ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning
The paper "ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning" by Ruiqi Zhang, Dingqi Zhang, and Mark W. Mueller presents a novel control solution for enabling robust quadcopter flight in close proximity scenarios. This research addresses the challenges posed by aerodynamic interactions between quadcopters, such as downwash effects, which are not straightforward to model using conventional aerodynamic modeling methods.
The proposed solution, termed ProxFly, integrates a residual reinforcement learning (RL) module atop a cascaded controller, called the basic controller. This hybrid controller system aims to generate high-level control commands that can compensate for aerodynamic disturbances and thrust loss, making ProxFly less reliant on precise system identification and well-tuned controller parameters.
Methodology
Fundamentally, ProxFly utilizes the residual RL technique. The basic controller provides the primary control input, which is adjusted by additional outputs from the residual module. This residual reinforcement learning approach has several advantages:
- Compensation for Model Inaccuracies: The residual RL module corrects the errors from the basic controller by learning the discrepancies during flight. This ensures the quadcopter’s robustness to external disturbances without requiring exact aerodynamic models.
- Ego-State Dependency for Communication Minimization: The method exclusively relies on the quadcopter's own state and controller outputs. There is no need for communication between quadcopters, which decreases reliance on bandwidth and reduces the complexity of multi-agent control systems.
- Domain Randomization: The training process involves domain randomization to ensure generalization across diverse system parameters. This facilitates ProxFly's adaptability to different dynamics during various flight scenarios.
Experimental Validation
The effectiveness of ProxFly was validated through a combination of simulation and real-world experiments under different proximity conditions. Simulated tests involved high-fidelity aerodynamic disturbances modeled based on existing aerodynamic data. These simulations confirmed ProxFly's ability to stabilize and accurately control the quadcopters despite significant downwash-induced thrust loss and external aerodynamic forces.
In real-world tests, ProxFly was compared with a basic controller and an advanced model-based controller, known as FB-AeroComp, which includes complex aerodynamic compensation. These experiments included tasks such as close proximity hovering, circular trajectory tracking, and quadcopter docking—a challenging task that introduces substantial aerodynamic disturbances due to extremely close proximity.
Results and Analysis
The results showed that:
- In close proximity hovering tasks, ProxFly reduced the root mean square error (RMSE) of position and attitude by 29.0% and 43.7%, respectively, compared to the basic controller.
- In circular trajectory tracking, ProxFly demonstrated comparative performance to FB-AeroComp, showcasing improvements in handling downwash effects without requiring explicit communication or precise model parameters.
- For aerial docking tasks, ProxFly successfully managed the demanding conditions, including impulses and despite permanent changes in dynamics, demonstrating high adaptability and robustness.
Implications and Future Directions
ProxFly demonstrates significant potential for applications requiring close proximity flight, such as collaborative mapping, payload transport, and in-air docking for quadcopter charging. The independence from precise aerodynamic modeling and minimized communication makes this approach particularly appealing for large-scale quadcopter swarms.
The robustness and adaptability of ProxFly suggest several practical and theoretical implications:
- Scalability: ProxFly can be leveraged for larger swarms or different quadcopter models with varied dynamics.
- Simplified Modeling Requirements: The reduced need for precise system identification can lower the barrier for deploying complex quadcopter systems in various real-world environments.
- Broader Application Spectrum: The approach could be extended to different aerial robotics applications where interaction effects are non-negligible.
Future research could focus on mitigating the oscillations caused by the residual module to avoid potential motor overheating. Additionally, verifying the approach under diverse hardware configurations and larger quadcopter swarms would extend the generalizability and applicability of ProxFly. Moreover, integrating ProxFly with other modern RL techniques, such as meta-reinforcement learning, may enhance its adaptability and performance further.
In conclusion, ProxFly offers a promising, adaptable, and effective solution for close proximity quadcopter flight control by combining classical control techniques with advanced reinforcement learning, thus advancing the robustness and efficiency of aerial robotics systems.