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Effective communication is crucial for deploying robots in mission-specifictasks, but inadequate or unreliable communication can greatly reduce missionefficacy, for example in search and rescue missions where communication-deniedconditions 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 robotcollected is lost owing to the lack of communication. In our prior work, wedeveloped the notion of a path-based sensor. A path-based sensor detectswhether or not an event has occurred along a particular path, but it does notprovide the exact location of the event. Such path-based sensor observationsare well-suited to communication-denied environments, and various studies haveexplored methods to improve information gathering in such settings. In somemissions it is typical for target elements to be in close proximity tohazardous factors that hinder the information-gathering process. In this study,we examine a similar scenario and conduct experiments to determine ifadditional knowledge about the correlation between hazards and targets improvesthe efficiency of information gathering. To incorporate this knowledge, weutilize a Bayesian network representation of domain knowledge and develop analgorithm based on this representation. Our empirical investigation revealsthat such additional information on correlation is beneficial only inenvironments with moderate hazard lethality, suggesting that while knowledge ofcorrelation helps, further research and development is necessary for optimaloutcomes.

Path-Based Sensors: Will the Knowledge of Correlation in Random Variables Accelerate Information Gathering?