论文标题
使用贝叶斯优化和向量的线性组合来控制多机器人系统的框架
A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors
论文作者
论文摘要
我们提出了一个通用框架，用于为分散的多机器人系统创建参数化控制方案。在分散的多机器人文献中可以看到各种任务，每个任务都有许多可能的控制方案。对于其中一些，代理使用算法选择控制速度，这些算法从环境中提取信息并以有意义的方式组合信息。从这个基本的形成中，提出了一个框架，将每个机器人的测量信息分类为相关标量和向量的集，并创建测量向量集的线性组合。除了优化的参数集外，标量测量值可用于生成线性组合的系数。有了这个框架和贝叶斯优化，我们可以为多个多机器人任务创建有效的控制系统，包括凝聚力和隔离，模式形成以及搜索/觅食。
We propose a general framework for creating parameterized control schemes for decentralized multi-robot systems. A variety of tasks can be seen in the decentralized multi-robot literature, each with many possible control schemes. For several of them, the agents choose control velocities using algorithms that extract information from the environment and combine that information in meaningful ways. From this basic formation, a framework is proposed that classifies each robots' measurement information as sets of relevant scalars and vectors and creates a linear combination of the measured vector sets. Along with an optimizable parameter set, the scalar measurements are used to generate the coefficients for the linear combination. With this framework and Bayesian optimization, we can create effective control systems for several multi-robot tasks, including cohesion and segregation, pattern formation, and searching/foraging.
