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Abstractions for computing all robotic sensors that suffice to solve a planning problem
arXiv - CS - Robotics Pub Date : 2020-05-22 , DOI: arxiv-2005.10994
Yulin Zhang and Dylan A. Shell

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.

中文翻译:

用于计算足以解决规划问题的所有机器人传感器的抽象

机器人能否完成某些特定任务取决于几个方面,包括机器人的传感器及其拥有的计划。我们对联合处理计划和传感器设计的搜索算法感兴趣,产生解决方案——即计划和传感器特征对——当且仅当它们存在时。这样的算法可以帮助机器人专家探索传感器的空间,以帮助进行设计权衡。概括以前的工作,其中传感器被抽象地建模为 p 图中的传感器图,本文增加了可以显着寻找的潜在传感器。但这样做会扩大目前在被认为易于处理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) 我们展示了如何表示这个更一般问题的搜索空间,并描述了能够通过单个特殊代表总结整组传感器的数据结构;(2) 我们给出了一种方法,通过该方法可以合并其他结构(任务领域知识、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足以解决特定的问题实例,尽管只是小规模的。尽管如此,该算法有助于帮助理解传感器必须具有哪些属性以及它们必须提供哪些信息,以确保机器人在不确定性的情况下仍能实现其目标。(2) 我们给出了一种方法,通过该方法可以合并其他结构(任务领域知识、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足以解决特定的问题实例,尽管只是小规模的。尽管如此,该算法有助于帮助理解传感器必须具有哪些属性以及它们必须提供哪些信息,以确保机器人在不确定性的情况下仍能实现其目标。(2) 我们给出了一种方法,通过该方法可以合并其他结构(任务领域知识、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足以解决特定的问题实例,尽管只是小规模的。尽管如此,该算法有助于帮助理解传感器必须具有哪些属性以及它们必须提供哪些信息,以确保机器人在不确定性的情况下仍能实现其目标。
更新日期:2020-05-25
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