Reinforcement learning-based distributed optimal formation tracking control with obstacle avoidance for quadrotor UAVs

Key Points

• Optimal formation tracking control is achieved with reinforcement learning algorithms, enhancing quadrotor coordination.
• The control framework effectively integrates obstacle avoidance, increasing navigation safety in complex environments.
• Analysis employs a simulation approach to mimic real-world scenarios and assess UAV interactions and responses.
• Findings emphasize improved efficiency in UAV operations, suggesting further advancements in autonomous flight systems.