Abstract
People have designed many different kinds of humanoid robots, but few of them have been applied to real life. On the one hand, the robot has insufficient movement ability and poor flexibility; on the other hand, there are no effective structures that can effectively buffer the impact caused by falling. Therefore, it is very important to design a robot which can detect when a fall will occur, what kinds of protective actions will be taken after a fall, and most importantly to resist the impact of the fall. In this work we present a novel humanoid robot whose design was based on the principles of bionics, high stiffness, light weight, and multipoint protections. Based on capture point theory and 3D-LIPM model, the robot can detect when it would fall down and what protective actions it would take after falling. It was verified in the actual robot, including falling, standing after a fall in outdoor environment. The experiment results show that the proposed Falling-Crawling robot can resist the impact force caused by falling.
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References
Nashner, L.M., Mccollum, G.: The organization of human postural movements, a formal basis and experimental synthesis. The Behavioral and Brain Science 8(01), 135 (1985)
Maki, B.E., Mcilroy, W.E.: The role of limb movements in maintaining upright stance: The ”change-in-support” strategy. Phys. Ther. 77(5), 488–507 (1997)
Pratt, J., Carff, J., Drakunov, S., Goswami, A.: Capture point: A step toward humanoid push recovery. In: 2006 6th IEEE-RAS International Conference on Humanoid Robots (2007)
Atkeson, C.G., Babu, B., Banerjee, N., Berenson, D., Xinjilefu, X.: No falls, no resets: Reliable humanoid behavior in the darpa robotics challenge. In: IEEE-RAS International Conference on Humanoid Robots (2015)
Fujiwara, K., Kanehiro, F., Kajita, S., et al.: UKEMI: falling motion control to minimize damage to biped humanoid robot. Intelligent Robots and Systems. IEEE (2002)
Kakiuchi, Y., Kamon, M., Shimomura, N., Yukizaki, S., Inaba, M.: Development of life-sized humanoid robot platform with robustness for falling down, long time working and error occurrence. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Nguyen, K., Kojio, Y., Noda, S., Sugai, F., Inaba, M.: Dynamic fall recovery motion generation on biped robot with shell protector. IEEE Robotics and Automation Letters 6(4), 6741–6748 (2021)
Kajita, S., et al.: Impact acceleration of falling humanoid robot with an airbag. In: IEEE-RAS International Conference on Humanoid Robots, pp. 637–643 (2016)
Lee, S.H., Goswami, A.: Fall on backpack: Damage minimization of humanoid robots by falling on targeted body segments. J. Comput. Nonlinear Dyna. 8(2), 021005 (2013)
Ha, S., Liu, C.K.: Multiple contact planning for minimizing damage of humanoid falls. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015)
Yun, S.K., Goswami, A.: Tripod fall: Concept and experiments of a novel approach to humanoid robot fall damage reduction. In: IEEE International Conference on Robotics Automation (2014)
Subburaman, R., Lee, J., Caldwell, D.G., Tsagarakis, N.G.: Online falling-over control of humanoids exploiting energy shaping and distribution methods. In: 2018 IEEE International Conference on Robotics and Automation (2018)
Fujiwara, K., Kajita, S., Harada, K., Kaneko, K., Hirukawa, H.: Towards an optimal falling motion for a humanoid robot. In: IEEE-RAS International Conference on Humanoid Robots, pp. 524-529 (2006)
Li, Q., Chen, X., Zhou, Y., Yu, Z., Zhang, W., Huang, Q.: A minimized falling damage method for humanoid robots. Int. J. Adva. Robo. Sys. 14, 172988141772801 (2017)
Samy, V., Caron, S., Bouyarmane, K., Kheddar, A.: Post-impact adaptive compliance for humanoid falls using predictive control of a reduced model. In: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 655–660 (2017)
Meng, L., Yu, Z., Chen, X., Zhang, W., Liu, H.: A falling motion control of humanoid robots based on biomechanical evaluation of falling down of humans. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) (2015)
Kajita, S., Sakaguchi, T., Nakaoka, S., Morisawa, M., Kanehiro, F.: Quick squatting motion generation of a humanoid robot for falling damage reduction. In: 2017 IEEE International Conference on Cyborg and Bionic Systems (2017)
Fujiwara, K., Kanehiro, F., Kajita, S., Kaneko, K., Yokoi, K., Hirukawa, H.: Ukemi: falling motion control to minimize damage to biped humanoid robot. In: Intelligent Robots and Systems, 2521–2526 (2002)
Kumar, V.C., Ha, S., Liu, C.K.: Learning a unified control policy for safe falling (2017)
Huang, Q., Huang., Y., Yu., Z.: Fundamental Theory and Technology of Humanoid Robots. Beijing Institute of Technology Press, Beijing (2021)
Kalyanakrishnan, S., Goswami, A.: Learning to predict humanoid fall. Int. J. Humano. Robo. 8(2), 245–273 (2011)
Fujiwara, K., Kanehiro, F., Saito, H., Kajita, S., Hirukawa, H.: Falling motion control of a humanoid robot trained by virtual supplementary tests. In: IEEE International Conference on Robotics Automation (2004)
Kim, J.J., Kim, Y.J., Lee, J.J.: A machine learning approach to falling detection and avoidance for biped robots. In: Sice Conference 2011, pp. 562-567. Tokyo, Japan (2011)
Nho, Y.H., Lim, J.G., Kwon, D.S.: Cluster-analysis-based user-adaptive fall detection using fusion of heart rate sensor and accelerometer in a wearable device. IEEE Access 8, 40389–40401 (2020)
Di, P., et al.: Fall detection for the elderly using a cane robot based on ZMP estimation. In: 2013 International Symposium on Micro-NanoMechatronics and Human Science (MHS) IEEE, pp. 1–6 (2013)
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Zuo, W., Gao, J., Cao, J., Mu, T., Bi, Y. (2023). Structure Design and Fall Trajectory Planning of an Electrically Driven Humanoid Robot. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14271. Springer, Singapore. https://doi.org/10.1007/978-981-99-6495-6_41
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DOI: https://doi.org/10.1007/978-981-99-6495-6_41
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