Multi-Robot Adaptive Sampling based on Mixture of Experts Approach to Modeling Non-Stationary Spatial Fields
Multi-Robot Adaptive Sampling based on Mixture of Experts Approach to Modeling Non-Stationary Spatial Fields
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DOI：
10.1109/mrs60187.2023.10416785
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Kizito Masaba;Alberto Quattrini Li
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文献类型：
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作者：
Kizito Masaba;Alberto Quattrini Li
本文提出了一种自适应采样策略，用于机器人团队对非平稳空间场进行建模-即，具有不均匀变化的字段-在大环境中，具有所需的预测准确性。对非平稳非均匀场进行建模对于许多应用是必不可少的，例如监测空气质量或湖泊污染水平。主流的自适应采样策略假设环境现象是平稳的，并使用单一模型来解释这些领域，从而导致对独特的局部变化的不准确描述。在本文中，我们将非平稳场建模为（理论上是无限的）平稳均匀子场的非重叠层的集合。这种方法允许使用混合专家对非平稳场进行建模，其中每个专家被分配一个特定的同质子区域进行映射。这种方法将环境分解为更小的同质区域，从而允许对大型环境进行实时建模。我们设计了一个数据驱动的方法来自适应地识别每个固定层，并定义其与其他层的关系。我们将各个子区域之间的关系建模为专家网络-快速采样自适应图。每个机器人逐步建立自己的专家子网络，用于确定在哪里采样。在现实模拟中的几个实验证明竞争力的准确性和采样效率相比，其他国家的最先进的方法。
This paper presents an adaptive sampling strategy for a team of robots to model non-stationary spatial fields – i.e., fields with uneven variations – in large environments, with a desired predictive accuracy. Modeling non-stationary heterogeneous fields is essential for many applications, like monitoring air quality or contamination level in lakes. Mainstream adaptive sampling strategies assume stationarity of the environmental phenomenon and use a single model to explain such fields, resulting in inaccurate characterization of unique localized variations. In this paper, we model a non-stationary field as a collection of (infinite, in theory) non-overlapping layers of stationary homogeneous subfields. This approach allows for modeling non-stationary fields using a mixture of experts, where each expert is assigned a particular homogeneous subregion to map. This approach decomposes the environment into smaller homogeneous regions, which allows real-time modeling of large environments. We design a data-driven approach to adaptively identify each stationary layer and define its relationship to other layers. We model the relationship between various subregions as a network of experts – the rapidly-sampling adaptive graph. Each robot incrementally builds its own sub-network of experts, which is used to determine where to sample. Several experiments in realistic simulation demonstrate competitive accuracy and sampling efficiency compared to other state-of-the-art methods.