J. Senthilnath K. Harikumar Suresh Sundaram
Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore Robotics Research Center, IIIT Hyderabad, India Department of Aerospace Engineering, Indian Institute of Science Bengaluru
This article presents a metacognitive decisionmaking (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting and confirming stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of “which-sensor-to-use,” “when-to-switch-sensor,” and “how-to-search.” Based on the information gathered by sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching, where the lower levels of search aim to localize a target (detection) and the highest level of a search exploit a target (confirmation). The performance of the MDM framework with two sensors having a low accuracy for detection and increased accuracy to confirm targets is evaluated through Monte Carlo simulations and compared with six decentralized multiUAV search algorithms (three self-cognitive searches and three self and social-cognitive-based searches). The results indicate that the MDM framework can efficiently detect and confirm targets in an unknown environment.