人形機器人步態模仿與重現

建立一個具有多維度的人形機器人模型，並將此模型運用在機器人的行走平衡上，需要相當龐大的數學推倒與計算。這對非專業人士而言，更是一項困難挑戰。本論文目的為利用體感應器Kinect捕捉人體走路的姿態，並使機器人模仿真人行走。由於人體與機器人架構的差異，如何將所蒐集到的步態轉換成機器人的行走姿態是一項重要課題。另外，所蒐集的關節資訊中，缺乏腳踝資訊，踝關節對於行走而言是相當重要的一環。因此，我們使用加強式學習法調整各關節角度使機器人可以穩定、流暢的行走。我們設計一個學習架構來處理在實際的連續動作中穩定行走並保持平衡的複雜問題。另外，為了提高學習速率，我們利用兩兩姿態的差異性做為指標，求得關鍵姿態，僅對關鍵姿態做學習與調整。對於機器人的步態調整，穩定是最重要的考量，因此利用腳底壓力感測器所測得的值來計算腳底壓力的位置做為穩定性的依據。

關鍵字

行為模仿；人形機器人；加強式學習； Kinect ；關鍵姿態

並列摘要

Building a model of humanoid robot with many dimensions, and applying this model to achieve the balance of robot behavior at the same time, it needs a lot of mathematical derivation. It is a difficult challenge for those who are non-professional in robot control. The purpose of the thesis is to obtain the gait of human by Kinect and let robot learn walk by imitating human’s gait. Because of the structure difference between human and robot, how to map the gait from human to robot’s walking postures is a significant issue. Furthermore, we lack the information of human’s ankle, and it is a key data for walking. In order to solve this problem, we utilize reinforcement learning to adjust postures for each joint so the robot can walk stably and smoothly. We design a learning structure to deal with the problem of stability on robot’s physical continuous motion. On the other hand, we use the dissimilarity value between two adjacent postures to obtain key postures to increase the efficiency of robot’s learning, and to train these key postures only. Because the stability is most important for adjusting robot’s gait, we set up force sensors on robot’s soles of feet to compute the center of press as the index of stability.

並列關鍵字

Behavior imitation ； Humanoid robot ； Reinforcement learning ； Kinect ； Key posture

參考文獻

[2] F. Zuher, R. Romero,“Recognition of Human Motions for Imitation and Control of a Humanoid Robot”, Robotics Symposium and Latin American Robotics Symposium (SBR-LARS), Brazilian, pp. 190 – 195, 2012.

[3] T. H. S. Li, Y. T. Su, S. W. Lai, J. J. Hu, “Walking Motion Generation, Synthesis, and Control for Biped Robot by Using PGRL, LPI, and Fuzzy Logic”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, pp. 736 – 748, 2011.

[4] K. Y. Yu, Y. H. Sun, “Static Stable Regions Acording to Center of Press to Quantify Humanoid Walking Stability”, IEEE International Symposium on Robotic and Sensors Environments (ROSE), Montreal, QC, pp. 172 – 177, 2011.

[5] L. Cheng, Q. Sun, H. Su, Y. Cong, S. Zhao, “Design and Implementation of Human-Robot Interactive Demonstration System Based on Kinect”, IEEE Control and Decision Conference (CCDC), pp. 971 – 975, 2012.

[8] R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction, Cambridge, Mass., MIT Press, 1998.

國際替代計量

人形機器人步態模仿與重現

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