Efficient and Asymptotically Optimal Vehicle Motion Planning With Stochastic Template-Based RRT*

Kinodynamic motion planning plays a vital role in robotics, particularly in autonomous driving, where planned trajectories must satisfy kinematic and dynamic constraints while ensuring both safety and efficiency. Although existing kinodynamic RRT* algorithms achieve asymptotic optimality, their high computational cost often limits their practicality in high-dimensional or complex environments, such as autonomous driving scenarios. To enhance efficiency in such scenarios, motion templates with predefined action sequences have been proposed as a guiding strategy for planners. However, traditional fixed templates lack the flexibility and adaptability required to handle dynamic and diverse driving conditions, reducing their effectiveness in real-world applications. To overcome these limitations, we propose Stochastic Template-Based RRT* (ST-RRT*), a novel approach that introduces stochasticity into the template generation process. By dynamically generating templates guided by probabilistic models, ST-RRT* achieves efficient exploration, improves adaptability to complex constraints, and retains the asymptotic optimality guarantees of RRT*. We demonstrate the effectiveness of ST-RRT* through experiments in automotive environments, showcasing its ability to generate high-quality trajectories under stringent motion constraints. Additionally, we validate its generalizability by applying it to other kinodynamic planning scenarios, highlighting its efficiency, robustness, and versatility compared to state-of-the-art methods.