A self-navigating robot using Fuzzy Petri nets

Abstract Petri nets (PNs) are capable of modeling nearly any conceivable system and can provide a better understanding of the idealized action sequence in which to most effectively describe or execute said system through their powerful analytical capabilities. However, because real world instances are rarely as consistent and ideal as simulated models, basic PN modeling and simulation properties may be insufficient in practical application. We remedy this through specialization in Fuzzy Petri nets (FPNs). Fuzzy logic is incorporated to better model a self-navigating robot algorithm, thanks to its versatile multi-valued logic reasoning. By using FPNs, it is possible to simulate, assess, and communicate the process and reasoning of the navigational algorithm and apply it to real world programming. In this paper, we propose a series of specific fuzzy algorithms intended to be implemented in concert on a mobile robot platform in order to optimize the sequence of actions needed for a given task, primarily the navigation of an unknown maze. A set of varied maze configurations were developed and simulated as PN and FPN models, providing a testing environment to examine the efficiency of several methodologies. Five methods, including an original proposal in this paper, were compared across 30,000 simulations, evaluating in particular performance in processing cost in time. Our experiments concluded with results suggesting a very competitive task completion time at a considerable fraction in processing cost compared to the closest performing alternatives.