计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (20): 142-145.DOI: 10.3778/j.issn.1002-8331.2010.20.040

• 人工智能 • 上一篇    下一篇

生物交哺行为启发的群体机器人搜集行为研究

姜丽梅,张汝波,王超伦   

  1. 哈尔滨工程大学 计算机科学与技术学院,哈尔滨 150001
  • 收稿日期:2010-04-15 修回日期:2010-05-14 出版日期:2010-07-11 发布日期:2010-07-11
  • 通讯作者: 姜丽梅

Research on foraging behavior of swarm robotics inspired by biological trophallaxis

JIANG Li-mei,ZHANG Ru-bo,WANG Chao-lun   

  1. College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
  • Received:2010-04-15 Revised:2010-05-14 Online:2010-07-11 Published:2010-07-11
  • Contact: JIANG Li-mei

摘要: 为了提高群体机器人系统的整体性能,受生物系统中普遍存在的交哺现象的启发,在原来多机器人系统的基本行为的基础上,提出了一种引入交哺行为的多机器人协作机制。机器人依靠有限的感知能力和局部交互功能,以自组织方式执行目标搜集任务。机器人的内部状态变量反映其执行任务的情况以及对环境和其他机器人的评价。比较机器人的内部状态变量,可以判断是否需要交哺和交哺的方向性。主要目的是减少机器人之间的冲突,降低系统能量消耗的同时,提高机器人搜集目标的效率。最后通过计算机仿真实验以及与其他多机器人协作方法比较,分析该方法对提高系统性能的有效性。

关键词: 多机器人协作, 群体机器人, 交哺行为, 群体智能

Abstract: To improve the overall performance of the multi-robot system,inspired by the biological trophallaxis which exists extensively in nature,a new mechanism is proposed for coordination of multi-robot system based on the basic behavior of the former robots.Since limited sense and local interaction are adopted,the system is built in a self-organization way.An internal state variable is used for every robot to reflect the working state and its estimation of the environments and other robots’ state.The probability of whether to exchange targets with other robots and the direction of trophallaxis between two robots are judged by comparing the internal state variables.The new method is aimed to reduce the collision between robots and extra energy cost which is caused by movement of the loading robots.In addition,the speed and rate of targets collected are used to evaluate the efficiency of the new method.Finally,computer simulations are accomplished and the simulation results are analyzed.Compared with other coordination mechanisms,the effectiveness of the methods proposed is shown when executing search and collection task.

Key words: multi-robot coordination, swarm robotics, trophallaxis behavior, swarm intelligence

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