This paper studies how global dynamics can inform path planning and decision-making for robots. Specifically, we investigate how coherent sets, an environmental feature found in flow-like environments, informs robot awareness within these scenarios. We compute coherent sets online with techniques from machine learning, and design a framework for robot behavior that uses coherent sets. We demonstrate the effectiveness of online methods over offline methods. Notably, we apply these online methods for robot monitoring of urban environments and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.
翻译:本文研究全球动态如何为机器人的路径规划和决策提供信息。 具体地说, 我们研究如何以一致的方式设置机器人, 一个在流程环境里发现的环境特征, 向机器人了解这些假设情景。 我们用机器学习的技巧来计算连贯的设置, 并设计一个使用一致组合的机器人行为框架。 我们展示了在线方法在离线方法上的有效性。 值得注意的是, 我们运用这些在线方法来监测机器人的城市环境, 通过水对机器人进行导航。 一致组合等环境特征为机器人提供了丰富背景, 以便他们更聪明、更高效的行为。