Accurate modeling and simulation of autonomous underwater vehicle （AUV） is essential for autonomous control and maneuverability research. In this paper, a mini AUV- ＂MAUV-Ⅱ＂ was researched and the nonlinear mathe...Accurate modeling and simulation of autonomous underwater vehicle （AUV） is essential for autonomous control and maneuverability research. In this paper, a mini AUV- ＂MAUV-Ⅱ＂ was researched and the nonlinear mathematic model of the AUV in spatial motion was derived based on momentum theorem. The forces acting on AUV were resolved to several modules which were expressed in matrix form. Based on the motion model and combined with virtual reality technology, a motion simulation system was constructed. Considering the characteristic of ＂MAUV-Ⅱ ＂, the heading control and depth control were simulated by adopting S-surface control method. A long distance traveling simulation experiment based on target planning was also done. The simulation results show that the ＂MAUV-Ⅱ＂ has good spatial maneuverability, and verify the feasibility and reliability of control software.展开更多
Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the ...Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the navigation test of the self-propelled model, the complex environment including various port facilities, navigation facilities, and the ships nearby must be considered carefully, because in this dense environment the impact of sea waves and winds on the model is particularly significant. In order to improve the security of the self-propelled model, this paper introduces the Q learning based on reinforcement learning combined with chaotic ideas for the model＇s collision avoidance, in order to improve the reliability of the local path planning. Simulation and sea test results show that this algorithm is a better solution for collision avoidance of the self navigation model under the interference of sea winds and waves with good adaptability.展开更多
基金Supported by National Natural Science Foundation under Grant No.50879014
文摘Accurate modeling and simulation of autonomous underwater vehicle （AUV） is essential for autonomous control and maneuverability research. In this paper, a mini AUV- ＂MAUV-Ⅱ＂ was researched and the nonlinear mathematic model of the AUV in spatial motion was derived based on momentum theorem. The forces acting on AUV were resolved to several modules which were expressed in matrix form. Based on the motion model and combined with virtual reality technology, a motion simulation system was constructed. Considering the characteristic of ＂MAUV-Ⅱ ＂, the heading control and depth control were simulated by adopting S-surface control method. A long distance traveling simulation experiment based on target planning was also done. The simulation results show that the ＂MAUV-Ⅱ＂ has good spatial maneuverability, and verify the feasibility and reliability of control software.
基金Foundation item： Supported by the National Natural Science Foundation of China under Grant No.61100005.
文摘Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the navigation test of the self-propelled model, the complex environment including various port facilities, navigation facilities, and the ships nearby must be considered carefully, because in this dense environment the impact of sea waves and winds on the model is particularly significant. In order to improve the security of the self-propelled model, this paper introduces the Q learning based on reinforcement learning combined with chaotic ideas for the model＇s collision avoidance, in order to improve the reliability of the local path planning. Simulation and sea test results show that this algorithm is a better solution for collision avoidance of the self navigation model under the interference of sea winds and waves with good adaptability.
