A path planning method for unmanned ships based on deep reinforcement learning and considering marine environment elements

Abstract

The invention discloses a path planning method for an unmanned ship based on deep reinforcement learning and taking into account marine environment elements. The Bayesian network evaluates the maximum value of the unmanned ship's wind, wave and current; S3 reorganizes the AIS data in the target sea area to train the network, and obtains the optimized experience pool and preliminary network parameters; S4 inputs the state feature vector of the unmanned ship to the deep reinforcement learning module Carry out algorithm iterations, update network parameters, and output actions; S5 runs for 15 seconds for each iteration of the unmanned ship, and update data when the cumulative time reaches 1 hour; S6 ends the iteration when the unmanned ship reaches the target point, and outputs the path. The invention fully considers the influence of marine environment elements on the unmanned ship's navigation, and is more in line with the actual long-distance voyage of the unmanned ship. It can allow the unmanned ship to simultaneously consider environmental elements and obstacle information under harsh sea conditions, and obtain a high-quality safety.

Description

technical field [0001] This patent relates to the field of unmanned ship path planning, in particular to an unmanned ship path planning method based on deep reinforcement learning and taking into account marine environment elements. Background technique [0002] Relying on the development of artificial intelligence control technology, unmanned ships have made breakthroughs in many technical fields, and gradually entered people's field of vision, began to undertake tasks such as ocean exploration and data acquisition, and gradually developed into the maritime transportation industry. [0003] Currently published patents: CN109657863A, CN109726866A, and CN107289939A all propose better path planning methods in this field, but generally only consider the impact of obstacles on unmanned ships. And each unmanned ship has the limit value of wind and waves according to its own material, structure and draft. When a ship sails in the sea, avoiding dangerous marine environment element...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem that many unmanned ship path planning methods proposed so far do not consider the marine environment elements, the present invention provides an unmanned ship path planning method based on deep reinforcement learning and taking into account the marine environment elements, fully considering the real marine environment elements and maritime obstacles, combined with deep reinforcement learning methods to plan a safe and efficient driving path for unmanned ships

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