Obstacle Rich Environment

Obstacle-rich environment navigation focuses on enabling robots, both individually and in swarms, to efficiently and safely traverse complex spaces cluttered with obstacles. Current research emphasizes robust and real-time planning algorithms, often incorporating control barrier functions, parallel optimization techniques (like ADMM), and machine learning models (e.g., transformers, neural networks) to handle uncertainty and dynamic obstacles. These advancements are crucial for improving the autonomy and reliability of robots in various applications, from autonomous driving and search-and-rescue to industrial automation and surgical robotics.