Mustafa Mahmoud Mohammed Hassan Elsayed
Demonstrator
Development and Optimization of a Cost-Effective Lightweight Humanoid Robotic Arm for Assistive Applications
Engineering research journal • 2024
Publication Information
Authors
Elsayed, M. M., Elsamanty, M., & Selmy, M.
Keywords
Robotics Arm, Assistive Devices, Humanoid Robotic, 3D Printing,
Motion Planning, System Identification, and PID control.
Journal
Engineering research journal
Publisher
Not Available
Volume
183
Issue
3
Pages
Not Available
publication.type
Local
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
This paper presents the development of a cost-effective, lightweight humanoid
robotic arm designed to assist the elderly and vulnerable populations. The project
aims to provide specialized robotic arms by utilizing a motion planning method based
on human arm biomechanics. The arm, created using 3D printing technology with
40% infill, achieves a weight reduction of over 60%. Low-torque servos and a
human-like adaptable gripper further enhance cost efficiency and functionality. The
arm features simplified joints and is driven by six modified R/C servomotors with
analog feedback for precise angle measurement. System identification shows high
accuracy, with joint fit percentages ranging from 87.5% to 97.07%. A PID controller,
optimized via Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and
Honey Badger Algorithm (HBA), ensures rapid and accurate positioning. The
Simscape library models the arm's dynamic behavior, addressing forward and inverse
kinematics, workspace, and path planning. These innovations promise to advance
assistive technologies significantly.
robotic arm designed to assist the elderly and vulnerable populations. The project
aims to provide specialized robotic arms by utilizing a motion planning method based
on human arm biomechanics. The arm, created using 3D printing technology with
40% infill, achieves a weight reduction of over 60%. Low-torque servos and a
human-like adaptable gripper further enhance cost efficiency and functionality. The
arm features simplified joints and is driven by six modified R/C servomotors with
analog feedback for precise angle measurement. System identification shows high
accuracy, with joint fit percentages ranging from 87.5% to 97.07%. A PID controller,
optimized via Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and
Honey Badger Algorithm (HBA), ensures rapid and accurate positioning. The
Simscape library models the arm's dynamic behavior, addressing forward and inverse
kinematics, workspace, and path planning. These innovations promise to advance
assistive technologies significantly.