1,721,362 research outputs found

    FUZZY LOGIC FOR BUILDING REAL-TIME INTELLIGENT SYSTEMS

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    Master'sMASTER OF ENGINEERIN

    Shuzhi Sam Ge [People in Control]

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    Questioning Items’ Link in Users’ Perception of a Training Robot for Elders

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    Socially Assistive robots are becoming more common in modern society. These robots can accomplish a variety of tasks for people that are exposed to isolation and difficulties. Among those, elderly people are the largest part, and with them, robotics can play new roles. Elderly people are the ones who usually suffer a major technological gap, and it is worth evaluating their perception when dealing with robots. To this end, the present work addresses the interaction of elderly people during a training session with a humanoid robot. The analysis has been carried out by means of a questionnaire, using four key factors: Motivation, Usability, Likability, and Sociability. The results can contribute to the design and the development of social interaction between robots and humans in training contexts to enhance the effectiveness of human-robot interaction

    ADAPTIVE NEURAL NETWORK CONTROL OF FLEXIBLE ROBOTS

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    Master'sMASTER OF ENGINEERIN

    Using a Pneumatic Tactile Steering Wheel to Enhance the Multi-Modal Takeover Request In Smart Vehicle

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    Haptic interactions enhance user experiences on smart devices. As smart vehicles advance, In-Vehicle Interaction (IVI) has gained focus, especially multi-modal interactions for safe and trusted automation. This paper introduces a pneumatic Human-Vehicle Interaction (HVI) interface integrated into a steering wheel to improve driver-vehicle communication via tactile feedback. A Logitech G27 steering wheel was modified with Pneumatic Tactile Silicon Sac (PTSS) to convey warnings through air cell adjustments. Our study covers the development of the Pneumatic Tactile Steering Wheel and a Multi-Modal HVI system with visual, audio, and haptic elements for seamless transitions from autonomous to manual driving. Simulation results show the Visual-Audio-Haptic takeover request (TOR) improves driver takeover performance and trust in L2 autonomous vehicles (AVs)
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