Path Planning and Tracking Control of Car-like Robot Based on Improved NSGA-III and Fuzzy Sliding Mode Control

Abstract

<div class="section abstract"><div class="htmlview paragraph">In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem. Furthermore, the chattering problem of sliding mode control is eliminated by introducing fuzzy control, and a sliding mode controller with fuzzy control is also applied for the lateral path tracking control of car-like robots. To verify the effectiveness of the proposed methods, simulation experiments are carried out for the planning and control layers, respectively. Comparing the improved NSGA-III with NSGA-III, HV decreased by 12.47%, SP increased by 1.17%, and the number of iterations decreased by 20.59% on average. The results show that the improved NSGA-III sacrifices part of the population diversity but has a more significant improvement in the convergence and accurate path predictions. Furthermore, the lateral path tracking controller effectively solved the chattering problem and reduced the lateral deviation by 29.35% and 52.53%, under the standard double-line-change working condition with road adhesion coefficients of 0.8 and 0.2. In addition, the proposed planning and control methods in this study can cooperate under medium and low speed conditions which is suitable for most application scenarios of car-like robots.</div></div>