Path Planning and Motion Control for Accurate Positioning of Car-like Robots

TL;DR

This paper presents an integrated planning and control approach for car-like robots, combining a CC trajectory generator with NMPC to improve positioning accuracy, validated through simulations showing superior performance over traditional methods.

Contribution

It introduces a novel CC path planning method with explicit existence conditions and demonstrates enhanced positioning accuracy using NMPC with CC trajectories.

Findings

CC trajectories outperform Reeds-Shepp paths in accuracy

Feasibility of CC steering validated by simulations

Closed-loop system shows improved performance with CC references

Abstract

This paper investigates the planning and control for accurate positioning of car-like robots. We propose a solution that integrates two modules: a motion planner, facilitated by the rapidly-exploring random tree algorithm and continuous-curvature (CC) steering technique, generates a CC trajectory as a reference; and a nonlinear model predictive controller (NMPC) regulates the robot to accurately track the reference trajectory. Based on the $\mu$-tangency conditions in prior art, we derive explicit existence conditions and develop associated computation methods for a special class of CC paths which not only admit the same driving patterns as Reeds-Shepp paths but also consist of cusp-free clothoid turns. Afterwards, we create an autonomous vehicle parking scenario where the NMPC endeavors to follow the reference trajectory. Feasibility and computational efficiency of the CC steering are validated by numerical simulation. CarSim-Simulink joint simulations statistically verify that with exactly same NMPC, the closed-loop system with CC trajectories as references substantially outperforms the case where Reeds-Shepp trajectories are used as references.