This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

中文翻译：

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资源约束的协同多代理在线搜索的基于样本贝叶斯方法

本文提出了一种协同多主体搜索算法，以解决在多个约束条件下在二维平面上搜索目标的问题。当代理获取观察信息时，贝叶斯框架用于更新目标的局部概率密度函数（PDF）。为了获得用于决策的全局PDF，提出了一种基于采样的对数意见池算法来融合局部PDF，并使用粒子采样方法来表示连续的PDF。然后应用高斯混合模型（GMM）从粒子重构全局PDF，并提出了加权期望最大化算法来估计GMM的参数。此外，我们提出了一个优化目标，该目标旨在指导代理商以更少的资源消耗找到目标，并同时保持每个代理的资源消耗平衡。为此，提出了一种基于效用函数的优化问题，并通过基于梯度的方法解决了该问题。若干对比模拟表明，与其他现有方法相比，所提出的方法使用的整体资源更少，并且在平衡资源消耗方面表现出更好的性能。