论文标题
工作区分区和拓扑发现算法，用于异质多代理网络
Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multi-Agent Networks
论文作者
论文摘要
在本文中，我们考虑了一类工作空间分区问题，这些问题是在区域覆盖范围和空间负载平衡的背景下为空间分布的异质多机构网络而出现的。假定每个代理都有一定的运动方向或传感和探索的方向，比其他机构更可取。这些偏好是通过凸面和各向异性（方向依赖性）二次接近度度量来衡量的，通常对于每个药物而言都是不同的。这些接近度的指标诱导网络工作区的伏诺伊样分区，这些分区由细胞组成，这些细胞可能并不总是是凸（甚至连接）集的集合，但必须包含在其相应试剂中已知的椭圆形中。这项工作的主要贡献是1）一种分布式算法，用于计算异质多机构网络的voronoi类式分区和2）一个系统的过程，以发现后者类似voronoi的分区引起的网络拓扑。还提供了说明所提出算法功效的数值模拟。
In this paper, we consider a class of workspace partitioning problems that arise in the context of area coverage and spatial load balancing for spatially distributed heterogeneous multi-agent networks. It is assumed that each agent has certain directions of motion or directions for sensing and exploration that are more preferable than others. These preferences are measured by means of convex and anisotropic (direction-dependent) quadratic proximity metrics which are, in general, different for each agent. These proximity metrics induce Voronoi-like partitions of the network's workspace that are comprised of cells which may not always be convex (or even connected) sets but are necessarily contained in ellipsoids that are known to their corresponding agents. The main contributions of this work are 1) a distributed algorithm for the computation of a Voronoi-like partition of the workspace of a heterogeneous multi-agent network and 2) a systematic process to discover the network topology induced by the latter Voronoi-like partition. Numerical simulations that illustrate the efficacy of the proposed algorithms are also presented.
