Imperial College London

DrPetarKormushev

Faculty of EngineeringDyson School of Design Engineering

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 9235p.kormushev Website

 
 
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Location

 

25 Exhibition Road, 3rd floor, Dyson BuildingDyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Cursi:2020,
author = {Cursi, F and Modugno, V and Kormushev, P},
title = {Model predictive control for a tendon-driven surgical robot with safety constraints in kinematics and dynamics},
url = {http://kormushev.com/papers/Cursi_IROS-2020.pdf},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety andaccuracy of the surgical task. Mechanical designs and actuationschemes have inevitable limitations such as backlash and jointlimits. Moreover, surgical robots need to operate in narrowpathways, which may give rise to additional environmentalconstraints. Therefore, the control strategies must be capableof satisfying the desired motion trajectories and the imposedconstraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem bytaking into consideration the evolution of the system states, costfunction, and constraints over time. The high nonlinearities intendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a modellearning approach for the dynamics of tendon-driven robots.The dynamic model is then employed to impose constraintson the torques of the robot under consideration and solve anoptimal constrained control problem for trajectory trackingby using MPC. To assess the capabilities of the proposedframework, both simulated and real world experiments havebeen conducted
AU - Cursi,F
AU - Modugno,V
AU - Kormushev,P
PY - 2020///
TI - Model predictive control for a tendon-driven surgical robot with safety constraints in kinematics and dynamics
UR - http://kormushev.com/papers/Cursi_IROS-2020.pdf
UR - http://hdl.handle.net/10044/1/80783
ER -