EvoGrip: design and model predictive control of a modular, cost-efficient robot hand for research applications

This thesis presents the design and implementation of EvoGrip, a modular 3D-printed robotic hand developed as an open platform for advanced control experimentation. Leveraging open-source designs, EvoGrip enhances accessibility, adaptability, and dexterity. Its mechanical structure, derived from the Inmoov arm, was refined to incorporate position and force sensing with improved finger mechanics for precise and reliable performance. Two modeling strategies and a dual control scheme using Model Predictive Control (MPC) were proposed and experimentally validated. The system achieved accurate finger trajectories and effective disturbance rejection, demonstrating the potential of MPC for robotic hand control. Although some reactive behavior was observed in multi-finger scenarios, decentralized controllers showed promising results, indicating potential for more advanced control strategies. This work establishes a foundation for future extensions, including multi-input, multi-output MPC and reinforcement learning to enhance coordination and robustness. EvoGrip offers a versatile research platform for developing innovative control techniques in humanoid robotic hands.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Mechatronics Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Mohsini, Shahram

Advisor dc:contributor.advisor

• Charest-Finn, Meaghan

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1991

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1991