Novel Path Planning

Novel path planning research focuses on developing efficient and safe algorithms for navigating diverse environments, addressing challenges like multi-objective optimization and dynamic obstacles. Current efforts leverage techniques such as reinforcement learning (including model predictive and deep reinforcement learning), hybrid A* search algorithms enhanced with roadmaps or waypoints, and sampling-based methods like RRT*, often incorporating advanced map representations (e.g., hybrid 2D/2.5D maps). These advancements are crucial for improving the autonomy and performance of robots, autonomous vehicles, and even aircraft, impacting fields ranging from industrial automation and logistics to urban air mobility and assistive robotics.