Quadrotor Motion Planning

Quadrotor motion planning focuses on generating safe and efficient flight trajectories for these agile robots, particularly in complex and dynamic environments. Current research emphasizes robust algorithms that integrate global path planning (e.g., using visibility graphs or kinodynamic search) with local trajectory optimization (e.g., model predictive control, B-spline optimization) and often incorporate machine learning techniques like reinforcement learning or neural networks for improved adaptability and performance. This field is crucial for advancing autonomous flight capabilities in applications such as search and rescue, aerial surveillance, and drone racing, driving improvements in speed, safety, and robustness of quadrotor navigation.