Hybrid Motion Planning

Hybrid motion planning integrates diverse planning techniques, such as sampling-based, optimization-based, and learning-based methods, to overcome the limitations of individual approaches. Current research focuses on combining these methods for improved efficiency, safety, and robustness in applications like autonomous driving and robotic manipulation, often employing architectures like neural networks (e.g., MLPs, CVAE) alongside algorithms such as A*, RRT*, and Model Predictive Control (MPC). This interdisciplinary approach yields more adaptable and effective motion planning solutions for complex, dynamic environments, impacting fields ranging from robotics and autonomous vehicles to intelligent manufacturing.