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{Caulcrick:2021:10.1109/TMRB.2021.3105141,
author = {Caulcrick, C and Huo, W and Franco, E and Mohammed, S and Hoult, W and Vaidyanathan, R},
doi = {10.1109/TMRB.2021.3105141},
journal = {IEEE Transactions on Medical Robotics and Bionics},
pages = {980--991},
title = {Model predictive control for human-centred lower limb robotic assistance},
url = {http://dx.doi.org/10.1109/TMRB.2021.3105141},
volume = {3},
year = {2021}
}
TY - JOUR
AB - Loss of mobility and/or balance resulting from neural trauma is a critical public health issue. Robotic exoskeletons hold great potential for rehabilitation and assisted movement. However, the synergy of robot operation with human effort remains a problem. In particular, optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons that achieves on-the-fly transitions between modes of assistance. The architecture implements a fuzzy logic algorithm (FLA) to map key modes of assistance based on human involvement. Three modes are utilised: passive, for human relaxed and robot dominant; active-assist, for human cooperation with the task; and safety, in the case of human resistance to the robot. Electromyography (EMG) signals are further employed to predict the human torque. EMG output is used by the MPC for trajectory following and by the FLA for decision making. Experimental validation using a 1-DOF knee exoskeleton demonstrates the controller tracking a sinusoidal trajectory with relaxed, assistive, and resistive operational modes. Results demonstrate rapid and appropriate transfers among the assistance modes, and satisfactory AAN performance in each case, offering a new level of human-robot synergy for mobility assist and rehabilitation.
AU - Caulcrick,C
AU - Huo,W
AU - Franco,E
AU - Mohammed,S
AU - Hoult,W
AU - Vaidyanathan,R
DO - 10.1109/TMRB.2021.3105141
EP - 991
PY - 2021///
SN - 2576-3202
SP - 980
TI - Model predictive control for human-centred lower limb robotic assistance
T2 - IEEE Transactions on Medical Robotics and Bionics
UR - http://dx.doi.org/10.1109/TMRB.2021.3105141
UR - http://arxiv.org/abs/2011.05079v1
UR - https://ieeexplore.ieee.org/document/9514611
UR - http://hdl.handle.net/10044/1/94556
VL - 3
ER -
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