Implementing the deep RL-based controller on real-world drone platforms beyond the experimental test stand setup poses several challenges and considerations. One key challenge is the need for robustness and adaptability of the controller to real-world uncertainties such as wind disturbances, sensor noise, and communication delays. Real-world drones operate in dynamic and unpredictable environments, requiring the controller to handle these uncertainties effectively.
Another consideration is the computational complexity and real-time performance of the deep RL algorithm on onboard drone hardware. Deep RL algorithms can be computationally intensive, and optimizing them for real-time execution on resource-constrained drone platforms is crucial. Efficient implementation, hardware acceleration, and optimization techniques are essential to ensure the controller's responsiveness and stability during flight.
Furthermore, the integration of additional sensors, such as force/torque sensors, LiDAR, or cameras, can enhance the controller's perception capabilities and improve the system's overall performance. Incorporating sensor fusion techniques and advanced perception algorithms can provide the controller with more comprehensive environmental awareness, enabling better decision-making and control in complex scenarios.
Overall, transitioning the deep RL-based controller from simulation to real-world drone platforms requires addressing challenges related to robustness, computational efficiency, sensor integration, and real-time performance to ensure safe and reliable operation in practical applications.