Effective communication is crucial for deploying robots in mission-specific tasks, but inadequate or unreliable communication can greatly reduce mission efficacy, for example in search and rescue missions where communication-denied conditions may occur. In such missions, robots are deployed to locate targets, such as human survivors, but they might get trapped at hazardous locations, such as in a trapping pit or by debris. Thus, the information the robot collected is lost owing to the lack of communication. In our prior work, we developed the notion of a path-based sensor. A path-based sensor detects whether or not an event has occurred along a particular path, but it does not provide the exact location of the event. Such path-based sensor observations are well-suited to communication-denied environments, and various studies have explored methods to improve information gathering in such settings. In some missions it is typical for target elements to be in close proximity to hazardous factors that hinder the information-gathering process. In this study, we examine a similar scenario and conduct experiments to determine if additional knowledge about the correlation between hazards and targets improves the efficiency of information gathering. To incorporate this knowledge, we utilize a Bayesian network representation of domain knowledge and develop an algorithm based on this representation. Our empirical investigation reveals that such additional information on correlation is beneficial only in environments with moderate hazard lethality, suggesting that while knowledge of correlation helps, further research and development is necessary for optimal outcomes.

翻译：有效通信对于在任务特定场景中部署机器人至关重要，但通信不足或不可靠可能严重降低任务效能，例如在可能发生通信中断的搜索与救援任务中。在此类任务中，机器人被部署用于定位目标（如幸存者），但可能被困在危险区域（如陷阱坑或废墟中）。因此，由于缺乏通信，机器人收集的信息会丢失。我们先前的研究提出了基于路径的传感器概念。这种传感器能检测特定路径上是否发生事件，但无法提供事件的确切位置。此类基于路径的传感器观测非常适合通信受限环境，且已有研究探索了在此类环境中改进信息收集的方法。在某些任务中，目标元素通常与阻碍信息收集过程的危险因素相邻近。本研究考察了类似场景，并通过实验探究风险与目标之间相关性的额外知识是否能提高信息收集效率。为整合此类知识，我们采用贝叶斯网络表示领域知识，并基于此设计算法。实验结果表明，相关性信息仅在中等风险致命性环境中有效，这说明尽管相关性知识有所助益，但为实现最优结果仍需进一步研究与开发。