Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved Aquila Optimization Algorithm

Abstract

Uncertainty and complexity in the local path planning are hot topics. In this paper, a novel IAOFC algorithm is proposed for local path planning in the complex environment. Considering the uncertainty and complexity of local path planning, this paper uses interval type-2 fuzzy control to design path planning method, which can respond more quickly to the uncertainty of the environment and improve the computation speed and efficiency. To further improve the performance of the fuzzy controller, this paper uses an improved Aquila Optimizer (AO) algorithm to optimize the membership function of the interval type-2 fuzzy controller (renamed by IAOFC). By using the optimized fuzzy controller, the time cost and path cost can be reduced. In simulation experiments, the path planning in static environment is designed to verify the basic performance and efficiency of the algorithm, and the path planning in dynamic environment is validated to verify the robustness of the algorithm. Finally, the superiority of the IAOFC algorithm is proved by comparing it with some other algorithms. According to experiment results, IAOFC has an average cost reduction of 15% and 6% than other algorithms in static and dynamic environments, respectively.