在合作机器人中进行类似的任务分配和招聘
M J Krieger1, J B Billeter, L Keller
1Institute of Ecology, University of Lausanne, Switzerland.
Nature
|September 13, 2000
概括
灵感来自群的机器人群体展示了高效的食和能源维护. 由于干扰,群体的好处随着规模的增加而减少,反映了社会昆虫的动态.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 群集情报 群集情报 群集情报
背景情况:
- 机器人技术面临着与不可预测环境实时交互的挑战.
- 殖民地表现出成功的自我组织和分工,为机器人系统提供了一个模型.
研究的目的:
- 调查殖民地原则对机器人群设计的应用.
- 评估受社会昆虫启发的去中心化控制算法的效率和稳定性.
主要方法:
- 为机器人群落实施受启发的去中心化控制算法.
- 将机器人群的采食效率和能量水平与单个机器人进行比较.
- 分析群体规模和信息传输对群体表现的影响.
主要成果:
- 使用灵感来自的算法的机器人群体显示出比单个机器人更有效的食和更高的群体能量.
- 随着小组规模的增加,观察到小组效率的回报下降,这归因于食干扰.
- 信息传输 (招聘) 在集群环境中显著提高了群体效率,类似于社会昆虫.
结论:
- 群原则可以为灵活,坚固和有效的机器人系统的设计提供信息.
- 分散控制和信息共享对于优化群体行为和性能至关重要.
- 机器人群动力学可以有效地模仿社会昆虫中观察到的群体策略.
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