论文标题
Tigris：一种知情的基于抽样的算法，用于信息路径计划
TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning
论文作者
论文摘要
内容丰富的路径计划是机器人技术中的一个重要且具有挑战性的问题，尚待解决，以允许广泛实施和现实世界实用的方式解决。在此原因的各种原因中，缺乏方法可以在高维空间和非平凡的传感器约束中进行信息性路径计划。在这项工作中，我们提出了一种基于抽样的方法，该方法使我们能够应对大型和高维搜索空间的挑战。这是通过在高维连续空间中执行知情采样并在奖励估计中沿边缘纳入潜在的信息增益来完成的。该方法迅速生成了一个全局路径，该路径可最大程度地利用给定路径预算约束的信息增益。我们讨论了使用带有前向摄像头的固定翼无人机在大型搜索空间中搜索多个感兴趣的对象的实施情况的详细信息。我们将我们的方法与基于抽样的计划者的基线进行了比较，并证明了我们的贡献如何使我们的方法始终如一地超过基线18.0％。因此，我们提供了一个实用且可普遍的路径计划框架，可用于非常大的环境，预算有限和高维搜索空间，例如具有运动约束或高维配置空间的机器人。
Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces.
