Replanning Strategy

Replanning strategies in robotics aim to enable robots to adapt to unforeseen circumstances and dynamic environments by efficiently adjusting their plans during execution. Current research focuses on improving the speed and optimality of replanning algorithms, often employing sampling-based methods, probabilistic models (like ProDMPs), and integrating AI techniques such as deep reinforcement learning and large language models for higher-level decision-making. These advancements are crucial for enhancing the robustness and reliability of autonomous systems in complex and unpredictable real-world scenarios, impacting fields like autonomous navigation, multi-robot coordination, and human-robot collaboration.