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About

Angelica Ginnante is a robotics researcher whose work centers on the design, optimization, and control of kinematic redundant manipulators for advanced manufacturing. Her major contributions lie in developing computational methods to enhance the performance of machining robots, addressing critical challenges in stiffness, accuracy, and workspace determination. Her most cited work, "Task priority based design optimization of a kinematic redundant robot" (2023, 10 citations), introduces a novel framework for optimizing robot design by prioritizing multiple task objectives, a key advancement for industrial applications requiring high precision. She further refined workspace analysis with her "Optimized Ray-Based Method for Workspace Determination of Kinematic Redundant Manipulators" (2024), offering a computationally efficient alternative to traditional Monte Carlo approaches. Ginnante’s innovative "Design and Kinematic Analysis of a Novel 2-DoF Closed-Loop Mechanism for the Actuation of Machining Robots" (2021) proposes a hybrid mechanism to improve stiffness in serial robots, bridging the gap between CNC machine accuracy and robotic flexibility. Her "Kinetostatic Optimization for Kinematic Redundancy Planning of Nimbl’Bot Robot" (2023) demonstrates practical application in machining tasks. With a growing citation impact, Ginnante’s work is shaping the future of redundant robotics in manufacturing.