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A Systematic Design Selection Methodology for System-Optimal Compliant Actuation
Published online by Cambridge University Press: 29 November 2018
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This work presents a systematic design selection methodology that utilizes a co-design strategy for system-level optimization of compliantly actuated robots that are known for their advantages over robotic systems driven by rigid actuators. The introduced methodology facilitates a decision-making strategy that is instrumental in making selections among system-optimal robot designs actuated by various degrees of variable or fixed compliance. While the simultaneous co-design method that is utilized throughout guarantees systems performing at their full potential, a homotopy technique is used to maintain integrity via generation of a continuum of robot designs actuated with varying degrees of variable and fixed compliance. Fairness of the selection methodology is ensured via utilization of common underlying (variable) compliant actuation principle and dynamical task requirements throughout the generated system designs. The direct consequence of the developed methodology is that it allows robot designers make informed selections among a variety of systems which are guaranteed to perform at their best. Applicability of the introduced methodology has been validated using a case study for system-optimal design of an active knee prosthesis that is driven by a mechanically adjustable compliance and controllable equilibrium position actuator (MACCEPA) under a periodic/real-life dynamical task.
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References
Pratt, G. A. and Williamson, M. M., “Series Elastic Actuators,” IEEE/RSJ International Conference on Intelligent Robots and Systems (1995) pp. 399–406.Google Scholar
Sensinger, J. W. and Weir, R. F., “Design and Analysis of a Non-backdrivable Series Elastic Actuator,” IEEE International Conference on Rehabilitation Robotics (2005) pp. 390–393.10.1109/ICORR.2005.1501126CrossRefGoogle Scholar
Martinez-Villalpando, E. C., Weber, J., Elliot, G. and Herr, H., “Design of an Agonist-Antagonist Active Knee Prosthesis,” IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (2008) pp. 529–534.10.1109/BIOROB.2008.4762919CrossRefGoogle Scholar
Hollander, K. W., Ilg, R., Sugar, T. and Herring, D., “An efficient robotic tendon for gait assistance,” J. Biomech. Eng. 128(5), 788–791 (2006).10.1115/1.2264391CrossRefGoogle ScholarPubMed
Tokatli, O. and Patoglu, V., “Series Elastic Actuation for Force Controlled Micro-Manipulation,” IEEE International Conference on Mechatronics (ICM) (2011) pp. 421–426.Google Scholar
Schempf, H., Kraeuter, C. and Blackwell, M., “Roboleg: A Robotic Soccer-Ball Kicking Leg,” IEEE International Conference on Robotics and Automation (1995) pp. 1314–1318.Google Scholar
Okada, M., Ban, S. and Nakamura, Y., “Skill of Compliance with Controlled Charging/Discharging of Kinetic Energy,” IEEE International Conference on Robotics and Automation (2002) pp. 2455–2460.Google Scholar
Paluska, D. and Herr, H., “The effect of series elasticity on actuator power and work output: Implications for robotic and prosthetic joint design,” Rob. Auton. Syst. 54(8), 667–673 (2006).10.1016/j.robot.2006.02.013CrossRefGoogle Scholar
Migliore, S. A., Brown, E. A. and DeWeerth, S. P., “Biologically Inspired Joint Stiffness Control,” IEEE International Conference on Robotics and Automation (2005) pp. 4508–4513.Google Scholar
Bicchi, A., Tonietti, G. and Schiavi, R., “Safe and Fast Actuators for Machines Interacting with Humans,” IEEE Technical Exhibition Based Conference on Robotics and Automation (2004) pp. 17–18.Google Scholar
Petit, F., Friedl, W., Hoppner, H. and Grebenstein, M., “Analysis and synthesis of the bidirectional antagonistic variable stiffness mechanism,” IEEE/ASME Trans. Mechatron. 20(2), 684–695 (2015).10.1109/TMECH.2014.2321428CrossRefGoogle Scholar
Ham, R. V., Vanderborght, B., Damme, M. V., Verrelst, B. and Lefeber, D., “MACCEPA: The Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator for ‘Controlled Passive Walking’,” IEEE International Conference on Robotics and Automation (2006) pp. 2195–2200.Google Scholar
Jafari, A., Tsagarakis, N. G., Vanderborght, B. and Caldwell, D. G., “A Novel Actuator with Adjustable Stiffness (AwAS),” IEEE/RSJ International Conference on Intelligent Robots and Systems (2010) pp. 4201–4206.10.1109/IROS.2010.5648902CrossRefGoogle Scholar
Yalcin, M., Uzunoglu, B., Altintepe, E. and Patoglu, V., “VnSA: Variable Negative Stiffness Actuation Based on Nonlinear Deflection Characteristics of Buckling Ceams,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2013) pp. 5418–5424.10.1109/IROS.2013.6697140CrossRefGoogle Scholar
Bicchi, A. and Tonietti, G., “Fast and soft arm tactics: Dealing with the safety-performance tradeoff in robot arms design and control,” IEEE Rob. Autom. Mag. 11(2), 22–23 (2004).10.1109/MRA.2004.1310939CrossRefGoogle Scholar
Garabini, M., Passaglia, A., Belo, F., Salaris, P. and Bicchi, A., “Optimality Principles in Variable Stiffness Control: The VSA Hammer,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2011) pp. 3770–3775.Google Scholar
Haddadin, S., Huber, F. and Albu-Schffer, A., “Optimal Control for Exploiting the Natural Dynamics of Variable Stiffness Robots,” IEEE International Conference on Robotics and Automation (2008) pp. 3347–3354.Google Scholar
Peters, D. L., Papalambros, P. Y. and Ulsoy, A. G., “Relationship between Coupling and the Controllability Grammian in Co-design Problems,” American Control Conference (2010) pp. 623–628.Google Scholar
Fathy, H. K., Reyer, J. A., Papalambros, P. Y. and Ulsoy, A. G., “On the Coupling between the Plant and Controller Optimization Problems,” American Control Conference (2001) pp. 1864–1869.10.1109/ACC.2001.946008CrossRefGoogle Scholar
Kamadan, A., Kiziltas, G. and Patoglu, V., “Co-design strategies for optimal variable stiffness actuation,” IEEE/ASME Trans. Mechatron. 22(6), 2768–2779 (2017).10.1109/TMECH.2017.2765085CrossRefGoogle Scholar
S. W. et al., “Variable Stiffness Actuators: Review on Design and Components,” IEEE/ASME Transactions on Mechatronics, 21(5), 2418–2430 (2016).10.1109/TMECH.2015.2501019CrossRefGoogle Scholar
Hussain, I., Albalasie, A., Awad, M. I., Seneviratne, L. and Gan, D., “Modeling, control, and numerical simulations of a novel binary-controlled variable stiffness actuator (bcvsa),” Front. Rob. AI 5(68), 1–17 (2018).Google Scholar
Marler, R. T. and Arora, S., “Survey of multi-objective optimization methods for engineering,” Struct. Multidiscip. Optim. 26(6), 369–395 (2004).10.1007/s00158-003-0368-6CrossRefGoogle Scholar
Martinez-Villalpando, E. C. and Herr, H., “Agonist-Antagonist Active Knee Prosthesis: A Preliminary Study in Level-Ground Walking,” J. Rehab. Res. Dev. 46(3), 361–374 (2009).10.1682/JRRD.2008.09.0131CrossRefGoogle ScholarPubMed
Gill, P. E., Murray, W. and Saunders, M. A., “SNOPT: An SQP Algorithm for Large-Scale Constrained Programming,” Technical Report (Stanford University, 1997).Google Scholar
Fletcher, R. and Leyffer, S., “Nonlinear Programming without a Penalty Function,” Technical Report (University of Dundee, 1997).Google Scholar
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