A New Soft Robot Control Method: Using Model Predictive Control for a Pneumatically Actuated Humanoid

Traditional rigid robots, such as those used in manufacturing, have been effective at precise, accurate, rapid motions in well-structured environments for many decades now. However, they operate largely behind cages due to the danger of injury when moving in close proximity to people. A significant and recent shift in robotics involves trading rigid links and rigid actuators for soft, deformable links and compliant actuators. These soft robots generally have lower inertia and avoid many of the problems caused by the high effective inertia resulting from the high gear ratios necessary for rigid robots.

[1]  Stefan Schulz,et al.  Compliant Robotics and Automation with Flexible Fluidic Actuators and Inflatable Structures , 2012 .

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

[3]  Marc D. Killpack,et al.  Comparing Model Predictive Control and input shaping for improved response of low-impedance robots , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[4]  Siddharth Sanan,et al.  Pneumatic Torsional Actuators for Inflatable Robots , 2014 .

[5]  Cagdas D. Onal,et al.  Design and control of a soft and continuously deformable 2D robotic manipulation system , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Marc D. Killpack,et al.  Simultaneous position and stiffness control for an inflatable soft robot , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Yuval Tassa,et al.  Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts , 2011, Robotics: Science and Systems.

[8]  Christopher G. Atkeson,et al.  Robots with inflatable links , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Marc D. Killpack,et al.  Control of a pneumatically actuated, fully inflatable, fabric-based, humanoid robot , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[10]  Daniela Rus,et al.  Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator , 2016, Int. J. Robotics Res..

[11]  Pieter Abbeel,et al.  An Application of Reinforcement Learning to Aerobatic Helicopter Flight , 2006, NIPS.

[12]  Daniela Rus,et al.  Whole arm planning for a soft and highly compliant 2D robotic manipulator , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Christopher G. Atkeson,et al.  Physical human interaction for an inflatable manipulator , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Oleg Ivlev Soft fluidic actuators of rotary type for safe physical human-machine interaction , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[15]  Daniela Rus,et al.  Design, kinematics, and control of a soft spatial fluidic elastomer manipulator , 2016, Int. J. Robotics Res..

[16]  R. G. Stone,et al.  Mechanical engineering department , 1976 .

[17]  G. Whitesides Soft Robotics. , 2018, Angewandte Chemie.

[18]  S. Shankar Sastry,et al.  Decentralized nonlinear model predictive control of multiple flying robots , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[19]  Charles C. Kemp,et al.  Fast reaching in clutter while regulating forces using model predictive control , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).

[20]  Charles C. Kemp,et al.  Model predictive control for fast reaching in clutter , 2016, Auton. Robots.

[21]  Stephen P. Boyd,et al.  CVXGEN: a code generator for embedded convex optimization , 2011, Optimization and Engineering.

[22]  RusDaniela,et al.  Design, kinematics, and control of a soft spatial fluidic elastomer manipulator , 2016 .

[23]  Phil F. Culverhouse,et al.  Robust Adaptive Control of an Uninhabited Surface Vehicle , 2015, J. Intell. Robotic Syst..