Control Design for Soft Robots Based on Reduced-Order Model

Inspired by nature, soft robots promise disruptive advances in robotics. Soft robots are naturally compliant and exhibit nonlinear behavior, which makes their study challenging. No unified framework exists to control these robots, especially when considering their dynamics. This letter proposes a methodology to study this type of robots around a stable equilibrium point. It can make the robot converge faster and with reduced oscillations to a desired equilibrium state. Using computational mechanics, a large-scale dynamic model of the robot is obtained and model reduction algorithms enable the design of a low-order controller and observer. A real robot is used to demonstrate the interest of the results.

[1]  C. Walsh,et al.  Biomechanical and Physiological Evaluation of Multi-Joint Assistance With Soft Exosuits , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  A. Cohen,et al.  Model Reduction and Approximation: Theory and Algorithms , 2017 .

[3]  Francisco Chinesta,et al.  Reduced-order modeling of soft robots , 2018, PloS one.

[4]  Cecilia Laschi,et al.  Learning dynamic models for open loop predictive control of soft robotic manipulators. , 2017, Bioinspiration & biomimetics.

[5]  Cecilia Laschi,et al.  Soft robotics: a bioinspired evolution in robotics. , 2013, Trends in biotechnology.

[6]  Ebrahim Mattar,et al.  Robotics and Autonomous Systems a Survey of Bio-inspired Robotics Hands Implementation: New Directions in Dexterous Manipulation , 2022 .

[7]  Robert J. Webster,et al.  Design and Kinematic Modeling of Constant Curvature Continuum Robots: A Review , 2010, Int. J. Robotics Res..

[8]  Ian D. Walker,et al.  Continuous Backbone “Continuum” Robot Manipulators , 2013 .

[9]  Jérémie Dequidt,et al.  Kinematic modeling and observer based control of soft robot using real-time Finite Element Method , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Kaspar Althoefer,et al.  Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation , 2017, Soft robotics.

[11]  C. Majidi.  Soft Robotics: A Perspective—Current Trends and Prospects for the Future , 2014 .

[12]  Jérémie Dequidt,et al.  Software toolkit for modeling, simulation, and control of soft robots , 2017, Adv. Robotics.

[13]  Leonardo Cappello,et al.  A soft wearable robot for the shoulder: Design, characterization, and preliminary testing , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[14]  Lakmal Seneviratne,et al.  Discrete Cosserat Approach for Multisection Soft Manipulator Dynamics , 2017, IEEE Transactions on Robotics.

[15]  Matteo Cianchetti,et al.  Soft Robotics: New Perspectives for Robot Bodyware and Control , 2014, Front. Bioeng. Biotechnol..

[16]  Thor Morales Bieze.  Contribution to the kinematic modeling and control of soft manipulators using computational mechanics. (Contribution à la modélisation cinématique et au contrôle de manipulateurs déformables, fondée sur la mécanique numérique) , 2017 .

[17]  S. M. Hadi Sadati,et al.  Control Space Reduction and Real-Time Accurate Modeling of Continuum Manipulators Using Ritz and Ritz–Galerkin Methods , 2018, IEEE Robotics and Automation Letters.

[18]  Christian Duriez,et al.  Reduced Order Control of Soft Robots with Guaranteed Stability , 2018, 2018 European Control Conference (ECC).

[19]  Annika Raatz,et al.  A framework for the automated design and modelling of soft robotic systems , 2017 .

[20]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[21]  F. Pigula,et al.  The use of soft robotics in cardiovascular therapy , 2017, Expert review of cardiovascular therapy.

[22]  MajidiCarmel,et al.  Soft Robotics: A Perspective—Current Trends and Prospects for the Future , 2014 .

[23]  LipsonHod,et al.  Challenges and Opportunities for Design, Simulation, and Fabrication of Soft Robots , 2014 .

[24]  Cecilia Laschi,et al.  Control Strategies for Soft Robotic Manipulators: A Survey. , 2018, Soft robotics.

[25]  J WalshConor,et al.  An Implantable Extracardiac Soft Robotic Device for the Failing Heart: Mechanical Coupling and Synchronization. , 2017 .

[26]  David Swann,et al.  Challenges and opportunities for design , 2017 .

[27]  Paolo Dario,et al.  Soft Robot Arm Inspired by the Octopus , 2012, Adv. Robotics.

[28]  Christian Duriez,et al.  Control of elastic soft robots based on real-time finite element method , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  Ian D. Walker,et al.  OctArm - A soft robotic manipulator , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Yizhar Or,et al.  Interaction Between Inertia, Viscosity, and Elasticity in Soft Robotic Actuator With Fluidic Network , 2018, IEEE Transactions on Robotics.

[31]  Lakmal D. Seneviratne,et al.  Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics , 2017, ArXiv.

[32]  Markus A. Horvath,et al.  An Implantable Extracardiac Soft Robotic Device for the Failing Heart: Mechanical Coupling and Synchronization. , 2017, Soft robotics.

[33]  Hao Jiang,et al.  A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[34]  D. Rus,et al.  Design, fabrication and control of soft robots , 2015, Nature.

[35]  Matteo Bianchi,et al.  Controlling Soft Robots: Balancing Feedback and Feedforward Elements , 2017, IEEE Robotics & Automation Magazine.

[36]  B. Marx,et al.  Simultaneous state and unknown inputs estimation with PI and PMI observers for Takagi Sugeno model with unmeasurable premise variables , 2009, 2009 17th Mediterranean Conference on Control and Automation.