Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods
The study compared classical planning methods and Reinforcement Learning (RL) for motion planning around dynamic obstacles. It found that uncertainty in how obstacles change over time, not just limited visibility, is the main difficulty. RL performed better than classical methods when obstacles were stochastic, showing it is more practical for real-time use under uncertain conditions.