Title :
A partitioning-based task allocation strategy for Police Multi-Agents
Author :
Liang Zhiwei ; Yang Xiang ; Deng Yao
Author_Institution :
Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing, China
fDate :
May 31 2014-June 2 2014
Abstract :
RCRSS (RoboCup Rescue Simulation System, RCRSS) is a typical multi-agent system. In order to solve the task allocation problem for police multi-agents of this system, this paper presents a novel partitioning-based task allocation strategy. It is carried out through the use of clustering, namely the K-means clustering algorithm, to divide the map into several regions. Then the dynamic adjustment system with static assignment was adopted synthetically for the implementation. The experimental results show that the new task allocation strategy for the police can adapt to a variety of disaster environments and improve the efficiency of multi-agent collaboration.
Keywords :
multi-agent systems; multi-robot systems; pattern clustering; rescue robots; K-means clustering algorithm; RCRSS; RoboCup rescue simulation system; disaster environment; dynamic adjustment system; multiagent collaboration; partitioning-based task allocation; police multiagents; static assignment; Buildings; Clustering algorithms; Collaboration; Fires; Resource management; Roads; Robots; Cluster; Multi-agent; RoboCup rescue; Task allocation;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852518
