Abstract
Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.
Highlights
The autonomous underwater vehicle (AUV) is an important tool in the field of ocean development [1]
Harbor waters, and waterway waters make up more than 90% of AUVs navigation area, and each of them has its typical environmental characteristics
TphreoAvUe VthteesAt UreVsu'sltms foortiwonalpl tlraancnkiinngg. ability in underwater complex tasks, this paper proposes a three-layer AUV motion planning architecture
Summary
The autonomous underwater vehicle (AUV) is an important tool in the field of ocean development [1]. Considering that the choice of a fuzzy boundary is subjective, the generated path could not be guaranteed to be optimal The following year, they [5] proposed a new discrete centralized planning strategy based on glasius bio-inspired neural network for AUVs full coverage motion planning. Garcia-Garrido et al [6] planned a path to optimize scientific impact and navigation efficiency according to the complex space-time structure of ocean flow field and dynamic system. They pre-planned the Silbo glider’s mission to cross the North Atlantic from April 2016 to March 2017.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
