Abstract

The planning of drone trajectories in the search for ground-level objects is considered, in the case of challenging observation conditions. In existing planning algorithms, a priori data regarding the location of the objects are used in the secondary search, but no account is taken of their local visibility (on account of fog or smoke, say). To improve search productivity in challenging observation conditions, the observability may be taken into account in planning the drone trajectory. Heuristic models are used to assess the observability. The working trajectory is selected by calculating the maximum useful search information obtained from various possible trajectories. To eliminate Shannon indeterminacy in the utility of the information regarding successive points, we introduce an additional utility function. The results obtained by simulation of the search process confirm that this approach is more effective than trajectory planning on the basis of the maximum a priori probability that objects are present and on the basis of search entropy estimates.

中文翻译：

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挑战性条件下的无人机搜索

摘要

在具有挑战性的观测条件下，要考虑在搜寻地面物体时规划无人机的轨迹。在现有的规划算法中，有关对象位置的先验数据用于辅助搜索中，但没有考虑到它们的局部可见性（例如，考虑到雾或烟）。为了提高在具有挑战性的观察条件下的搜索效率，在计划无人机轨迹时可考虑可观察性。启发式模型用于评估可观察性。通过计算从各种可能的轨迹获得的最大有用搜索信息来选择工作轨迹。为了消除有关连续点信息的效用中的Shannon不确定性，我们引入了一个附加的效用函数。