NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

The emergence of active perception - seeking conceptual foundations

The aim of this thesis is to explain the emergence of active perception. It takes an interdisciplinary approach, by providing the necessary conceptual foundations for active perception research - the key notions that bridge the conceptual gaps remaining in understanding emergent behaviours of active perception in the context of robotic implementations. On the one hand, the autonomous agent approach to mobile robotics claims that perception is active. On the other hand, while explanations of emergence have been extensively pursued in Artificial Life, these explanations have not yet successfully accounted for active perception.The main question dealt with in this thesis is how active perception systems, as behaviour -based autonomous systems, are capable of providing relatively optimal perceptual guidance in response to environmental challenges, which are somewhat unpredictable. The answer is: task -level emergence on grounds of complicatedly combined computational strategies, but this notion needs further explanation.To study the computational strategies undertaken in active perception re- search, the thesis surveys twelve implementations. On the basis of the surveyed implementations, discussions in this thesis show that the perceptual task executed in support of bodily actions does not arise from the intentionality of a homuncu- lus, but is identified automatically on the basis of the dynamic small mod- ules of particular robotic architectures. The identified tasks are accomplished by quasi -functional modules and quasi- action modules, which maintain transformations of perceptual inputs, compute critical variables, and provide guidance of sensory -motor movements to the most relevant positions for fetching further needed information. Given the nature of these modules, active perception emerges in a different fashion from the global behaviour seen in other autonomous agent research.The quasi- functional modules and quasi- action modules cooperate by estimating the internal cohesion of various sources of information in support of the envisaged task. Specifically, such modules basically reflect various computational facilities for a species to single out the most important characteristics of its ecological niche. These facilities help to achieve internal cohesion, by maintaining a stepwise evaluation over the previously computed information, the required task, and the most relevant features presented in the environment.Apart from the above exposition of active perception, the process of task - level emergence is understood with certain principles extracted from four models of life origin. First, the fundamental structure of active perception is identified as the stepwise computation. Second, stepwise computation is promoted from baseline to elaborate patterns, i.e. from a simple system to a combinatory system. Third, a core requirement for all stepwise computational processes is the comparison between collected and needed information in order to insure the contribution to the required task. Interestingly, this point indicates that active perception has an inherent pragmatist dimension.The understanding of emergence in the present thesis goes beyond the distinc- tion between external processes and internal representations, which some current philosophers argue is required to explain emergence. The additional factors are links of various knowledge sources, in which the role of conceptual foundations is two -fold. On the one hand, those conceptual foundations elucidate how various knowledge sources can be linked. On the other, they make possible an interdisci- plinary view of emergence. Given this two -fold role, this thesis shows the unity of task -level emergence. Thus, the thesis demonstrates a cooperation between sci- ence and philosophy for the purpose of understanding the integrity of emergent cognitive phenomena

Layered control architectures in robots and vertebrates

We revieiv recent research in robotics, neuroscience, evolutionary neurobiology, and ethology with the aim of highlighting some points of agreement and convergence. Specifically, we com pare Brooks' (1986) subsumption architecture for robot control with research in neuroscience demonstrating layered control systems in vertebrate brains, and with research in ethology that emphasizes the decomposition of control into multiple, intertwined behavior systems. From this perspective we then describe interesting parallels between the subsumption architecture and the natural layered behavior system that determines defense reactions in the rat. We then consider the action selection problem for robots and vertebrates and argue that, in addition to subsumption- like conflict resolution mechanisms, the vertebrate nervous system employs specialized selection mechanisms located in a group of central brain structures termed the basal ganglia. We suggest that similar specialized switching mechanisms might be employed in layered robot control archi tectures to provide effective and flexible action selection

Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

The design and intelligent control of an autonomous mobile robot

This thesis presents an investigation into the problems of exploration, map building and collision free navigation for intelligent autonomous mobile robots. The project began with an extensive review of currently available literature in the field of mobile robot research, which included intelligent control techniques and their application. It became clear that there was scope for further development with regard to map building and exploration in new and unstructured environments. Animals have an innate propensity to exhibit such abilities, and so the analogous use of artificial neural networks instead of actual neural systems was examined for use as a method of robot mapping. A simulated behaviour based mobile robot was used in conjunction with a growing cell structure neural network to map out new environments. When using the direct application of this algorithm, topological irregularities were observed to be the direct result of correlations within the input data stream. A modification to this basic system was shown to correct the problem, but further developments would be required to produce a generic solution. The mapping algorithms gained through this approach, although more similar to biological systems, are computationally inefficient in comparison to the methods which were subsequently developed. A novel mapping method was proposed based on the robot creating new location vectors, or nodes, when it exceeded a distance threshold from its mapped area. Network parameters were developed to monitor the state of growth of the network and aid the robot search process. In simulation, the combination of the novel mapping and search process were shown to be able to construct maps which could be subsequently used for collision free navigation. To develop greater insights into the control problem and to validate the simulation work the control structures were ported to a prototype mobile robot. The mobile robot was of circular construction, with a synchro-drive wheel configuration, and was equipped with eight ultrasonic distance sensors and an odometric positioning system. It was self-sufficient, incorporating all its power and computational resources. The experiments observed the effects of odometric drift and demonstrated methods of re-correction which were shown to be effective. Both the novel mapping method, and a new algorithm based on an exhaustive mesh search, were shown to be able to explore different environments and subsequently achieve collision free navigation. This was shown in all cases by monitoring the estimates in the positional error which remained within fixed bounds

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion