The involvement of computer-programmed autonomous mobile robots in real-time activities is emerging in the recent years. The actuation and interaction of the robots are controlled through optimized high-level programming to respond to environmental factors. Such robots require an optimized touring plan with a better response to understand the inherent conditions. In this article, fuzzy rule-based optimization for actuated touring (FOAT) is presented. FOAT is responsible for balancing the actuation and response of the mobile robot agents in touring and path exploration. The touring and self-decision analysis of the programmed robots is improved through FOAT by adapting the environmental conditions and then constructing rules for response. Different from the functions of line-based or other robot touring, the proposed actuated touring frames decisive rules based on the varying inputs. With the framed rules, the touring process of the robot is modified to achieve the best solution instantly adaptive to the environment. The performance of FOAT is verified through experiments and is then analyzed using the metrics: touring cost, obstacles hit ratio, time-lapse, and tour length.

近年来，计算机编程的自主移动机器人参与实时活动的趋势正在出现。机器人的驱动和交互通过优化的高级编程来控制，以响应环境因素。这样的机器人需要具有更好响应的优化的旅行计划，以了解内在条件。在本文中，提出了基于模糊规则的智能旅行优化（FOAT）。FOAT负责在旅行和路径探索中平衡移动机器人代理的启动和响应。通过调整环境条件，然后构造响应规则，可以通过FOAT改进对编程机器人的巡回和自决分析的能力。与基于行或其他机器人巡回演出的功能不同，提议的动感旅行车框架基于变化的输入具有决定性的规则。利用框架规则，可以修改机器人的游览过程，以实现即时适应环境的最佳解决方案。FOAT的性能通过实验进行验证，然后使用以下指标进行分析：巡回费用，障碍物撞击率，延时和游览时间。