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Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning

机译：通过定性空间推理指导操作计划的生成

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摘要

Manipulation planning is a complex task for robots with a manipulator arm that need to grasp objects in the environment, specifically under narrow spatial conditions restricting the workspace of the robot. A popular approach for generating motion plans is probabilistic roadmap planning. However, the sampling strategy of such planners is usually unguided, and hence may lead to motion plans that seem counterintuitive for a human observer. In this article we present an approach that generates heuristics for the probabilistic sampling strategy from spatial plans that abstract from concrete metric data. These spatial plans describe a free trajectory in the workspace of the robot on a purely qualitative level, i.e., by employing spatial relations from formalisms considered in the domain of Qualitative Spatial and Temporal Reasoning. We discuss how such formalisms and constraint-based reasoning methods can be applied to approximate geometrically feasible motions. The paper is completed by an evaluation of a hybrid planning system in different spatial settings showing that run-times are notably improved when an abstract plan is considered as a guidance heuristic.
机译：对于具有机械手的机器人，操纵计划是一项复杂的任务，该机器人需要抓握环境中的物体，尤其是在狭窄的空间条件下限制了机器人的工作空间。生成运动计划的一种流行方法是概率路线图计划。但是，此类计划者的采样策略通常是不受指导的，因此可能导致运动计划对于人类观察者而言似乎违反直觉。在本文中，我们提出一种方法，该方法从从具体度量数据中提取的空间计划中为概率抽样策略生成启发式方法。这些空间计划在纯定性的水平上描述了机器人工作空间中的自由轨迹，即通过采用在定性空间和时间推理领域中考虑的形式主义的空间关系。我们讨论如何将这种形式主义和基于约束的推理方法应用于近似几何可行的运动。本文通过对不同空间环境下的混合计划系统进行评估来完成，表明当将抽象计划视为指导启发式方法时，运行时间将得到显着改善。

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