Obstacle Free

Obstacle-free path planning focuses on generating safe and efficient trajectories for robots navigating complex environments, avoiding both static and dynamic obstacles. Current research emphasizes developing efficient algorithms for representing obstacle-free space, including methods based on convex decomposition, differentiable parametric corridors, and implicit non-convex representations, often coupled with reinforcement learning or vision-language models for improved robustness and adaptability. These advancements are crucial for enabling autonomous navigation in diverse settings, from industrial automation to autonomous driving and swarm robotics, improving safety and efficiency in various applications.