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.

翻译：无人机在许多应用领域正变得不可或缺。在数据驱动的任务中，除了感知，无人机必须在运行时处理收集的数据，以决定在前往下一个兴趣点之前，是否需要在现场采取额外行动。如果处理未发现需要此类行动的事件或情况，无人机将徒劳等待而非前往下一个点。然而，如果无人机开始前往下一个点，但前一个点需要后续行动，则必须花费时间飞回。为做出此决策，我们提出了基于分支预测和强化学习的不同机器学习方法。我们在事件发生概率随时间变化的各种场景中评估这些方法。结果表明，所提方法始终优于文献中提出的基于回归的方法，并能将最坏情况任务时间显著提升高达4.1倍。此外，所实现的中位任务时间与在每个兴趣点具有当前底层事件概率完美知识的方法非常接近，仅高出最多2.7%。