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.
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
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)
- Soft Robotics
- Artificial Intelligence
- Reinforcement Learning