Improvement of the TEB Algorithm for Local Path Planning of Car-like Mobile Robots Based on Fuzzy Logic Control
Lei Chen, Rui Liu, Daiyang Jia, Sijing Xian, Ma Guo
Abstract
TEB (timed elastic band) can efficiently generate optimal trajectories that match the motion characteristics of car-like robots. However, the quality of the generated trajectories is often unstable, and they sometimes violate boundary conditions. Therefore, this paper proposes a fuzzy logic control–TEB algorithm (FLC-TEB). This method adds smoothness and jerk objectives to make the trajectory generated by TEB smoother and the control more stable. Building on this, a fuzzy controller is proposed based on the kinematic constraints of car-like robots. It uses the narrowness and turning complexity of the trajectory as inputs to dynamically adjust the weights of TEB’s internal objectives to obtain stable and high-quality trajectories in different environments. The results of real car-like robot tests show that compared to the classical TEB, FLC-TEB increased the trajectory time by 16% but reduced the trajectory length by 16%. The trajectory smoothness was significantly improved, the change in the turning angle on the trajectory was reduced by 39%, the smoothness of the linear velocity increased by 71%, and the smoothness of the angular velocity increased by 38%, with no reverse movement occurring. This indicates that when planning trajectories for car-like mobile robots, while FLC-TEB slightly increases the total trajectory time, it provides more stable, smoother, and shorter trajectories compared to the classical TEB.