An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-target tracking methods, to represent unseen objects. Using the PHD, the expected number of observed objects is optimized. In a sequential manner, each agent maximizes the number of observed new targets, taking into account the probability of undetected objects due to previous agents’ actions and the probability of detection, which yields a scalable algorithm. Algorithm properties are evaluated in simulations, and shown to outperform a greedy base line method. The algorithm is also evaluated by applying it to a sea ice tracking problem, using two datasets collected in the Arctic, with reasonable results. An implementation is provided under an Open Source license.

中文翻译：

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使用概率假设密度的多主体信息路径规划

提出了一种用于多个代理的知情路径规划算法。当无法进行大范围的调查时，可用于有效地利用可用代理。在内部，该算法具有概率假说密度（PHD）表示形式，该表示形式受到现代多目标跟踪方法的启发，可以表示看不见的对象。使用PHD，可以优化预期的观察对象数。考虑到先前代理的动作导致未检测到对象的可能性以及检测的可能性，每个代理以顺序的方式最大化了观察到的新目标的数量，从而产生了可扩展的算法。在仿真中对算法属性进行了评估，结果表明该算法的性能优于贪婪的基线方法。该算法还通过将其应用于海冰跟踪问题进行评估，使用在北极收集的两个数据集，得出了合理的结果。在开放源代码许可下提供了一个实现。