网络化机器人群中通信拓扑对标量场估计的影响
The Effect of Communication Topology on Scalar Field Estimation by Networked Robotic Swarms
AI总结：
本文研究了利用网络化机器人群重构二维标量场的问题，通过链式或网格拓扑结构进行通信，采用优化方法结合梯度计算，推导了可观测性格拉姆矩阵的迹界，验证了链式和网格拓扑的估计能力与鲁棒性。
摘要：
本文研究了利用网络化机器人群重构二维标量场的问题，考虑机器人通信网络形成链式或网格拓扑结构。将重构问题建模为受一阶线性动力学约束的优化问题。采用基于优化的方法，利用梯度法并进行梯度分析计算。此外，推导了系统可观测性格拉姆矩阵的迹界，帮助量化和比较链式和网格网络的估计能力。基于与系统H2范数相关的性能度量进行比较，用于研究网络拓扑的鲁棒性。结果通过模拟标量场和实际海洋盐度数据进行验证。
英文摘要：
This paper studies the problem of reconstructing a two-dimensional scalar field using a swarm of networked robots with local communication capabilities. We consider the communication network of the robots to form either a chain or a grid topology. We formulate the reconstruction problem as an optimization problem that is constrained by first-order linear dynamics on a large, interconnected system. To solve this problem, we employ an optimization-based scheme that uses a gradient-based method with an analytical computation of the gradient. In addition, we derive bounds on the trace of observability Gramian of the system, which helps us to quantify and compare the estimation capability of chain and grid networks. A comparison based on a performance measure related to the H2 norm of the system is also used to study robustness of the network topologies. Our resultsare validated using both simulated scalar fields and actual ocean salinity data.
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。
