透過您的圖書館登入
IP:3.23.127.197
  • 學位論文

機器人隊形控制之動態角色指定演算法

Dynamic Role Assignment Algorithm for Robot Formation Control

指導教授 : 王銀添

摘要


本論文針對多部移動機器人的隊形控制(formation control)議題,提出動態角色指定(dynamic role assignment)演算法。首先定義角色成本(character cost),用以描述隊形中各機器人被指派為特定角色的難易程度。其次,以角色成本定義隊形中的角色組合適應性(character set fitness)。最後,依據角色組合適應性,選取隊形形成時最適合的角色組合。另外,當隊形變動時,機器人必須迴避行進路徑中的機器人以免碰撞,本論文也完成機器人避障機制的設計。設計的動態角色指定演算法與避障機制應用在全方位驅動機器人系統的隊形控制中。模擬與實測的結果顯示,相較於固定角色指定方法,所設計的動態角色指定演算法使隊形形成或變動過程更具效率。

並列摘要


In this thesis, a dynamic role assignment algorithm is proposed for formation control of multiple mobile robots. First, the degree of difficulty for a robot been assigned a specified character in a formation is defined as the character cost. Second, define the character set fitness of a robot formation by determining the character cost of each robot. Finally, select the best character set of a formation by calculating the character set fitness. Meanwhile, in the procedure of formation changing, an obstacle avoidance mechanism is designed in this research for robots to prevent collision between themselves. The developed dynamic role assignment algorithm and obstacle avoidance mechanism are applied to the formation control of a group omni-directional driven robots. Simulation and experimental results show that the performance the proposed dynamic role assignment algorithm is more efficient than that of fixed role assignment.

參考文獻


[12] H.C.H. Hsu and A. Liu, 2005, “Applying various reference types to formation control of mobile robots,” Journal of Information Science and Engineering.
[22] 黃勻良,全方位移動機器人之機構與驅動設計,淡江大學機械與機電工程學系碩士論文,2005。
[7] J.P. Desai, “A graph theoretic approach for modeling mobile robot team formations,” Journal of Robot Systems, vol. 19, no. 11, pp. 511-525, November 2002.
[8] J. Fredslund and M.J. Mataric, “A general algorithm for robot formations using local sensing and minimal communication,” IEEE Transactions on Robotics and Automation, vol. 18, no. 5, pp. 837-846, October 2002.
[9] D.E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, Reading, Massachusetts, 1989.

延伸閱讀