用模糊逻辑提升机器人搜索效率，兼顾精准与快速决策。

This paper introduces a novel concept, fuzzy-logic-based model predictive control (FLMPC), along with a multi-robot control approach for exploring unknown environments and locating targets. Traditional model predictive control (MPC) methods rely on Bayesian theory to represent environmental knowledge and optimize a stochastic cost function, often leading to high computational costs and lack of effectiveness in locating all the targets. Our approach instead leverages FLMPC and extends it to a bi-level parent-child architecture for enhanced coordination and extended decision making horizon. Extracting high-level information from probability distributions and local observations, FLMPC simplifies the optimization problem and significantly extends its operational horizon compared to other MPC methods. We conducted extensive simulations in unknown 2-dimensional environments with randomly placed obstacles and humans. We compared the performance and computation time of FLMPC against MPC with a stochastic cost function, then evaluated the impact of integrating the high-level parent FLMPC layer. The results indicate that our approaches significantly improve both performance and computation time, enhancing coordination of robots and reducing the impact of uncertainty in large-scale search and rescue environments.