Abstract
This paper presents a new framework for trajectory optimization using comprehensive differential kinematics and dynamics theory, and also its applications and perspectives. For a robotic system with large degrees of freedom including humanoid robots, numerical gradient computation is not practical in terms of precision and time. Trajectory optimization is more and more demanded in different fields not only for usual motion planning but also motion imitation, dynamic parameter identification and human motion understanding. The proposed theory is based on the comprehensive motion transformation matrix (CMTM) that allows describing variational relationship in differential kinematics and dynamics including velocity and acceleration based on a simple chain product. This enables analytical gradient computation of various physical quantities such as joint force or torque with respect to trajectory parameters, which is beneficial to various optimization problems. We overview the possible evolution brought by this technique and demonstrate its advantages through examples of efficient optimization of dynamic motions for a redundant robot and a humanoid under severe constraints. Also, we discuss the possibility of its integration in optimal control method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ayusawa, K., Morisawa, M., Yoshida, E.: Motion retargeting for humanoid robots based on identification to preserve and reproduce human motion features. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2774–2779 (2015)
Ayusawa, K., Nakamura, Y.: Fast inverse kinematics algorithm for large dof system with decomposed gradient computation based on recursive formulation of equilibrium. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3447–3452 (2012)
Ayusawa, K., Rioux, A., Yoshida, E., Venture, G., Gautier, M.: Generating persistently exciting trajectory based on condition number optimization. In: Proceedings of the 2017 IEEE International Conference Robotics and Automation, pp. 6518–6524 (2017)
Ayusawa, K., Venture, G., Nakamura, Y.: Identification of humanoid robots dynamics using floating-base motion dynamics. In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2854–2859 (2008)
Ayusawa, K., Yoshida, E.: Comprehensive theory of differential kinematics and dynamics for motion optimization. In: Robotics: Science and Systems XIII (2017)
Ayusawa, K., Yoshida, E.: Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework. Int. J. Rob. Res. (2018). (Under review)
Bouyarmane, K., Kheddar, A.: On the dynamics modeling of free-floating-base articulated mechanisms and applications to humanoid whole-body dynamics and control. In: Proceedings of the 2012 IEEE-RAS International Conference on Humanoid Robots, pp. 36–42 (2012)
Caron, S., Kheddar, A.: Multi-contact walking pattern generation based on model preview control of 3D com accelerations. In: Proceedings of the 2016 IEEE-RAS International Conference on Humanoid Robots, pp. 550–557 (2016)
Dai, H., Valenzuela, A., Tedrake, R.: Whole-body motion planning with centroidal dynamics and full kinematics. In: Proceedings of the 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 295–302 (2014)
Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley-Interscience, New York (1897)
Imamura, Y., Ayusawa, K., Yoshida, E.: Risk estimation for intervertebral disc pressure through musculoskeletal joint reaction force simulation. In: Proceedings of the 39th IEEE Annual International Conference Engineering in Medicine and Biology Society (2017). To appear
Jovic, J., Escande, A., Ayusawa, K., Yoshida, E., Kheddar, A., Venture, G.: Humanoid and human inertia parameter identification using hierarchical optimization. IEEE Trans. Robot. 32(3), 726–735 (2016). https://doi.org/10.1109/TRO.2016.2558190
Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K., Hirukawa, H.: Biped walking pattern generation by using preview control of zero-moment point. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, pp. 1620–1626 (2003)
Kaneko, K., Kanehiro, F., Morisawa, M., Akachi, K., Miyamori, G., Hayashi, A., Kanehira, N.: Humanoid robot HRP-4 - humanoid robotics platform with lightweight and slim body. In: Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4400–4407 (2011)
Khatib, O.: A unified approach for motion and force control of robot manipulators: the operational space formulation. Int. J. Robot. Res. 3(1), 43–53 (1987)
Lengagne, S., Vaillant, J., Yoshida, E., Kheddar, A.: Generation of whole-body optimal dynamic multi-contact motions. Int. J. Robot. Res. 32(9–10), 1104–1119 (2013). https://doi.org/10.1177/0278364913478990
Liu, M., Micaelli, A., Evrard, P., Escande, A., Andriot, C.: Interactive virtual humans: a two-level prioritized control framework with wrench bounds. IEEE Trans. Robot. 28(6), 1309–1322 (2012)
Miossec, S., Yokoi, K., Kheddar, A.: Development of a software for motion optimization of robots - application to the kick motion of the hrp-2 robot. In: Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, pp. 299–304 (2006)
Mombaur, K.: Using optimization to create self-stable human-like running. Robotica 27, 321–330 (2009)
Murai, A., Kurosaki, K., Yamane, K., Nakamura, Y.: Computationally fast estimation of muscle tension for realtime bio-feedback. In: Proceedings of the 31st Annual International Conference of the IEEE EMBS, pp. 6546–6549 (2009)
Nakamura, Y., Yamane, K., Fujita, Y., Suzuki, I.: Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model. IEEE Trans. Robot. 21(1), 58–66 (2005)
Nakaoka, S.: Choreonoid: extensible virtual robot environment built on an integrated gui framework. In: Proceedings of the 2012 IEEE/SICE International Symposium on System Integration, pp. 79–85 (2012)
Nakaoka, S., Komura, T.: Interaction mesh based motion adaptation for biped humanoid robots. In: Proceedings of the 2012 IEEE-RAS International Conference on Humanoid Robots, pp. 625–631 (2012)
Park, F.C., Bobrow, J.E., Ploen, S.R.: A lie group formulation of robot dynamics. Int. J. Robot. Res. 14(6), 609–618 (1995)
Ramirez-Alpizar, I.G., Harada, K., Yoshida, E.: Motion planning for dual-arm assembly of ring-shaped elastic objects. In: Proceedings of the 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 594–600 (2014)
Ratliff, N., Zucker, M., Bagnell, J.A., Srinivasa, S.: CHOMP: gradient optimization techniques for efficient motion planning. In: Proceedings 2009 IEEE International Conference on Roboics and Automation, pp. 489–494 (2009)
Schulman, J., Ho, J., Lee, A., Awwal, I., Bradlow, H., Abbeel, P.: Finding locally optimal, collision-free trajectories with sequential convex optimization. In: Proceedings of the Robotics: Science and Systems IX (2013)
Sohl, G.A., Bobrow, J.E.: A recursive multibody dynamics and sensitivity algorithm for branched kinematic chains. J. Dyn. Syst. Meas. Control 123(3), 391–399 (2001)
Suleiman, W., Yoshida, E., Kanehiro, F., Laumond, J.P., Monin, A.: On human motion imitation by humanoid robot. In: Proceedings of the 2008 IEEE International Conference Robotics and Automation, pp. 2697–2704 (2008)
Suleiman, W., Yoshida, E., Laumond, J.P., Monin, A.: On humanoid motion optimization. In: Proceedings of 7th IEEE-RAS International Conference on Humanoid Robots, pp. 180–187 (2007)
Tassa, Y., Erez, T., Todorov, E.: Synthesis and stabilization of complex behaviors through online trajectory optimization. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4906–4913 (2012)
Vaillant, J., Kheddar, A., Audren, H., Keith, F., Brossette, S., Escande, A., Bouyarman, K., Kaneko, K., Morisawa, M., Gergondet, P., Yoshida, E., Kajita, S., Kanehiro, F.: Multi-contact vertical ladder climbing with an hrp-2 humanoid. Auton. Robot. 40(3), 561580 (2016). https://doi.org/10.1007/s10514-016-9546-4
Vukobratović, M., Borovac, B.: Zero-moment point - thirty-five years of its life. Int. J. Humanoid Robot. 1(1), 157–174 (2004)
Wieber, P.B.: Trajectory free linear model predictive control for stable walking in the presence of strong perturbations. In: Proceedings of the 2006 IEEE-RAS International Conference on Humanoid Robots, pp. 137–142 (2006)
Yamane, K., Anderson, S.O., Hodgins, J.K.: Controlling humanoid robots with human motion data: Experimental validation. In: Proceedings 2010 IEEE-RAS International Conference on Humanoid Robots, pp. 504–510 (2010)
Yoshida, E., Belousov, I., Esteves, C., Laumond, J.P.: Humanoid motion planning for dynamic tasks. In: Proceedings of 5th IEEE-RAS International Conference on Humanoid Robots, pp. 1–6 (2005)
Yoshida, E., Esteves, C., Belousov, I., Laumond, J.P., Sakaguchi, T., Yokoi, K.: Planning 3D collision-free dynamic robotic motion through iterative reshaping. IEEE Trans. Robot. 24(5), 1186–1198 (2008). https://doi.org/10.1109/TRO.2008.2002312
Yoshida, E., Kanoun, O., Esteves, C., Laumond, J.P., Yokoi, K.: Task-driven support polygon reshaping for humanoids. In: Proceedings of 6th IEEE-RAS International Conference on Humanoid Robots, pp. 827–832 (2006)
Acknowledgements
This research has been partly supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (A) Number 17H00768.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yoshida, E., Ayusawa, K. (2020). Towards Unified Framework for Trajectory Optimization Using General Differential Kinematics and Dynamics. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-28619-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28618-7
Online ISBN: 978-3-030-28619-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)