Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness
This work addresses robot decision-making in dynamic environments, but it appears incremental as it builds on existing coherent set concepts with online methods.
The paper tackles the problem of improving robot situational awareness in flow-like environments by using online-generated coherent sets as environmental features, demonstrating effectiveness in pedestrian monitoring and water navigation scenarios.
This paper studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, we investigate how coherent sets, an environmental feature found in these environments, inform robot awareness within these scenarios. The proposed approach is an online environmental feature generator which can be used for robot reasoning. We compute coherent sets online with techniques from machine learning and design frameworks for robot behavior that leverage coherent set features. We demonstrate the effectiveness of online methods over offline methods. Notably, we apply these online methods for robot monitoring of pedestrian behaviors and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.