Real-time path planning for Mecanum-wheeled robots with type-2 fuzzy logic controller

Abstract

With complex dynamic characteristics in many variables changing during actual operation, a Mecanum-wheeled mobile robot (MWMR) causes many difficulties for path planning and control. In addition, in many applications, robots need to operate automatically and find the optimal path. This article proposes intelligent MWMR path planning based on the real-time rapidly exploring random tree* (RT-RRT*) and optimal interval type 2 fuzzy logic controller (IT2FLC). First, the path planning of the MWMRs in dynamic environments is developed by RT-RRT*. Second, the optimal IT2FLC is designed for MWMRs based on the genetic algorithm. The pre-treatment and post-treatment coefficients of IT2FLC are optimized with the goal of achieving the best trajectory quality of the robot. This helps the robot to operate independently and accurately, and complete tasks in environments with many complex disturbances. Various trajectories are given to test the performance of the proposed approach. The results show that the efficiency of the designed method is better than that of a type 1 fuzzy logic controller (traditional fuzzy logic controller) and proportional–integral–derivative under the same conditions.