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p-Index From 2021 - 2026
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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Engineering, Science and Information Technology
Nidhi Mishra
Kalinga University
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Soft Robotics with Quantum-Driven Electronic Neural Networks F. Rahman; Nidhi Mishra; Bhumika Bansal
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1391

Abstract

Although this field attracts lots of attention, conventional control mechanisms of Soft Robotics are still restricted in real-time decision making, learning efficiency, and energy consumption. This research further strengthens soft robotic intelligence to present a novel Quantum Driven Electronic Neural Network (QD-ENN) framework based on Design of Reservoir Computing (QRC) to be used for development of Brain of the Offspring (BO) and contextual entangled processing (CEPT) nodes. Quantum superposition and entanglement make it intrinsically superior to sensorimotor learning, low power computation, and rapid adaptation of the sensorimotor interaction in an unstructured environment. Compared with classical deep learning methods that require huge quantities of training and computations to learn, the proposed system solves real-time control problem and changes morphologies of soft actuators dynamically using quantum inspired neural plasticity. Based on the design of the architecture, which is implemented for neuromorphic processing using memristor electronic synapses and based on quantum circuits to help with reinforcement learning, the architecture designed employs quantum circuits and memristor electronic synapses. Experimental evaluations also demonstrate excellent speed up in terms of learning speed, decision accuracy and energy efficiency compared to the traditional AI-driven soft robotic controllers. Based on this work, future research on quantum neuromorphic architectures in robotics follows by building semiconductor hardware towards self-learning robotics of exceptionally dynamical and unpredictable nature.
Human state digital twin architecture for physiology constrained adaptive robotic autonomy Jaganathan Nirmaladevi; S. Saranya; Nidhi Mishra; Fazal Noorbasha; Manoharan Kavitha
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp503-518

Abstract

Time-dependent human cognitive and physical conditions should be considered in adaptive autonomy in collaborative robotics to ensure safety, performance, and operator comfort. But most of the current methods depend on heuristic levels of workload or individual physiological measures and fail to combine human-state estimation with the control structure. This paper suggests a human state digital twin (HSDT) framework of physiological-constrained adaptive robotic autonomy. This framework is a combination of multimodal physiological measures of electrocardiography-based heart-rate variability, electromyography, electrodermal activity, and task context to predict latent human states such as cognitive workload, fatigue, and stress. These estimates are included in a twin-constrained adaptive autonomy controller, which varies the control authority of the robot in real time. The simulated collaborative manipulation situation is tested in the proposed framework in different workload and fatigue conditions. It has demonstrated better tracking performance, less torque ripple, reduced operator stress indicators and less torque ripple. The paper presents a controlled and reconfigurable physiology-aware adaptive autonomy control architecture and a robotics-based digital twin control design framework of human-aware control design.
Co-Authors Bhumika Bansal F. Rahman Fazal Noorbasha Jaganathan Nirmaladevi Manoharan Kavitha S. Saranya