Local Path Planning

Local path planning focuses on enabling robots to navigate dynamically and safely within their immediate surroundings, generating collision-free trajectories to reach a goal. Current research emphasizes improving the efficiency and robustness of these planners, particularly through the use of deep reinforcement learning, model predictive control, and advanced search algorithms like rapidly-exploring random trees (RRT) and A*, often incorporating sensor data and semantic understanding of the environment. These advancements are crucial for enhancing the autonomy and reliability of robots in diverse applications, ranging from autonomous vehicles and drone swarms to service robots and mobile manipulators operating in complex, unstructured environments.