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.

翻译：本文提出了一种新颖的概念——基于模糊逻辑的模型预测控制（FLMPC），以及一种用于探索未知环境和定位目标的多机器人控制方法。传统的模型预测控制（MPC）方法依赖贝叶斯理论来表示环境知识并优化随机成本函数，这通常导致较高的计算成本且在定位所有目标方面缺乏有效性。我们的方法转而利用FLMPC，并将其扩展为一种双层父子架构，以增强协调能力并延长决策视野。FLMPC从概率分布和局部观测中提取高层信息，简化了优化问题，并与其他MPC方法相比显著扩展了其操作视野。我们在具有随机放置障碍物和人员的未知二维环境中进行了大量仿真。我们将FLMPC与采用随机成本函数的MPC在性能和计算时间上进行了比较，随后评估了集成高层父FLMPC层的影响。结果表明，我们的方法显著提升了性能和计算时间，增强了机器人的协调性，并降低了大规模搜索与救援环境中不确定性的影响。