10,031 research outputs found
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Online Robot Introspection via Wrench-based Action Grammars
Robotic failure is all too common in unstructured robot tasks. Despite
well-designed controllers, robots often fail due to unexpected events. How do
robots measure unexpected events? Many do not. Most robots are driven by the
sense-plan act paradigm, however more recently robots are undergoing a
sense-plan-act-verify paradigm. In this work, we present a principled
methodology to bootstrap online robot introspection for contact tasks. In
effect, we are trying to enable the robot to answer the question: what did I
do? Is my behavior as expected or not? To this end, we analyze noisy wrench
data and postulate that the latter inherently contains patterns that can be
effectively represented by a vocabulary. The vocabulary is generated by
segmenting and encoding the data. When the wrench information represents a
sequence of sub-tasks, we can think of the vocabulary forming a sentence (set
of words with grammar rules) for a given sub-task; allowing the latter to be
uniquely represented. The grammar, which can also include unexpected events,
was classified in offline and online scenarios as well as for simulated and
real robot experiments. Multiclass Support Vector Machines (SVMs) were used
offline, while online probabilistic SVMs were are used to give temporal
confidence to the introspection result. The contribution of our work is the
presentation of a generalizable online semantic scheme that enables a robot to
understand its high-level state whether nominal or abnormal. It is shown to
work in offline and online scenarios for a particularly challenging contact
task: snap assemblies. We perform the snap assembly in one-arm simulated and
real one-arm experiments and a simulated two-arm experiment. This verification
mechanism can be used by high-level planners or reasoning systems to enable
intelligent failure recovery or determine the next most optima manipulation
skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494
Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models
Robot introspection, as opposed to anomaly detection typical in process
monitoring, helps a robot understand what it is doing at all times. A robot
should be able to identify its actions not only when failure or novelty occurs,
but also as it executes any number of sub-tasks. As robots continue their quest
of functioning in unstructured environments, it is imperative they understand
what is it that they are actually doing to render them more robust. This work
investigates the modeling ability of Bayesian nonparametric techniques on
Markov Switching Process to learn complex dynamics typical in robot contact
tasks. We study whether the Markov switching process, together with Bayesian
priors can outperform the modeling ability of its counterparts: an HMM with
Bayesian priors and without. The work was tested in a snap assembly task
characterized by high elastic forces. The task consists of an insertion subtask
with very complex dynamics. Our approach showed a stronger ability to
generalize and was able to better model the subtask with complex dynamics in a
computationally efficient way. The modeling technique is also used to learn a
growing library of robot skills, one that when integrated with low-level
control allows for robot online decision making.Comment: final version submitted to humanoids 201
Third Conference on Artificial Intelligence for Space Applications, part 2
Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed
Advanced manned space flight simulation and training: An investigation of simulation host computer system concepts
The findings of a preliminary investigation by Southwest Research Institute (SwRI) in simulation host computer concepts is presented. It is designed to aid NASA in evaluating simulation technologies for use in spaceflight training. The focus of the investigation is on the next generation of space simulation systems that will be utilized in training personnel for Space Station Freedom operations. SwRI concludes that NASA should pursue a distributed simulation host computer system architecture for the Space Station Training Facility (SSTF) rather than a centralized mainframe based arrangement. A distributed system offers many advantages and is seen by SwRI as the only architecture that will allow NASA to achieve established functional goals and operational objectives over the life of the Space Station Freedom program. Several distributed, parallel computing systems are available today that offer real-time capabilities for time critical, man-in-the-loop simulation. These systems are flexible in terms of connectivity and configurability, and are easily scaled to meet increasing demands for more computing power
Supervisory Control System Architecture for Advanced Small Modular Reactors
This technical report was generated as a product of the Supervisory Control for Multi-Modular SMR Plants project within the Instrumentation, Control and Human-Machine Interface technology area under the Advanced Small Modular Reactor (SMR) Research and Development Program of the U.S. Department of Energy. The report documents the definition of strategies, functional elements, and the structural architecture of a supervisory control system for multi-modular advanced SMR (AdvSMR) plants. This research activity advances the state-of-the art by incorporating decision making into the supervisory control system architectural layers through the introduction of a tiered-plant system approach. The report provides a brief history of hierarchical functional architectures and the current state-of-the-art, describes a reference AdvSMR to show the dependencies between systems, presents a hierarchical structure for supervisory control, indicates the importance of understanding trip setpoints, applies a new theoretic approach for comparing architectures, identifies cyber security controls that should be addressed early in system design, and describes ongoing work to develop system requirements and hardware/software configurations
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