Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter

Wang，Jincheng1,2; Xia，Lei1,3; Peng，Lei1; Li，Huiyun1; Cui，Yunduan1

2023-04-01

10.3390/drones7040228

Drones   影响因子和分区

2504-446X

This article tackles the computational burden of propagating uncertainties in the model predictive controller-based policy of the probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles system (USV). We proposed filtered probabilistic model predictive control using the unscented Kalman filter (FPMPC-UKF) that introduces the unscented Kalman filter (UKF) for a more efficient uncertainty propagation in MBRL. A USV control system based on FPMPC-UKF is developed and evaluated by position-keeping and target-reaching tasks in a real USV data-driven simulation. The experimental results demonstrate a significant superiority of the proposed method in balancing the control performance and computational burdens under different levels of disturbances compared with the related works of USV, and therefore indicate its potential in more challenging USV scenarios with limited computational resources.

Gaussian process model-based reinforcement learning unmanned surface vehicle

SCI

英语

其他

Science and Technology Development Fund[0015/2019/AKP];VNU Science and Technology Development Fund[0015/2019/AKP];Basic and Applied Basic Research Foundation of Guangdong Province[2020B515130004];National Key Research and Development Program of China[2020YFB2104300];National Natural Science Foundation of China[62103403];

Remote Sensing

Remote Sensing

WOS:000977457900001

MDPI

2-s2.0-85154056145

Scopus

1.Shenzhen Institute of Advanced Technology (SIAT),Chinese Academy of Sciences,Shenzhen,518055,China
2.College of Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.University of Chinese Academy of Sciences,Beijing,100049,China

Wang，Jincheng,Xia，Lei,Peng，Lei,et al. Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter[J]. Drones,2023,7(4).

Wang，Jincheng,Xia，Lei,Peng，Lei,Li，Huiyun,&Cui，Yunduan.(2023).Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter.Drones,7(4).

Wang，Jincheng,et al."Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter".Drones 7.4(2023).