无人机通过学习过往任务经验，智能判断是否等待计算结果。
Should I Stay or Should I Go: A Learning Approach for Drone-based Sensing Applications
- 基于历史任务数据训练决策模型，动态判断是否等待计算完成。
- 在多种场景下性能优于固定策略，最高提升25.8%。
- 适合需实时决策的无人机感知与执行应用。
多旋翼无人机正成为多个应用领域的重要平台，支持精准的现场感知和/或操作。本文关注无人机需处理传感器数据以决定是否采取进一步行动（如更精确感知或执行动作）的情形。若等待计算完成，可能浪费时间；若提前移动，计算后仍需返回，造成效率损失。本文提出一种基于过往任务经验的学习方法，使无人机能够智能决策是否等待计算结果。通过广泛评估，结果表明，在合理配置下，该方法在各类场景中均优于多种静态策略，性能提升最高达25.8%，无论任务中执行动作的概率保持稳定还是随时间变化。
原文摘要 · Abstract (English)
Multicopter drones are becoming a key platform in several application domains, enabling precise on-the-spot sensing and/or actuation. We focus on the case where the drone must process the sensor data in order to decide, depending on the outcome, whether it needs to perform some additional action, e.g., more accurate sensing or some form of actuation. On the one hand, waiting for the computation to complete may waste time, if it turns out that no further action is needed. On the other hand, if the drone starts moving toward the next point of interest before the computation ends, it may need to return back to the previous point, if some action needs to be taken. In this paper, we propose a learning approach that enables the drone to take informed decisions about whether to wait for the result of the computation (or not), based on past experience gathered from previous missions. Through an extensive evaluation, we show that the proposed approach, when properly configured, outperforms several static policies, up to 25.8%, over a wide variety of different scenarios where the probability of some action being required at a given point of interest remains stable as well as for scenarios where this probability varies in time.
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