Title

前蹼式履帶型機器人之以加速度計、陀螺儀及卡爾曼濾波器為基礎的傾斜測量及攀爬階梯能力分析

Translated Titles

Tilt Measurement based on an Accelerometer, a Gyro and a Kalman Filter as well as Stair Climbing Analysis for a Tracked Robot with Front Flipper Arms

DOI

10.6341/fcu.M0211772

Authors

李孟諺

Key Words

履帶型機器人 ; 攀爬階梯 ; 傾斜測量 ; 卡爾曼濾波器 ; 模糊控制

PublicationName

逢甲大學自動控制工程學系學位論文

Volume or Term/Year and Month of Publication

2015年

Academic Degree Category

碩士

Advisor

林南州

Content Language

繁體中文

Chinese Abstract

本文介紹履帶型機器人攀爬階梯能力及傾斜測量,所使用機器人主要由兩個主履帶及兩隻前蹼所構成。機器人能自主式移動、攀爬,感測器為研究重點之一,本研究所使用機器人裝置慣性感測器加速度計和陀螺儀、超音波感測器、及馬達編碼器。攀爬階梯能力是由槓桿原理方式分析,推導出機器人攀爬階梯而不發生傾倒的最大高度,規劃攀爬階梯的動作流程,有效擺放機器人前蹼位置可以增加機器人接觸地面面積進而提升攀爬階梯摩擦力。階梯攀爬可行性偵測,透過超音波感測器及前蹼擺放角度由公式計算出前方階梯高度,透過最大高度分析判斷階梯攀爬可行性,增強機器人自主式作動能力。加速度計和陀螺儀皆可用來測量傾斜角度,加速度計感測三軸重力加速度大小,透過三角函數計算傾斜角度,陀螺儀感測三軸角速度大小,透過時間積分計算傾斜角度,但是加速度計容易因為外部加速度訊號而產生干擾,而陀螺儀會因為積分過程隨著時間導致誤差越來越大,加入卡爾曼濾波器成功讓它們優劣互補,增加傾斜測量精確度。使用卡爾曼濾波器降低誤差有所拘束,透過快速前後移動及上下擺動實驗結果得知,其測量誤差在實驗環境變化較大時隨之變大。結合模糊控制理論調整卡爾曼濾波器之斜方差矩陣參數,改變濾波器對陀螺儀及加速度計原有信賴程度,成功改善環境變化較大時角度測量誤差。

Topic Category 資電學院 > 自動控制工程學系
工程學 > 機械工程
Reference
  1. [1] S. G. Tzafestas, “Mobile Robots: General Concepts,” Introduction to Mobile Robot Control, pp. 1-29, 2014.
    連結:
  2. [2] S. G. Tzafestas, “Mobile Robots at Work,” Introduction to Mobile Robot Control, pp. 635-663, 2014.
    連結:
  3. [3] T. S. Wei, Mechanical design of a small all-terrain robot, National University of Singapore, 2002.
    連結:
  4. [5] D. Calisi, D. Nardi, K. Ohno and S. Tadokoro, “A semi-autonomous tracked robot system for rescue missions,” SICE Annual Conference, pp. 2066-2069, 2008.
    連結:
  5. [6] Q. Li, P. D. Ayers and A.B. Anderson, “Modeling of terrain impact caused by tracked vehicles,” Journal of Terramechanics, Vol. 44, pp. 395-410.
    連結:
  6. [7] D. Koh, K. Hyun and S. Kim, “Design of Multi-joint Tracked Robot for Adaptive Uneven Terrain Driving,” IEEE International Conference on Autonomous Robots and Agents, pp. 464-469, 2009.
    連結:
  7. [8] T. Fujita and T. Shoji, “Development of a Rough Terrain Mobile Robot with Multistage Tracks,” IEEE International Conference on Advanced Robotics, pp.1-6, 2013.
    連結:
  8. [9] Q. Zhang, S. S. Ge and P. Y. Tao, “Autonomous Stair Climbing for Mobile Tracked Robot,” IEEE International Symposium on Safety, Security and Rescue Robotics, pp. 92-98, 2011.
    連結:
  9. [10] C. K. Tseng, I. H. Li, Y. H. Chien, M. C. Chen and W. Y. Wang, “Autonomous Stair Detection and Climbing Systems for a Tracked Robot,” IEEE International Conference on System Science and Engineering, pp.201-204, 2013.
    連結:
  10. [11] 鄭善韋,履帶型串節式機器人之階梯攀爬能力分析,國立台北科技大學機電整合研究所碩士學位論文,2011。
    連結:
  11. [12] C. Zong, S. Jiang, W. Guo, L. Li and X. Gao, “Obstacle-surmounting Capability Analysis of A Joint Double-tracked Robot,” IEEE International Conference on Mechatronics and Automation, pp. 723-728, 2014.
    連結:
  12. [13] Q. H. Vu, B. S. Kim and J. B. Song, “Autonomous Stair Climbing Algorithm for a Small Four-Tracked Robot,” IEEE International Conference on Control, Automation and Systems, pp. 2356-2360, 2008.
    連結:
  13. [15] S. Singh, B. D. Jadhav and K. M. Krishna, “Posture Control of a Three-Segmented Tracked Robot with Torque Minimization During Step Climbing,” IEEE International Conference on Robotics & Automation, pp. 4200-4207, 2014.
    連結:
  14. [16] R. E. Kalman “A New Approach to Linear Filtering and Prediction Problems” Transactions of the ASME–Journal of Basic Engineering, Vol. 82, pp. 35-45,1960.
    連結:
  15. [18] J. J. R. Pasaye, J. A. B. Valencia and F. J. Perez, “Tilt Measurement based on an Accelerometer, a Gyro and a Kalman Filter to Control a Self-Balancing Vehicle,” IEEE International Autumn Meeting on Power, Electronics and Computing, pp. 1-5, 2013.
    連結:
  16. [19] L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 4, pp.28-44, 1973.
    連結:
  17. [20] N. Yadaiah, T. Srikanth and V. S. Rao “Fuzzy Kalman Filter Based Trajectory Estmation,” International Conference on Hybrid Intelligent Systems, pp. 566-571, 2011.
    連結:
  18. [4] A. Tunwannarux and S. Tunwannarux, “The CEO Mission II, Rescue Robot with Multi-Joint Mechanical Arm,” International Journal of Electrical and Computer Engineering, pp. 453-458, 2007.
  19. [14] Q. Quan, S. Ma, B. Li and R. Liu, “Posture Control of a Dual-crawler-driven Robot,” IEEE International Conference on Robotics and Automation, pp. 2977-2982, 2009.
  20. [17] G. Welch and G. Bishop, An Introduction to the Kalman Filter, Department of Computer Science, University of North Carolina at Chapel Hill, 2006.