Abstract
This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized the dimensionality reduction of state space and solved the problem of dimension disaster. An artificial potential field was used to design the positive rewards of the algorithm to shorten the training time. According to the different requirements of the task, this study modified the rewards in the training process to obtain different paths. The path planning simulation and field tests were carried out. The results of the tests corroborated that the training time of the proposed method was shorter than that of the traditional method. The path obtained by simulation training was proved to be safe and effective.
Highlights
As a key technology in the marine industry, autonomous underwater vehicles (AUVs) have been given considerable attention and application [1]
In view of the existing problems in the present studies, this study proposed an improved hierarchical deep Q network (HDQN) method with the prioritized experience replay to realize the three-dimensional path planning of AUV
After launching an AUV, the path nodes obtained by global path planning are transmitted to the lower computer by radio [16]
Summary
As a key technology in the marine industry, autonomous underwater vehicles (AUVs) have been given considerable attention and application [1]. Petres [7] designed a continuous state, which used an anisotropic fast matching algorithm to complete the AUV path planning task This method only used linear evaluation function, which had certain limitations. Hiroshi et al [11] proposed a multi-layer training structure based on Q-Learning They carried out a planning simulation experiment on the R-ONE vehicle. Cheng et al [14] proposed a motion planning method based on DRL They used CNN to extract the characteristics of sensor information in order to make decisions on the motion. In view of the existing problems in the present studies, this study proposed an improved hierarchical deep Q network (HDQN) method with the prioritized experience replay to realize the three-dimensional path planning of AUV.
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