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Enhanced geometry control powered by AI for UAVS with a robotic arm for compensating for disturbances
Khaled Oqda1, Eman M El-Gendy2, Hanaa Salem Marie3
1Mechatronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. khaledoqda@std.mans.edu.eg.
Scientific Reports
|May 22, 2026
Summary
This study introduces an AI-enhanced geometric control framework for stabilizing Unmanned Aerial Vehicles (UAVs) with robotic arms. The novel approach improves flight stability and disturbance rejection during complex aerial manipulation tasks.
Area of Science:
- Robotics
- Control Systems
- Artificial Intelligence
Background:
- Unmanned Aerial Vehicles (UAVs) with robotic manipulators enable advanced tasks in hazardous environments.
- Integrating robotic arms causes dynamic coupling and stability challenges due to shifting payloads and external disturbances.
- Conventional control strategies struggle with the nonlinear dynamics of UAV-manipulator systems.
Purpose of the Study:
- To propose an AI-enhanced geometric control framework for stabilizing UAV-manipulator systems during high-maneuverability operations.
- To enhance flight stability, attitude tracking, and disturbance rejection in aerial manipulation.
- To address the limitations of purely geometric controllers through AI integration.
Main Methods:
- A hybrid control framework combining Geometric Control on the SO(3) manifold with a Long Short-Term Memory (LSTM) neural network.
- Utilizing geometric control for robust attitude and position stabilization in the nonlinear configuration space.
- Employing an LSTM network to predict and compensate for real-time manipulator-induced disturbances and dynamic coupling effects.
Main Results:
- The AI-enhanced geometric approach demonstrated superior attitude tracking and disturbance rejection compared to PID and sliding mode control.
- Significantly reduced oscillations during complex maneuvers, ensuring stable flight and accurate attitude regulation.
- Validated effectiveness in simulations using a quadrotor UAV with a Cartesian robotic arm in a CoppeliaSim-MATLAB environment.
Conclusions:
- The proposed AI-enhanced geometric control framework effectively stabilizes UAV-manipulator systems under demanding conditions.
- This hybrid approach offers a robust solution for aerial manipulation tasks requiring high maneuverability and payload handling.
- The integration of AI with geometric control presents a promising direction for future advancements in autonomous aerial robotics.
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