While maximizing line-of-sight coverage of specific regions or agents in the environment is a well-explored path planning objective, the converse problem of minimizing exposure to the entire environment during navigation is especially interesting in the context of minimizing detection risk. This work demonstrates that minimizing line-of-sight exposure to the environment is non-Markovian, which cannot be efficiently solved optimally with traditional path planning. The optimality gap of the graph-search algorithm A* and the trade-offs in optimality vs. computation time of several approximating heuristics is explored. Finally, the concept of equal-exposure corridors, which afford polynomial time determination of all paths that do not increase exposure, is presented.

翻译：尽管最大化环境中特定区域或智能体的视线覆盖是一个已被深入探索的路径规划目标，但其逆问题——在导航过程中最小化对整个环境的暴露——在最小化探测风险的背景下尤其值得关注。本工作证明，最小化对环境的视线暴露是一个非马尔可夫性问题，无法通过传统路径规划方法高效地求得最优解。本文探讨了图搜索算法A*的最优性差距，以及几种近似启发式算法在最优性与计算时间之间的权衡。最后，提出了等曝光走廊的概念，该概念允许在多项式时间内确定所有不会增加暴露的路径。