标题:Improved poly-clonal artificial immune network for multi-robot dynamic path planning
作者:Deng, Lixia; Ma, Xin; Gu, Jason; Li, Yibin
通讯作者:Ma, X
作者机构:[Deng, Lixia; Ma, Xin; Gu, Jason; Li, Yibin] Shandong Univ, Sch Control Sci & Engn, Jinan 250100, Shandong, Peoples R China.
会议名称:IEEE International Conference on Information and Automation (ICIA)
会议日期:AUG 26-28, 2013
来源:2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA)
出版年:2013
页码:128-133
关键词:Artificial immune network; poly-clonal; multi-robot; dynamic path; planning
摘要:The most challenge of dynamic path planning lies in that the high unpredictability of environmental information. With the strong space search ability and learning ability, artificial immune network (AIN) has been used for path planning. Polyclonal artificial immune network (PCAIN) solves the problems of immature convergence and local minima with the increasing diversity of antibodies. In this paper, we propose improved polyclonal artificial immune network (IPCAIN) for multi-robot path planning with moving obstacles and moving goals in unknown environment. The antibody concentration is computed with taking other robots and moving obstacles into account. Moreover, memory units are used for preserving antibodies in the specific situations. The memory ability increases the initial concentration of specific antibodies, thus, reduces the response time for dynamic path planning. Extensive simulation experiments validate the proposed method can search the optimal path for multiple robots in dynamic unknown environment.
收录类别:CPCI-S
WOS核心被引频次:5
资源类型:会议论文
TOP