A distributed multi-robot adaptive sampling scheme for complex field estimation
A distributed multi-robot adaptive sampling scheme for complex field estimation
复制标题
复杂场估计的分布式多机器人自适应采样方案
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
D. Popa
中科院分区：
文献类型：
--
作者：
M. Mysorewala;L. Cheded;Mirza Salman Baig;D. Popa
监测广泛的环境场是一项复杂的任务，在许多领域都有很大的用途，例如建立自然现象的模型：例如农作物田、油藏等的湿度。对此类时空分布田地的成功监测取决于无线传感器网络的使用，无线传感器网络通过其分布式特性，允许有效的自适应采样程序来收集田地密度估计所需的统计信息。所使用的采样程序的自适应性质体现了一种策略，该策略根据收集的统计信息选择下一个采样位置，并随着过去的测量而发展。本文提出了一种新颖的分布式多机器人“自适应采样算法”，它是先前提出的仅使用单个机器人进行复杂场估计的算法的扩展。提出了集中式、分散式、联邦分散式和分布式传感器网络中传感器融合的新公式，用于场密度估计，而不仅仅是云边界确定。其中包括所涉及的各种计算负载的比较。仿真结果表明，添加有效的采样区域划分和并行多机器人采样可以提高现场重建时间。使用 N 个机器人，可以观察到采样次数减少了 N 倍以上。联邦和分布式方案还提高了通信和计算效率。
Monitoring widespread environmental fields is a complex task that is of great use in many areas, such as building models of natural phenomenon: e.g. moisture in a crop field, oil reservoirs, etc. A successful monitoring of such spatio-temporally distributed fields hinges upon the use of wireless sensor networks which, through their distributed nature, allow for an effective adaptive sampling procedure to gather the statistical information necessary for field density estimation. The adaptive nature of the sampling procedure used embodies a strategy which selects the next sampling location based on the gathered statistical information, and which evolves with past measurements. This paper presents a novel distributed multi-robot "Adaptive sampling algorithm", which is an extension of the algorithm proposed earlier for complex field estimation using a single-robot only. New formulations of sensor fusion in a centralized, decentralized, federated-decentralized, and distributed sensor network are presented for field density estimation, and not just cloud boundary determination. A comparison of the various computational loads involved is included. Simulation results show that adding an efficient partitioning of the sampling area and parallel multi-robot sampling improves the field reconstruction time. With N robots, more than an N-fold reduction in the number of sampling times is observed. The federated and distributed scheme also leads to an improved communication and computational efficiency.