Conference Papers

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    Development of Pneumatic Soft Gripper for Effective Material Handling
    (Institute of Electrical and Electronics Engineers Inc., 2024) Naveen, S.; Panigrahi, S.; Vinit, A.; Sudar, I.H.; Thomas, M.J.
    Recent times have shown a drastic transition in robotics from rigid to soft mechanisms. The reason is that soft robots offer safer human-robot interactions, reduce weight and also offer strong environmental adaptability. However, the inherent characteristics of soft materials to exhibit multiple degrees of freedom (DoF) make it challenging to control their movements. Therefore, this paper presents the development of a soft gripper with simple architecture to effectively and economically manipulate delicate objects. The gripper constructed using silicon rubber has an air cavity to actuate its opening and closing pneumatically. This paper presents the different stages of its construction and demonstrates its application on a 4 DoF robotic arm for handling delicate objects. A simulation study of the structural parameters of the proposed soft gripper is also carried out using ANSYS finite element software. The preliminary results show the superior adaptability of the soft gripper in handling objects of various shapes and sizes. The proposed system can find application in food, biomedical and electronic industries. © 2024 IEEE.
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    Gesture Prediction Using Surface-EMG Signals
    (Springer Science and Business Media Deutschland GmbH, 2025) Panigrahi, S.; Seal, S.; Lal, S.; Naik, G.
    Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.