异构多Agent网络的工作空间划分和拓扑发现算法
Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multi-Agent Networks
摘要：
在本文中，我们考虑了一类工作空间划分问题，出现在空间分布的异构多智能体网络的区域覆盖和空间负载平衡的背景下。假设每个智能体都具有某些运动方向或比其他智能体更优选的用于感测和探索的方向。这些偏好是通过凸和各向异性（方向相关）的二次接近度量，这是，在一般情况下，每个代理不同的装置。这些接近度度量引起网络工作空间的Voronoi样分区，这些分区由可能不总是凸（或甚至连接）集的单元组成，但必须包含在其对应代理已知的椭圆中。这项工作的主要贡献是1）一个分布式算法的计算的Voronoi的异构多智能体网络的工作空间和2）一个系统的过程，发现网络拓扑诱导后Voronoi的划分。数值模拟，说明所提出的算法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
