In this section
Soft and flexible robotic systems for affordable healthcare.
Our research investigates fundamental aspects of control of soft and flexible robots for surgery. These include harnessing the intrinsic compliance of soft robots, rejecting disturbances that characterise the surgical environment, and complying with stringent safety requirements. Our ambition is to provide affordable robotic solutions for a range of surgical applications, including endoscopy, percutaneous intervention, and multi-handed surgery.
Robotics for healthcare is one of the fastest growing segments in the global robotics market. However, conventional surgical robots are unaffordable in low-resource settings. Harnessing the potential of soft and flexible robots can contribute to making surgery safter, more accurate, and more accessible in low-middle income countries. These are pressing needs due to the aging population, and to the growing workforce crisis in the healthcare market.
Our work aims to improve accuracy, reduce the risk of injury, and reduce discomfort in percutaneous interventions such as biopsy, in diagnostic and interventional endoscopy, and in multi-handed surgery.
Dr Enrico Franco
Lecturer
@article{Garriga-Casanovas:2024:10.3390/act13070242,
author = {Garriga-Casanovas, A and Shakib, F and Ferrandy, V and Franco, E},
doi = {10.3390/act13070242},
journal = {Actuators},
title = {Hybrid Control of Soft Robotic Manipulator},
url = {http://dx.doi.org/10.3390/act13070242},
volume = {13},
year = {2024}
}
TY - JOUR
AB - Soft robotic manipulators consisting of serially stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for potential applications that may include endoluminal surgery and inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with closed-loop control to compensate for the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with six inputs and four outputs.
AU - Garriga-Casanovas,A
AU - Shakib,F
AU - Ferrandy,V
AU - Franco,E
DO - 10.3390/act13070242
PY - 2024///
TI - Hybrid Control of Soft Robotic Manipulator
T2 - Actuators
UR - http://dx.doi.org/10.3390/act13070242
VL - 13
ER -
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