Efficient Probabilistic Search for Static Objects in Large Unstructured Environments using Belief Markov Decision Processes with Options

This study introduces a novel approach that formulates the search problem as a belief Markov decision process with options (BMDP-O) to enable efficient Monte Carlo tree search (MCTS) for overcoming the challenges of long planning horizons and sensor limitations in large-scale environments.