自然水域における自律型水上艇の深層強化学習に基づく追従制御
Deep Reinforcement Learning Based Tracking Control of an Autonomous Surface Vessel in Natural Waters
深層強化学習で自律型水上艇の軌道追従コントローラを訓練し、実環境で非線形モデル予測制御と比較して外乱抑制性能が優れることを示した。
著者: Wei Wang, Xiaojing Cao, Alejandro Gonzalez-Garcia, Lianhao Yin, Niklas Hagemann, Yuanyuan Qiao, Carlo Ratti, Daniela Rus
分類: cs.RO
原文アブストラクト
Accurate control of autonomous marine robots still poses challenges due to the complex dynamics of the environment. In this paper, we propose a Deep Reinforcement Learning (DRL) approach to train a controller for autonomous surface vessel (ASV) trajectory tracking and compare its performance with an advanced nonlinear model predictive controller (NMPC) in real environments. Taking into account environmental disturbances (e.g., wind, waves, and currents), noisy measurements, and non-ideal actuators presented in the physical ASV, several effective reward functions for DRL tracking control policies are carefully designed. The control policies were trained in a simulation environment with diverse tracking trajectories and disturbances. The performance of the DRL controller has been verified and compared with the NMPC in both simulations with model-based environmental disturbances and in natural waters. Simulations show that the DRL controller has 53.33% lower tracking error than that of NMPC. Experimental results further show that, compared to NMPC, the DRL controller has 35.51% lower tracking error, indicating that DRL controllers offer better disturbance rejection in river environments than NMPC.
