The increasing use of autonomous robot systems in hazardous environments underscores the need for efficient search and rescue operations. Despite significant advancements, existing literature on object search often falls short in overcoming the difficulty of long planning horizons and dealing with sensor limitations, such as noise. This study introduces a novel approach that formulates the search problem as a belief Markov decision processes with options (BMDP-O) to make Monte Carlo tree search (MCTS) a viable tool for overcoming these challenges in large scale environments. The proposed formulation incorporates sequences of actions (options) to move between regions of interest, enabling the algorithm to efficiently scale to large environments. This approach also enables the use of customizable fields of view, for use with multiple types of sensors. Experimental results demonstrate the superiority of this approach in large environments when compared to the problem without options and alternative tools such as receding horizon planners. Given compute time for the proposed formulation is relatively high, a further approximated "lite" formulation is proposed. The lite formulation finds objects in a comparable number of steps with faster computation.

翻译：随着自主机器人系统在危险环境中的日益广泛应用，高效搜索与救援操作的需求愈发凸显。尽管已有显著进展，现有目标搜索文献仍难以克服长规划周期带来的困难，并难以应对传感器噪声等局限性。本研究提出一种新方法，将搜索问题建模为带选项的置信马尔可夫决策过程（BMDP-O），使蒙特卡洛树搜索（MCTS）成为应对大规模环境中这些挑战的有效工具。该公式引入了在感兴趣区域间移动的动作序列（选项），使算法能够高效扩展至大规模环境。同时，该方法支持可自定义的视场，适用于多种传感器类型。实验结果表明，与不带选项的基准问题及递推视界规划器等替代工具相比，本方法在大规模环境中具有显著优势。鉴于所提公式计算时间相对较高，本文进一步提出一种近似化的"轻量"公式，该公式能在类似步数内完成目标搜索，且计算速度更快。