A New 2-DOF Dual-Double Tendon-Driven Two-Finger Gripper Design, Analysis, and Performance Evaluation

<p dir="ltr">Robotic dexterity is critical for applications in prosthetics, assistive devices, and industrial automation. Conventional single-DOF grippers lack sufficient joint coordination for precise force distribution, motivating the development of a two-degree-of-freedom (2-DOF) tendon-driven design capable of more human-like articulation. A 2-DOF architecture enables coordinated multi-joint motion and controlled distribution of contact forces, extending functionality beyond the limitations of single-DOF mechanisms and addressing the broader challenge of dexterous manipulation in robotics.</p><p dir="ltr">This thesis presents the design, implementation, and evaluation of a dual double-tendon, two-finger gripper. A predictive force actuation framework is introduced, combining calibrated load measurements with waveform-derived force estimation to characterise tendon transmission behaviour. A layered control architecture links embedded firmware for real-time acquisition, Python middleware for synchronised logging, and a MATLAB toolchain for kinematic modelling, motion tracking, and fingertip position computation. The analytical mechanism model maps MCP and PIP joint inputs to fingertip coordinates, supporting theoretical validation and trajectory prediction.</p><p dir="ltr">Mechanically, the gripper design incorporates press fit bearings, refined tendon routing, an improved four-bar linkage, and a re-engineered servo spool geometry to reduce frictional losses, backlash, and hysteresis. Free-body diagram analyses and closed-form kinematics yield fingertip force predictions that show strong alignment with experiments using calibrated loads. Position estimation is enhanced through MATLAB-based edge detection integrated with Kalman-filtered IMU data. Comparative benchmarking demonstrates improved fingertip force output relative to Unde et al. (2023) under matched test conditions.</p><p dir="ltr">The research contributes a compact and efficient tendon-driven platform supported by systematic analytical, experimental, and comparative evaluation. Beyond establishing a validated design framework, this work highlights pathways for future development, including refined nonlinear models, adaptive closed-loop control, and perception-informed grasp planning through vision and machine learning integration.</p>