This paper proposes a novel method based on Fuzzy Logic Systems (FLSs) optimized to maximize the area coverage of a tiling robot by reconfiguring per an obstacle considering the “Infi” concept. The consideration of an infinite number of reconfigurable shapes by a tiling robot is defined as the “Infi” concept. The major advantage of the proposed method over state-of-the-art “Infi” methods is the ability to tune based on cost measurements without demanding an explicit set of training data. The optimization techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), were used for tuning the FLSs. The FLSs tuned using these three techniques were compared with state of the art by considering hTetro through simulations. According to the statistical outcomes, the proposed method can significantly improve the area coverage compared to the conventional methods while providing performance and behavior similar to the existing “Infi” methods that demand training data. Thus, the work proposed in this paper would be of great interest for the development of a reconfigurable floor cleaning robot since the complete area coverage is a foremost feature expected from a floor cleaning robot.

本文提出了一种基于模糊逻辑系统 (FLS) 的新方法，该方法经过优化，通过考虑“Infi”概念重新配置每个障碍物来最大化平铺机器人的区域覆盖范围。平铺机器人对无限数量的可重构形状的考虑被定义为“Infi”概念。与最先进的“Infi”方法相比，所提出的方法的主要优点是能够根据成本测量进行调整，而无需明确的训练数据集。优化技术、遗传算法 (GA)、粒子群优化 (PSO) 和模拟退火 (SA) 用于调整 FLS。通过模拟考虑 hTetro，将使用这三种技术调整的 FLS 与现有技术进行了比较。根据统计结果，与传统方法相比，所提出的方法可以显着提高区域覆盖范围，同时提供类似于需要训练数据的现有“Infi”方法的性能和行为。因此，本文提出的工作对于可重构地板清洁机器人的开发具有重要意义，因为完整的区域覆盖是地板清洁机器人所期望的首要特征。