Car Like Robot

Research on car-like robots focuses on developing autonomous navigation and control systems for these vehicles, primarily aiming to achieve accurate and efficient motion planning and execution in various environments. Current research emphasizes techniques like model predictive control (MPC), deep reinforcement learning (DRL), and local path planning algorithms integrated with advanced sensor data processing (e.g., LiDAR point clouds). These advancements are significant for improving autonomous driving technologies, offering potential benefits for applications ranging from miniature robotic platforms for research and education to larger-scale autonomous vehicles.