A Fuzzy Inference System on Probabilistic Roadmap for Multi-Robot Path Planning

Abstract

In recent years, multi-robot systems have been widely used in many applications such as warehouse inventory tracking, homecare, natural resource monitoring, emergency search and rescue operations, etc. Probabilistic roadmap (PRM) is a typical path planning algorithm that determines an optimal trajectory once the robot start and goal configurations are specified. However, when the number of robots in the system increases, it converges very slowly and may even fail to find the solution. Fuzzy inference system resembles human reasoning and decision-making process and is very robust and efficient when dealing with complicated situations. In this research, a fuzzy inference system is proposed and combined with the probabilistic roadmap algorithm for multi-robot path planning. Computer simulation results for three different environments show that, compared with the standard PRM algorithm, this approach is very effective to reduce computational cost which is especially important for real-time applications. In the most complicated scenario studied in this paper, the proposed approach can reduce the run time by $23.92 \%$ with a trade-off of increased average path length of $6.83 \%$.