水下无人系统学报2026，Vol.34Issue(4)：687-700,14.DOI:10.11993/j.issn.2096-3920.2026-0028
基于AMDQN的AUV遍历路径规划方法
AUV Traversal Planning Method Based on AMDQN
庞舟岐 1林晓波 2耿士程 1郝程鹏1
作者信息
- 1. 中国科学院声学研究所,北京,100190||中国科学院大学,北京,100049
- 2. 中国科学院声学研究所,北京,100190
- 折叠
摘要
Abstract
To improve the traversal path planning capability of autonomous undersea vehicle(AUV),this paper proposed an AUV traversal path planning algorithm based on an advanced Munchausen deep Q-network(AMDQN).First,the paper established a traversal planning environment suitable for practical scenarios.On the one hand,the ray coverage method was used instead of the traditional rectangular grid modeling method to improve modeling accuracy;on the other hand,the optional action set of the AUV was constrained to limit its maneuverability.Second,each component of the algorithm was carefully designed under the above environment.Specifically,in the state space design,the paper fused vague global environmental information,accurate local environmental information centered on the AUV,and the AUV's own position information to systematically represent the environment.In the reward function design,rule penalty and edge guidance were introduced,enabling the AUV to stably improve coverage along environmental edges.For the parameter update method,an adaptive temperature parameter update strategy was designed based on DQN,while the multi-step reward and a dueling network architecture were introduced to alleviate training variance.During the training process,a soft reset of fully connected layer parameters was adopted to mitigate the local optimum problem.The simulation results show that the proposed algorithm has stronger environmental adaptability and higher traversal coverage than traditional methods and unimproved reinforcement learning algorithms.关键词
自主水下航行器/路径规划/强化学习/改进的蒙豪森深度Q网络Key words
autonomous undersea vehicle/path planning/reinforcement learning/advanced Munchausen deep Q-network分类
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庞舟岐,林晓波,耿士程,郝程鹏..基于AMDQN的AUV遍历路径规划方法[J].水下无人系统学报,2026,34(4):687-700,14.
