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Samuel Mohsen Yousef

Research Assistant

Faculty Building

UB2

Office Number

S12B

Biography

Samuel is a research assistant at the Smart Engineering Systems Research Center (SESC) and a master’s student in the mechatronics engineering program at Nile University. He received his BSc degree in mechatronic engineering from the Arab Academy for Science, Technology, and Maritime Transport (AASTMT) in 2017. He’s currently working as part of the bio-hybrid soft robotics lab on underwater soft robots and his current research interests are the modeling, control, and perception for robots, building biomimetic robots, and applying artificial intelligence for robot cognition.

Recent Publications

Design and control of soft biomimetic pangasius fish robot using fin ray effect and reinforcement learning

Soft robots provide a pathway to accurately mimic biological creatures and be integrated into their environment with minimal invasion or disruption to their ecosystem. These robots made from soft deforming materials possess structural properties and behaviors similar to the bodies and organs of living creatures. However, they are difficult to develop in terms of integrated actuation and sensing

Artificial Intelligence
Circuit Theory and Applications
Mechanical Design

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Nature and biological creatures are some of the main sources of inspiration for humans. Engineers have aspired to emulate these natural systems. As rigid systems become increasingly limited in their capabilities to perform complex tasks and adapt to their environment like living creatures, the need for soft systems has become more prominent due to the similar complex, compliant, and flexible

Mechanical Design

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN)

Circuit Theory and Applications
Research Tracks
  • Soft Robotics
  • Artificial Intelligence
  • Reinforcement Learning