Real Time Motion Planning

Real-time motion planning focuses on generating collision-free and efficient paths for robots and autonomous vehicles in dynamic environments, aiming to achieve fast computation speeds crucial for practical applications. Current research emphasizes integrating advanced algorithms like Model Predictive Control (MPC), Rapidly-exploring Random Trees (RRT), and graph neural networks with techniques such as reachable set analysis and hierarchical planning to handle complex scenarios involving multiple agents or obstacles. These advancements are significantly impacting fields like robotics, autonomous driving, and industrial automation by enabling safer, more efficient, and adaptable systems capable of real-time decision-making and reactive control.