A scalable, distributed algorithm for allocating workers in embedded systems
A scalable, distributed algorithm for allocating workers in embedded systems
复制标题
用于在嵌入式系统中分配工作人员的可扩展分布式算法
DOI：
10.1109/icsmc.2001.972039
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
R. Goodman
中科院分区：
文献类型：
--
作者：
W. Agassounon;A. Martinoli;R. Goodman
本文提出了一种可扩展的阈值为基础的算法分配工人到一个给定的任务，其需求随时间动态演变。该算法是完全分布式的，完全基于个人的本地感知。每个智能体只有在“感觉”到需要根据其感官输入完成某些工作时，才自主地和确定地决定工作。在本文中，我们采用的工人分配算法的集体操纵的情况下，研究有关的收集和聚类最初分散的小对象。采用宏观和微观概率模型，在三个不同的实验水平上对聚集实验进行了研究，并进行了具体的模拟。结果表明，团队使用的一些积极的工人动态控制的分配算法实现类似或更好的性能比那些特点是一个恒定的团队规模的聚合，而在整个聚合过程中使用的代理数量大大减少。由于该算法并不意味着代理之间的任何形式的显式通信，它代表了一个具有成本效益的解决方案，用于控制由几个到数千个单元组成的嵌入式系统中的活动工人的数量。
This paper presents a scalable threshold-based algorithm for allocating workers to a given task whose demand evolves dynamically over time. The algorithm is fully distributed and solely based on the local perceptions of the individuals. Each agent decides autonomously and deterministically to work only when it "feels" that some work needs to be done based on its sensory inputs. In this paper, we applied the worker allocation algorithm to a collective manipulation case study concerned with the gathering and clustering of initially scattered small objects. The aggregation experiment has been studied at three different experimental levels by using macroscopic and microscopic probabilistic models, and embodied simulations. Results show that teams using a number of active workers dynamically controlled by the allocation algorithm achieve similar or better performances in aggregation than those characterized by a constant team size, while using a considerably reduced number of agents over the whole aggregation process. Since this algorithm does not imply any form of explicit communication among agents, it represents a cost-effective solution for controlling the number of active workers in embedded systems consisting of a few to thousands of units.