COLREGs-compliant multiship collision avoidance based on deep reinforcement learning

Ocean Engineering - Tập 191 - Trang 106436 - 2019
Luman Zhao1, Myung-Il Roh2
1Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology, Norway
2Department of Naval Architecture and Ocean Engineering, Research Institute of Marine Systems Engineering, Seoul National University, Republic of Korea

Tài liệu tham khảo

Abdelaal, 2018, Nonlinear model predictive control for trajectory tracking and collision avoidance of underactuated vessels with disturbances, Ocean Eng., 160, 168, 10.1016/j.oceaneng.2018.04.026 Bhopale, 2019, Reinforcement learning based obstacle avoidance for autonomous underwater vehicle, J. Mar. Sci. Appl., 18, 228, 10.1007/s11804-019-00089-3 Chae, 2018, Autonomous braking system via deep reinforcement learning, IEEE Conf. Intell. Transp. Syst. Proc. ITSC, 1 Cheng, 2018, Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels, Neurocomputing, 272, 63, 10.1016/j.neucom.2017.06.066 Cui, 2019, Reinforcement learning ship autopilot: sample-efficient and model predictive control-based approach, vol. 11, 4 Eriksen, 2017, MPC-Based mid-level collision avoidance for ASVs using nonlinear programming, 1st Annu. IEEE Conf. Control Technol. Appl. CCTA 2017, 766 Everett, 2018, Motion planning among dynamic, decision-making agents with deep reinforcement learning, vol. 10, 1 Fossen, 2011 He, 2017, Quantitative analysis of COLREG rules and seamanship for autonomous collision avoidance at open sea, Ocean Eng., 140, 281, 10.1016/j.oceaneng.2017.05.029 Johansen, 2016, Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment, IEEE Trans. Intell. Transp. Syst., 17, 3407, 10.1109/TITS.2016.2551780 Kahn, 2017, Uncertainty-aware reinforcement learning for collision avoidance, vol. 9, 24 Kingma, 2014, Adam: a method for stochastic optimization, vol. 4, 14 Long, 2018, Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning, vol. 5, 21 Mou, 2010, Study on collision avoidance in busy waterways by using AIS data, Ocean Eng., 37, 483, 10.1016/j.oceaneng.2010.01.012 Blanke, 2002, 1 Schulman, 2015 Schulman, 2017 Shen, 2019, Automatic collision avoidance of multiple ships based on deep Q-learning, Appl. Ocean Res., 86, 268, 10.1016/j.apor.2019.02.020 Singla, 2018 Śmierzchalski, 2005, Ships' domains as collision risk at sea in the evolutionary method of trajectory planning Sutton, 2017 Tam, 2010, Collision risk assessment for ships, J. Mar. Sci. Technol., 15, 257, 10.1007/s00773-010-0089-7 Wang, 2018, Design, modeling, and nonlinear model predictive tracking control of a novel autonomous surface vehicle, IEEE Int. Conf. Robot. Autom., 6189 Wang, 2017, The ship maneuverability based collision avoidance dynamic support system in close-quarters situation, Ocean Eng., 146, 486, 10.1016/j.oceaneng.2017.08.034 Zhang, 2015, A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs, Ocean Eng., 105, 336, 10.1016/j.oceaneng.2015.06.054 Zhao, 2016, A real-time collision avoidance learning system for unmanned surface vessels, Neurocomputing, 182, 255, 10.1016/j.neucom.2015.12.028 Zhao, 2019, Control method for path following and collision avoidance of autonomous ship based on deep reinforcement learning, Accepted for Publication and appears in J. Mar. Sci. Technol. Taiwan, 27