Efficient Trajectory

Efficient trajectory planning focuses on generating optimal paths for robots and vehicles, minimizing metrics like time, energy consumption, or control effort while adhering to constraints such as collision avoidance and dynamic limitations. Current research emphasizes integrating advanced algorithms like rapidly-exploring random trees (RRT*), neural networks for heuristic prediction and approximation, and model predictive control (MPC) with control Lyapunov functions (CLFs) for enhanced safety and robustness. These advancements are crucial for improving the performance and reliability of autonomous systems in diverse applications, ranging from drone racing and asteroid exploration to robotic manipulation and autonomous driving.