loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Diego Bianchi 1 ; 2 ; Michele Antonelli 3 ; Cecilia Laschi 4 ; Angelo Sabatini 1 ; 2 and Egidio Falotico 1 ; 2

Affiliations: 1 The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ; 2 Departement of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy ; 3 Department of Industrial and Information Engineering and Economics, University of L’Aquila, L’Aquila, Italy ; 4 Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

Keyword(s): Soft Robotics, Throwing, Open-Loop Control, Neural Network, Ballistic Task.

Abstract: Controlling a soft robot poses a challenge due to its mechanical characteristics. Although the manufacturing process is well-established, there are still shortcomings in their control, which often limits them to static tasks. In this study, we aim to address some of these limitations by introducing a neural network-based controller specifically designed for the throwing task using a soft robotic arm. Drawing inspiration from previous research, we have devised a method for controlling the movement of the soft robotic arm during the ballistic task. By employing a feed-forward neural network, we approximate the relationship between the actuation pattern and the resulting landing position. This enables us to predict the input sequence that needs to be transmitted to the robot’s actuators based on the desired landing coordinates. To validate our approach, we conducted experiments using a 2-module soft robotic arm, which was utilized to throw four different objects towards ten target boxes positioned beneath the robot. We considered two actuation modalities, depending on whether the distal module was activated. The results indicate a success rate, defined as the proportion of successful trials out of the total number of throws, of up to 68% when a single module was actuated. These findings demonstrate the potential of our proposed controller in achieving successful performance of the throwing task using a soft robotic arm. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.220.251.236

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bianchi, D.; Antonelli, M.; Laschi, C.; Sabatini, A. and Falotico, E. (2023). Learning-Based Inverse Dynamic Controller for Throwing Tasks with a Soft Robotic Arm. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5; ISSN 2184-2809, SciTePress, pages 424-432. DOI: 10.5220/0012184200003543

@conference{icinco23,
author={Diego Bianchi. and Michele Antonelli. and Cecilia Laschi. and Angelo Sabatini. and Egidio Falotico.},
title={Learning-Based Inverse Dynamic Controller for Throwing Tasks with a Soft Robotic Arm},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={424-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012184200003543},
isbn={978-989-758-670-5},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Learning-Based Inverse Dynamic Controller for Throwing Tasks with a Soft Robotic Arm
SN - 978-989-758-670-5
IS - 2184-2809
AU - Bianchi, D.
AU - Antonelli, M.
AU - Laschi, C.
AU - Sabatini, A.
AU - Falotico, E.
PY - 2023
SP - 424
EP - 432
DO - 10.5220/0012184200003543
PB - SciTePress