Abstract
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.
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