Smart Path Generation using Model Predictive Control

Abstract

This paper presents an investigation into path generation techniques using Model Predictive Control (MPC) and Pure Pursuit algorithms, implemented and evaluated in a simulated environment using Python. The objective of the study was to compare the performance of these two approaches in terms of path tracking and obstacle avoidance. The research focused on the applicability of MPC and Pure Pursuit algorithms in autonomous navigation systems, with a specific emphasis on addressing the challenge of generating smooth and dynamically feasible paths while ensuring collision avoidance. The simulation environment provided a platform for conducting experiments, allowing for testing and analysis of the algorithms. The results of the study demonstrated that MPC successfully generated paths while effectively avoiding obstacles. The MPC algorithm exhibited robustness and adaptability to dynamically changing environments, allowing the autonomous agent to navigate through complex scenarios. However, the investigation also revealed a limitation with Pure Pursuit in terms of curvature volatility. The Pure Pursuit algorithm showed inconsistent performance due to abrupt changes in curvature, which impacted the smoothness and stability of path tracking. Overall, this research highlights the significance of selecting an appropriate path generation algorithm based on the specific requirements of the autonomous navigation system. The study serves as a foundation for future investigations and advancements in path planning and control techniques, enabling the development of more efficient and reliable autonomous systems.