Local Planner

Local planners are algorithms that enable robots and autonomous vehicles to navigate dynamically by generating safe and efficient trajectories in real-time, given a current position and a desired goal. Research focuses on improving the efficiency and safety of these planners, exploring methods like gap-based approaches, model predictive control, and machine learning techniques such as learning local heuristics to reduce computational burden and improve performance in complex environments. These advancements are crucial for enhancing the robustness and reliability of autonomous systems in various applications, including service robotics, warehouse automation, and autonomous driving.