论文标题
分布式数据存储和融合，以用于资源有限的移动机器人群中的集体感知
Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms
论文作者
论文摘要
在本文中，我们提出了一种方法，以在资源有限的机器人群中进行分布式存储和数据融合，以进行集体感知。我们在分布式的语义分类方案中演示了我们的方法。我们考虑一个移动机器人团队，其中每个机器人都会运行已知准确性的预训练的分类器，以注释环境中的对象。我们提供了两个主要贡献：（i）分散的，共享的数据结构，用于有效存储和检索语义注释，专为低资源移动机器人而设计； （ii）一种基于投票的，分散的算法，以减少存在不完美分类的注释的差异。我们讨论了这两种贡献的理论和实施，并执行一组逼真的模拟实验，以评估我们的方法的性能。
In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots; and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach.
