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Articles

Inverse optimal control to model human trajectories during locomotion

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Pages 499-511 | Received 06 Jan 2021, Accepted 27 Jul 2021, Published online: 16 Aug 2021

References

  • Arechavaleta G, Laumond J, Hicheur H, Berthoz A. 2008. An optimality principle governing human walking. IEEE Trans Robot. 24(1):5–14.
  • Arechavaleta G, Laumond JP, Hicheur H, Berthoz A. 2001. The nonholonomic nature of human locomotion: A modeling study. In: Proceeding of the IEEE/RAS-EMBS International Conference on Biomedical Robots and Biomechatronics. 2006(2):158–163.
  • Arechavaleta G, Laumond JP, Hicheur H, Berthoz A. 2008. On the nonholonomic nature of human locomotion. Auton Robot. 25(1/2):25–35.
  • Basili P, Sağlam M, Kruse T, Huber M, Kirsch A, Glasauer S. 2013. Strategies of locomotor collision avoidance. Gait Posture. 37(3):385–390.
  • Biess A, Liebermann D, Flash T. 2007. A computational model for redundant human three-dimensional pointing movements: Integration of independent spatial and temporal motor plans simplifies movement dynamics. J Neurosci. 27(48):13045–13064.
  • Bovi G, Rabuffetti M, Mazzoleni P, Ferrarin M. 2011. A multiple-task gait analysis approach: kinematic, kinetic and emg reference data for healthy young and adult subjects. Gait Posture. 33(1):6–13.
  • Elbanhawi M, Simic M, Jazar R. 2015. Continuous path smoothing for car-like robots using b-spline curves. J Intell Robot Syst. 80(S1):23–56.
  • Flash T, Hogan N. 1985. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci. 5(7):1688–1703.
  • Gard C, Miff C, Kuo A. 2004. Comparison of kinematic and kinetic methods for computing the vertical motion of the body center of mass during walking. In: Human Movement Science. 22(6):597–610.
  • Heijnen M, Muir BC, Rietdyk S. 2012. Factors leading to obstacle contact during adaptive locomotion. Exp Brain Res. 223(2):219–231.
  • Kim J, Bertram JEA. 2018. Compliant walking appears metabolically advantageous at extreme step lengths. Gait Posture. 64:84–89.
  • Koilias A, Nelson MG, Anagnostopoulos CN, Mousas C. 2020. Immersive walking in a virtual crowd: The effects of the density, speed, and direction of a virtual crowd on human movement behavior. Comput Anim Virtual Worlds. 31(6):e1928.
  • Kosuge K, Yoshida H, Fukuda T. 1993. Dynamic control for robot-human collaboration. In: Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication. 398–401.
  • Laumond JP. 1998. Robot motionplanning and control. In: Lectures notes in control and information sciences, vol. 229. Berlin, Heidelberg: Springer.
  • Maroger I, Stasse O, Watier B. 2020a. Comparison of human experimental trajectories and simulations during gait. Computer methods in biomechanics and biomedical engineering. 23(sup1):S189–S191.
  • Maroger I, Stasse O, Watier B. 2020b. Walking human trajectory models and their application to humanoid robot locomotion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas (USA).
  • Mastalli C, Budhiraja R, Merkt W, Saurel G, Hammoud B, Naveau M. 2020. Crocoddyl: An efficient and versatile framework for multi-contact optimal control. In: IEEE International Conference on Robotics and Automation.
  • Mombaur K, Laumond JP, Truong A. 2009. An inverse optimal control approach to human motion modeling. Berlin, Heidelberg: Springer; vol. 70. p. 451–468.
  • Mombaur K, Truong A, Laumond JP. 2010. From human to humanoid locomotion—an inverse optimal control approach. Auton Robot. 28(3):369–383.
  • Nandi GC, Semwal VB, Raj M, Jindal A. 2016. Modeling bipedal locomotion trajectories using hybrid automata. In: 2016 IEEE Region 10 Conference (TENCON). 1013–1018.
  • Panchea A, Ramdani N, Bonnet V, Fraisse P. 2018. Human arm motion analysis based on the inverse optimization approach. 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 2018 p. 1005–1010.
  • Papadopoulos A, Bascetta L, Ferretti G. 2013. Generation of human walking paths. IEEE International Conference on Intelligent Robots and Systems, Tokyo, Japan; 11.
  • Park T, Levine S. 2013. Inverse optimal control for humanoid locomotion, Robotics Science and Systems Workshop on Inverse Optimal Control & Robotic Learning from Demonstration.
  • Powell MJD. 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comp J. 7(2):155–162.
  • Raković M, Savić S, Santos-Victor J, Nikolić M, Borovac B. 2019. Human-inspired online path planning and biped walking realization in unknown environment. Front Neurorobot. 13:36.
  • Semwal VB, Kumar C, Mishra PK, Nandi GC. 2018. Design of vector field for different subphases of gait and regeneration of gait pattern. IEEE Trans Automat Sci Eng. 15(1):104–110.
  • Sheridan TB. 2016. Human-Robot Interaction: Status and Challenges. Hum Factors. 58(4):525–532.
  • Soueres P, Laumond J. 1996. Shortest paths synthesis for a car-like robot. IEEE Trans Automat Contr. 41(5):672–688.
  • Stasse O, et al. 2017. Talos: A new humanoid research platform targeted for industrial applications. In: IEEE-RAS International Conference on Humanoid Robotics. p. 689–695.
  • Sylla N, Bonnet V, Venture G, Armande N, Fraisse P. 2014. Human arm optimal motion analysis in industrial screwing task. Paper presented at the 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. Sao Paulo, Brazil; p. 964–969.
  • Tassa Y, Mansard N, Todorov E. 2014. Control-limited differential dynamic programming. In: IEEE International Conference on Robotics and Automation, Hong Kong, China.
  • Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, SciPy 1.0 Contributors, et al. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat Methods. 17(3):261–272.
  • Viviani P, Flash T. 1995. Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. J Exp Psychol Hum Percept Perform. 21(1):32–53.
  • Zhu Y, Ren D, Fan M, Qian D, Li X, Xia H. 2020. Robust trajectory forecasting for multiple intelligent agents in dynamic scene. CoRR. abs/2005.13133.

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