance of the empirical results as it pertains to extravehicular activity tools, training, monitoring, and planning

Fleet planning and technology upgrade projects: supporting decision-making through visualisation

In project planning, visualisations can be powerful tools for communication and in supporting decision-making between stakeholders. However the graphical elements, in terms of form and presentational style, are typically poorly treated and can therefore diminish both the impact and conveyance of information. Traditional timeline representations need to be adapted and modified in order to meet the requirements of specific stakeholder groups and thus fulfil their role as effective visual boundary objects. This paper describes the visualisation designed and developed for the planning of technology upgrade projects across a fleet of military platforms. A real-world application of the visualisation is provided through an illustrative case study based on the front-line fleet of Type 23 frigates of the Royal Navy

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1989, volume 1

The 1989 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by Texas A and M University and JSC. The 10-week program was operated under the auspices of the ASEE. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington, D.C. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

A layered control architecture for mobile robot navigation

A Thesis submitted to the University Research Degree Committee in fulfillment ofthe requirements for the degree of DOCTOR OF PHILOSOPHY in RoboticsThis thesis addresses the problem of how to control an autonomous mobile robot navigation in indoor environments, in the face of sensor noise, imprecise information, uncertainty and limited response time. The thesis argues that the effective control of autonomous mobile robots can be achieved by organising low level and higher level control activities into a layered architecture. The low level reactive control allows the robot to respond to contingencies quickly. The higher level control allows the robot to make longer term decisions and arranges appropriate sequences for a task execution. The thesis describes the design and implementation of a two layer control architecture, a task template based sequencing layer and a fuzzy behaviour based low level control layer. The sequencing layer works at the pace of the higher level of abstraction, interprets a task plan, mediates and monitors the controlling activities. While the low level performs fast computation in response to dynamic changes in the real world and carries out robust control under uncertainty. The organisation and fusion of fuzzy behaviours are described extensively for the construction of a low level control system. A learning methodology is also developed to systematically learn fuzzy behaviours and the behaviour selection network and therefore solve the difficulties in configuring the low level control layer. A two layer control system has been implemented and used to control a simulated mobile robot performing two tasks in simulated indoor environments. The effectiveness of the layered control and learning methodology is demonstrated through the traces of controlling activities at the two different levels. The results also show a general design methodology that the high level should be used to guide the robot's actions while the low level takes care of detailed control in the face of sensor noise and environment uncertainty in real time

Learning the selection of actions for an autonomous social robot by reinforcement learning based on motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot's needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment. The used reinforcement learning method exploits a new algorithm called Object Q-Learning. The proposed reduction of the state space and the new algorithm considering the collateral effects (relationship between different objects) results in a suitable algorithm to be applied to robots living in real environments. In this paper, a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot's performance. The quality of its performance will be determined by observing the evolution of the robot's wellbeing.The funds provided by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), and the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

A Self-Adaptive Online Brain Machine Interface of a Humanoid Robot through a General Type-2 Fuzzy Inference System

This paper presents a self-adaptive general type-2 fuzzy inference system (GT2 FIS) for online motor imagery (MI) decoding to build a brain-machine interface (BMI) and navigate a bi-pedal humanoid robot in a real experiment, using EEG brain recordings only. GT2 FISs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) maximum number of electroencephalography (EEG) channels is limited and fixed, 2) no possibility of performing repeated user training sessions, and 3) desirable use of unsupervised and low complexity features extraction methods. The novel learning method presented in this paper consists of a self-adaptive GT2 FIS that can both incrementally update its parameters and evolve (a.k.a. self-adapt) its structure via creation, fusion and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structure identification is based on an online GT2 Gath-Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models). The effectiveness of the proposed method is demonstrated in a detailed BMI experiment where 15 untrained users were able to accurately interface with a humanoid robot, in a single thirty-minute experiment, using signals from six EEG electrodes only