Deep Reinforcement Learning Framework for Autonomous Surface Vehicles in Environmental Cleanup
Author(s)
Ro, Junghwan
Advisor(s)
Pradalier, Cedric
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Abstract
The water pollution from floating plastics poses significant environmental threats that require efficient solutions. ASV presents a promising solution to address this challenge. However, deploying DRL for ASV control in real-world environmental missions is underexplored due to simulation limitations and the sim-to-real gap. This thesis presents a DRL framework for ASVs focused on environmental missions, explicitly targeting the autonomous collection of floating waste. An open-source, highly parallelized hydrodynamics and buoyancy simulation environment is developed to facilitate large-scale training. By integrating system identification with domain randomization, we reduce the sim-to-real gap, enhancing the robustness and energy efficiency of the trained agents. The proposed approach is validated through simulation and real-world experiments, demonstrating improved task completion times and reduced energy consumption. Task experiments show that our approach reduces energy consumption by 13.1%, while reducing task completion time by 7.4%. These findings, supported by sharing our open-source implementation, have the potential to impact the efficiency and versatility of ASVs, contributing to environmental preservation efforts.
This thesis incorporates and expands on work previously published in a paper presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) in 2024. Significant portions of the content have been reused and adapted to fit the comprehensive format and depth required for the thesis.
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Date
2024-12-08
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