UAV Path Planning

UAV path planning focuses on designing efficient and safe flight trajectories for unmanned aerial vehicles, optimizing for factors like energy consumption, mission completion time, and obstacle avoidance in diverse environments. Current research emphasizes robust algorithms, including deep reinforcement learning (e.g., DDPG, Q-learning), dynamic programming, and evolutionary algorithms, often incorporating elements like predictive modeling and multi-agent coordination to handle uncertainty and dynamic obstacles. These advancements are crucial for enabling autonomous UAV operations in challenging scenarios such as wildfire monitoring, bridge inspection, and urban air mobility, improving safety and efficiency in various applications.