Fuzzy based self-tuned move lengths for enhanced performance of gas source localization algorithm

Abstract

The world has witnessed a lot of catastrophes in recent times due to chemical gas leaks. The core problem is untimely or sudden happenings of calamity for which humans are not prepared to take appropriate actions. Hence robotic gas source localization can be considered as an alternative to prevent such catastrophes. This paper presents an improved approach to an existing chemotactic plume tracing algorithm with self-tuned move length/step size. The technique uses the proposed fuzzy inference model to produce the move lengths for the next walk based on the input of gas concentration magnitude in the present state. The move lengths correspond to either the plume finding or plume tracing stage with which a mobile robot surges for the next step. Dynamic plumes under eight different simulated environments are created to evaluate the proposed approach rather than plumes in laminar flow for a more realistic case. Performance analysis of the algorithm is based on success rate with self-tuned move length compared with fixed move length. In addition, there is an analysis of step size parameters that vary concerning a particular environmental condition. Results show that adaptive step size can increase the success rate of the plume tracing algorithm and consequently improve search performance and efficiency.