In this paper, we present a control system for the continuous teleoperation of a robotic manipulator via brain- machine interface (BMI). The proposed solution is based on shared control approach that allows the user to only focus on the operational tasks, while the low-level control details are automatically handled by the robotic intelligence. The user drives the manipulator through the imagination of limb movements (both hands vs. both feet) and a parameterized mapping function is implemented to convert the continuous BMI outputs into robot velocity commands which are sent to the shared control framework. The latter consists in: (i) a target predictor module, to infer the most probable target objects from the sequence of BMI commands; (ii) a control module based on an improved version of artificial potential fields (APF) to assist the user in reaching the target while avoiding collisions with obstacles in the environment. The system has been tested with a sample subject in a tabletop reach-to-grasp experiment with multiple target objects and obstacles achieving a success rate of 80%. The proposed system could be used in the future to help people with severe motor disabilities in performing daily life operations, such as drinking, feeding or manipulating objects.

Continuous Teleoperation of a Robotic Manipulator via Brain-Machine Interface with Shared Control

Stefano Tortora
;
Alberto Gottardi;Emanuele Menegatti;Luca Tonin
2022

Abstract

In this paper, we present a control system for the continuous teleoperation of a robotic manipulator via brain- machine interface (BMI). The proposed solution is based on shared control approach that allows the user to only focus on the operational tasks, while the low-level control details are automatically handled by the robotic intelligence. The user drives the manipulator through the imagination of limb movements (both hands vs. both feet) and a parameterized mapping function is implemented to convert the continuous BMI outputs into robot velocity commands which are sent to the shared control framework. The latter consists in: (i) a target predictor module, to infer the most probable target objects from the sequence of BMI commands; (ii) a control module based on an improved version of artificial potential fields (APF) to assist the user in reaching the target while avoiding collisions with obstacles in the environment. The system has been tested with a sample subject in a tabletop reach-to-grasp experiment with multiple target objects and obstacles achieving a success rate of 80%. The proposed system could be used in the future to help people with severe motor disabilities in performing daily life operations, such as drinking, feeding or manipulating objects.
2022 IEEE 27st international conference on emerging technologies and factory automation (ETFA)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3454845
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