用机器学习让无人机智能判断是否停留或前进，提升任务效率。
Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions
- 基于分支预测与强化学习，动态决策无人机是否停留
- 最差情况任务时间缩短4.1倍，中位数仅比理想情况高2.7%
- 适合实时数据驱动的无人机任务，尤其事件概率随时间变化场景
无人机在数据驱动任务中，除感知外还需在运行时处理数据，判断是否需就地采取行动。若未发现需行动的情况却停留，会浪费时间；若已启程才发现需返回，则需返航。为此，本文提出基于分支预测与强化学习的机器学习方法，在事件发生概率随时间变化的多种场景下进行评估。结果表明，该方法持续优于文献中的回归方法，最差情况任务时间可提升4.1倍；中位数任务时间仅比具备完美事件概率知识的理想方法高出2.7%。
原文摘要 · Abstract (English)
Drones are becoming indispensable in many application domains. In data-driven missions, besides sensing, the drone must process the collected data at runtime to decide whether additional action must be taken on the spot, before moving to the next point of interest. If processing does not reveal an event or situation that requires such an action, the drone has waited in vain instead of moving to the next point. If, however, the drone starts moving to the next point and it turns out that a follow-up action is needed at the previous point, it must spend time to fly-back. To take this decision, we propose different machine-learning methods based on branch prediction and reinforcement learning. We evaluate these methods for a wide range of scenarios where the probability of event occurrence changes with time. Our results show that the proposed methods consistently outperform the regression-based method proposed in the literature and can significantly improve the worst-case mission time by up to 4.1x. Also, the achieved median mission time is very close, merely up to 2.7% higher, to that of a method with perfect knowledge of the current underlying event probability at each point of interest.
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