372 research outputs found

    Advances in Robotics, Automation and Control

    Get PDF
    The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

    Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

    Get PDF
    Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

    Curiosity-Based Learning Algorithm for Interactive Art Sculptures

    Get PDF
    This thesis is part of the research activities of the Living Architecture System Group (LASG). Combining techniques in architecture, the arts, electronics, and software, LASG develops interactive art sculptures that engage occupants in an immersive environment. The overarching goal of this research is to develop architectural systems that possess life-like qualities. Recent advances in miniaturization of computing and sensing units enable system-wide responsive behaviours. Though complexity may emerge in current LASG systems through superposition of a set of simple and prescripted behaviours, the responses of the systems to occupants remain rather robotic and ultimately dictated by the will of the designers. In this thesis, a new series of sculptural system was initiated, implementing an additional layer of behavioural autonomy. In this thesis, the Curiosity-Based Learning Algorithm (CBLA), a reinforcement learning algorithm which selects actions that lead to maximum potential knowledge gains, is introduced to enable the sculpture to automatically generate interactive behaviours and adapt to changes. The CBLA allows the sculptural system to construct models of its own mechanisms and its surroundings through self-experimentation and interaction with human occupants. A novel formulation using multiple learning agents, each comprising a subset of the system, was developed in order to integrate a large number of sensors and actuators. These agents form a network of independent, asynchronous CBLA Nodes that share information about localized events through shared sensors and virtual inputs. Given different network configurations of the CBLA system, the emergence of system behaviours with varying activation patterns was observed. To realize the CBLA system on a physical interactive art sculpture, an overhaul of the previous series' interactive control hardware was necessary. CBLA requires the system to be able to sense the consequences of its own actions and its surrounding at a much higher resolution and frequency than previously implemented behaviour algorithms. This translates to the need to interface and collect samples from a substantially larger number of sensors. A new series of hardware as well as control system software was developed, which enables the control and sampling of hundreds of devices on a centralized computer through USB connections. Moving the computation from an embedded platform simplifies the implementation of the CBLA system, which is a computationally intensive and complex program. In addition, the large amount of data generated by the system can now be recorded without sacrificing response time nor resolution. An experimental test bed was built to validate the behaviours of the CBLA system. This small-scale interactive art sculpture resembles previous sculptures displayed publicly by the LASG and Philip Beesley Architect Inc (PBAI). Experiments were done on the testbed at PBAI's Toronto studios, to demonstrate the exploratory patterns of CBLA as well as the collective learning behaviours produced by the CBLA system. Furthermore, a user study was conducted to better understand users' responses to this new form of interactive behaviour. Comparing with prescripted behaviours that were explicitly programmed, the participants of the study did not find this implementation of the CBLA system more interesting. However, the positive correlations between activation level, responsiveness, and users' interest levels revealed insights about users' preferences and perceptions of the system. In addition, observations during the trials and the responses from the questionnaires showed a wide variety of user behaviours and expectations. This suggests that, in future work, results should be categorized to analyze how different types of users respond to the sculpture. Moreover, the experiments should also be designed to better reflect the actual use cases of the sculpture

    Ecological adaptation in the context of an actor-critic

    Get PDF
    Biological beings are the result of an evolutionary and developmental process of adaptation to the environment they perceive and where they act. Animals and plants have successfully adapted to a large variety of environments, which supports the ideal of inspiring artificial agents after biology and ethology. This idea has been already suggested by previous studies and is extended throughout this thesis. However, the role of perception in the process of adaptation and its integration in an agent capable of acting for survival is not clear.Robotic architectures in AI proposed throughout the last decade have broadly addressed the problems of behaviour selection, namely deciding "what to do next", and of learning as the two main adaptive processes. Behaviour selection has been commonly related to theories of motivation, and learning has been bound to theories of reinforcement. However, the formulation of a general theory including both processes as particular cases of the same phenomenon is still an incomplete task. This thesis focuses again on behaviour selection and learning; however it proposes to integrate both processes by stressing the ecological relationship between the agent and its environment. If the selection of behaviour is an expression of the agent's motivations, the feedback of the environment due to behaviour execution can be viewed as part of the same process, since it also influences the agent's internal motivations and the learning processes via reinforcement. I relate this to an argument supporting the existence of a common neural substrate to compute motivation and reward, and therefore relating the elicitation of a behaviour to the perception of reward resulting from its executionAs in previous studies, behaviour selection is viewed as a competition among parallel pathways to gain control over the agent's actuators. Unlike for the previous cases, the computation of every motivation in this thesis is not anymore the result of an additive or multiplicative formula combining inner and outer stimuli. Instead, the ecological principle is proposed to constrain the combination of stimuli in a novel fashion that leads to adaptive behavioural patterns. This method aims at overcoming the intrinsic limitations of any formula, the use of which results in behavioural responses restricted to a set of specific patterns, and therefore to the set of ethological cases they can justify. External stimuli and internal physiology in the model introduced in this thesis are not combined a priori. Instead, these are viewed from the perspective of the agent as modulatory elements biasing the selection of one behaviour over another guided by the reward provided by the environment, being the selection performed by an actor-critic reinforcement learning algorithm aiming at the maximum cumulative reward.In this context, the agent's drives are the expression of the deficit or excess of internal resources and the reference of the agent to define its relationship with the environment. The schema to learn object affordances is integrated in an actor-critic reinforcement learning algorithm, which is the core of a motivation and reinforcement framework driving behaviour selection and learning. Its working principle is based on the capacity of perceiving changes in the environment via internal hormonal responses and of modifying the agent's behavioural patterns accordingly. To this end, the concept of reward is defined in the framework of the agent's internal physiology and is related to the condition of physiological stability introduced by Ashby, and supported by Dawkins and Meyer as a requirement for survival. In this light, the definition of the reward used for learning is defined in the physiological state, where the effect of interacting with the environment can be quantified in an ethologically consistent manner.The above ideas on motivation, behaviour selection, learning and perception have been made explicit in an architecture integrated in an simulated robotic platform. To demonstrate the reach of their validity, extensive simulation has been performed to address the affordance learning paradigm and the adaptation offered by the framework of the actor-critic. To this end, three different metrics have been proposed to measure the effect of external and internal perception on the learning and behaviour selection processes: the performance in terms of flexibility of adaptation, the physiological stability and the cycles of behaviour execution at every situation. In addition to this, the thesis has begun to frame the integration of behaviours of an appetitive and consummatory nature in a single schema. Finally, it also contributes to the arguments disambiguating the role of dopamine as a neurotransmitter in the Basal Ganglia

    Defacement Detection with Passive Adversaries

    Get PDF
    A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector’s alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier’s behaviour. We achieve this goal by introducing a secret key—a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm’s hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company

    Intelligent Sensors for Human Motion Analysis

    Get PDF
    The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

    Reinforcement Learning

    Get PDF
    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

    Virtual Reality Games for Motor Rehabilitation

    Get PDF
    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
    • …
    corecore