Robots are expected to be pervasive in the society in
a not too distant future where they will work extensively
as assistants of humans in various activities. With this in
view, a novel affect-sensitive architecture for human-robot cooperation is presented
in this paper where the robot is expected to recognize
human psychological states. As a demonstration, an online heart rate
variability analysis to infer the mental stress of a human
engaged in a task is presented. This technique involves real-time
heart rate monitoring, signal processing using both Fourier Transforrn and
Wavelet Transform, and inferring the stress condition based on the
level of activation of the sympathetic and parasympathetic nervous systems
using fuzzy logic. Results from human subject trials are presented
to validate the presented methodology. This stress detection technique is
expected to be useful in the future human-robot cooperation activities,
where the robot will recognize human stress and respond appropriately.
Robot selection is one of critical decisions in the design of robotic workcells. Over the last ten years, many Case-Based Reasoning (CBR) systems have been developed to solve decision making problems successfully. We propose to develop three sort systems: browsing systems, preference-based selection organizers, and alternative suggestion agents. All four stages of the CBR cycle are designed to assist robotic application designers to go through robot selection and decision-making. A case-based reasoning approach is employed to solve new robot selection decision problems by adapting solutions that were used to solve previous robot selection problems. In this study, CBR has shown that it has several advantages over other techniques. The results of this study will help robot workcell designers to develop a more efficient and effective method to select robots for specific robot applications.
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