Topological path planning for autonomous information gathering
Topological path planning for autonomous information gathering
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DOI：
10.1007/s10514-021-10012-x
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发表时间：
2021-09-07
影响因子：
3.5
通讯作者：
Hollinger, Geoffrey A.
中科院分区：
文献类型：
--
作者：
McCammon, Seth;Hollinger, Geoffrey A.
在本文中，我们提出了两种新的算法，信息空间拓扑规划，识别拓扑特征的信息领域，并使用它们来规划最大信息路径的机器人在信息收集任务。这些功能提供了一种通过划分机器人的状态空间或路径空间来快速将全局上下文纳入信息丰富的路径规划过程的方法。我们的第一个算法，分层热点信息收集，使用拓扑状态空间划分，通过构建一个高层次的信息热点地图。然后，我们解决了全局调度问题的拓扑图，其中的解决方案，然后用于路径规划的一组本地贪婪的覆盖规划器内的每个热点。我们的第二个算法，拓扑感知自组织映射，扩展了自组织映射算法，发现突出的拓扑功能的信息功能。这些特征被用来执行拓扑路径空间分解，以提供具有拓扑多样性初始化的随机梯度上升优化算法，从而提高其性能。在模拟试验和现场实验中，我们比较了这两种方法的权衡，并表明我们的方法，利用信息场的拓扑特征始终表现出竞争力或优于不利用这些功能的方法，同时需要更少的计算时间。
In this paper, we present two novel algorithms for information space topological planning that identify topological features in an information field and use them to plan maximally informative paths for a robot in an information gathering task. These features provide a way to rapidly incorporate global context into the informative path planning process by partitioning the state space or the path space of a robot. Our first algorithm, hierarchical hotspot information gathering, uses a topological state space partitioning by constructing a high-level map of information hotspots. We then solve a global scheduling problem over the topological graph, the solution of which is then used for path planning by a set of local greedy coverage planners within each hotspot. Our second algorithm, Topology-Aware Self Organizing Maps, extends the Self Organizing Map algorithm to discover prominent topological features in the information function. These features are used to perform a topological path space decomposition to provide a Stochastic Gradient Ascent optimization algorithm with topologically diverse initialization, improving its performance. In simulated trials and field experiments, we compare the tradeoffs of these two approaches and show that our methods that leverage topological features of the information field consistently perform competitively or better than methods that do not exploit these features, while requiring less computation time.