Decentralized Classification in Societies of Autonomous and Heterogenous Robots
This paper addresses the classification problem for a set of autonomous robots that interact with each other. The objective is to classify agents that “behave in “different way, due to their own physical dynamics or to the interaction protocol they are obeying to, as belonging to different “species. This paper describes a technique that allows a decentralized classification system to be built in a systematic way, once the hybrid models describing the behavior of the different species are given. This technique is based on a decentralized identifi- cation mechanism, by which every agent classifies its neighbors using only local information. By endowing every agent with such a local classifier, the overall system is enhanced with the ability to run behaviors involving individuals of the same species as well as of different ones. The mechanism can also be used to measure the level of cooperativeness of neighbors and to discover possible intruders among them. General applicability of the proposed solution is shown through examples of multi– agent systems from Biology and from Robotics.
Simone Martini Adriano Fagiolini Giancarlo Zichittella Magnus Egerstedt Antonio Bicchi
Interdep. Research Center E. Piaggio,Faculty of Engineering,Università di Pisa,Italy School of Electrical and Computer Engineering,Georgia Institute of Technology,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
32-39
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）