论文标题
计算所有足以解决计划问题的机器人传感器的抽象
Abstractions for computing all robotic sensors that suffice to solve a planning problem
论文作者
论文摘要
机器人是否可以执行某些特定的任务取决于几个方面，包括机器人的传感器及其所拥有的计划。我们对共同处理计划和传感器设计的搜索算法感兴趣，并产生解决方案 - 即计划和传感器表征对 - 当它们存在时，它们才会对。这种算法可以帮助机器人探索传感器的空间，以帮助进行设计权衡。概括先前的工作，其中传感器被抽象地建模为p图上的传感器图，本文会增加可以显着寻求的潜在传感器。但是这样做会扩大目前在被认为可以拖动的外部限制上的问题。为了驯服这种复杂性，做出了两个贡献：（1）我们展示了如何代表更通用问题的搜索空间，并描述了能够通过单个特殊代表来汇总整个传感器的数据结构； （2）我们提供了一种手段，可以合并其他结构（任务域知识，传感器技术或制造约束）以减少要枚举的集合。这些导致我们已经实施的算法，并且足以解决特定的问题实例，尽管只有小规模。然而，算法有助于帮助了解传感器必须拥有的属性以及必须提供的信息，以确保尽管不确定性，但机器人仍可以实现其目标。
Whether a robot can perform some specific task depends on several aspects, including the robot's sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions---i.e., plan and sensor characterization pairs---if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design trade-offs. Generalizing prior work where sensors are modeled abstractly as sensor maps on p-graphs, the present paper increases the potential sensors which can be sought significantly. But doing so enlarges a problem currently on the outer limits of being considered tractable. Toward taming this complexity, two contributions are made: (1) we show how to represent the search space for this more general problem and describe data structures that enable whole sets of sensors to be summarized via a single special representative; (2) we give a means by which other structure (either task domain knowledge, sensor technology or fabrication constraints) can be incorporated to reduce the sets to be enumerated. These lead to algorithms that we have implemented and which suffice to solve particular problem instances, albeit only of small scale. Nevertheless, the algorithm aids in helping understand what attributes sensors must possess and what information they must provide in order to ensure a robot can achieve its goals despite non-determinism.
