What type of study is this?

September 10, 2025Open Access

EMG-Driven Shared Control Architecture for Human–Robot Co-Manipulation Tasks

FPFrancesca PatriarcaUniversity of Udine PLPaolo Di LilloUniversità degli studi di Cassino e del Lazio Meridionale FAFilippo ArrichielloUniversità degli studi di Cassino e del Lazio Meridionale

Key Points

The proposed control strategy enables users to guide a robotic manipulator effectively, indicating it can increase task efficiency.
Electromyographic signals classified using SVM allow for mode changes in robotic control, enhancing adaptability during tasks.
A Kinova Jaco2 manipulator with EMG sensors was tested, showing practical applications with a limited user group.
Results highlight the potential for improved human-robot collaboration in various environments and tasks.

Abstract

The paper presents a shared control strategy that allows a human operator to physically guide the end-effector of a robotic manipulator to perform different tasks, possibly in interaction with the environment. To switch among different operational modes referring to a finite state machine algorithm, ElectroMyoGraphic (EMG) signals from the user’s arm are used to detect muscular contractions and to interact with a variable admittance control strategy. Specifically, a Support Vector Machine (SVM) classifier processes the raw EMG data to identify three classes of contractions that trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco2 manipulator, equipped with force/torque sensor at the end-effector, and with a limited group of users wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb, demonstrating promising results.

Ask AI

Helpful

Bookmark

View Full Paper