A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors.

Area of Science:

• Robotics
• Control Systems
• Aerospace Engineering

Background:

• Quadrotor state estimation is crucial for autonomous navigation.
• Traditional sensor fusion methods (e.g., LIDAR/IMU) are susceptible to sensor faults.
• Accurate drag modeling is essential for high-performance quadrotor flight dynamics.

Purpose of the Study:

• To develop a drag model-aided fault-tolerant state estimation method for quadrotors.
• To improve the accuracy and robustness of quadrotor state estimation in the presence of sensor faults.
• To enhance the performance of quadrotor navigation systems.

Main Methods:

• Improved drag model incorporating angular rate and acceleration terms related to flight maneuvers.
• Fusion of drag model, Light Detection and Ranging (LIDAR), and Inertial Measurement Unit (IMU) data using a Federal Kalman filter.
• Fault detection, isolation, and compensation for LIDAR estimation errors.
• Estimation and compensation of disturbances affecting the drag model.

Main Results:

• Enhanced accuracy in velocity and position estimation compared to traditional LIDAR/IMU fusion.
• Demonstrated robustness against LIDAR estimation faults through experimental validation.
• Successful compensation for drag model disturbances and sensor failures.
• Improved overall state estimation performance for quadrotors.

Conclusions:

• The proposed drag model-aided fault-tolerant estimation method significantly improves quadrotor state estimation accuracy and reliability.
• The integration of an enhanced drag model and fault tolerance mechanisms provides a robust solution for real-world quadrotor applications.
• This approach offers a promising direction for advancing autonomous quadrotor navigation and control systems.