DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories
This provides a tool for researchers and practitioners in robotics and space applications to test autonomous navigation systems in micro-gravity emulations, though it is incremental as it applies existing DRL methods to a specific domain.
The paper tackles the problem of controlling floating platforms for space navigation testing by developing a deep reinforcement learning suite that achieves robustness and transferability from simulation to reality, with advantages like fast training and ROS integration.
This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate micro-gravity environments on Earth, useful to test autonomous navigation systems for space applications. Our approach addresses the system and environmental uncertainties in controlling such platforms by training policies capable of precise maneuvers amid dynamic and unpredictable conditions. Leveraging Deep Reinforcement Learning (DRL) techniques, our suite achieves robustness, adaptability, and good transferability from simulation to reality. Our deep reinforcement learning framework provides advantages such as fast training times, large-scale testing capabilities, rich visualization options, and ROS bindings for integration with real-world robotic systems. Being open access, our suite serves as a comprehensive platform for practitioners who want to replicate similar research in their own simulated environments and labs.