Search Based Planning

Search-based planning uses graph search algorithms like A* to find optimal solutions in complex environments, addressing challenges in robotics, AI, and other domains. Current research focuses on improving efficiency and scalability for high-dimensional problems, such as multi-robot coordination and planning in cluttered 3D spaces, often incorporating heuristics, reinforcement learning, and adaptive sampling techniques to overcome computational limitations. These advancements are crucial for enabling autonomous systems in diverse applications, including autonomous navigation, multi-agent manipulation, and emergency response missions, by providing robust and efficient planning capabilities.