Deep Reinforcement Learning-Based Path Control and Optimization for Unmanned Ships

Abstract

Unmanned ship navigates on the water in an autonomous or semiautonomous way, which can be widely used in maritime transportation, intelligence collection, maritime training and testing, reconnaissance, and evidence collection. In this paper, we use deep reinforcement learning to solve the optimization problem in the path planning and management of unmanned ships. Specifically, we take the waiting time (phase and duration) at the corner of the path as the optimization goal to minimize the total travel time of unmanned ships passing through the path. We propose a new reward function, which considers the environment and control delay of unmanned ships at the same time, which can reduce the coordination time between unmanned ships at the same time. In the simulation experiment, through the quantitative and qualitative results of deep reinforcement learning of unmanned ship navigation and path angle waiting, the effectiveness of our solution is verified.

Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery

“…This also indicates that the vegetation index by combining multisource satellite images is still a good measure of the long-term vegetation cover status. Therefore, based on the typical regional statistical results and the spatial distribution of NDVI and RVI of the four types of satellite images in the test area, there is a strong correlation between the long-term serial vegetation cover and the vegetation indices extracted from the multisource satellite images in the test area [ 17 ].…”

“…Several studies have shown that the vegetation indices extracted from satellite images can be a good measure of surface vegetation cover and its spatial and temporal evolution characteristics. For example, the extraction of vegetation indices using Landsat TM/OLI images [ 1 – 5 ], SPOT satellite images [ 6 – 8 ], HDS-1/2 satellite images [ 9 – 11 ], and Sentinel-2 satellite images [ 12 – 14 ] has provided basic data support for the spatial and temporal evolution of vegetation cover in the study area.…”

Research on Virtual Interactive Animation Design System Based on Deep Learning

“…The DRBM model can be considered as a two-layer model, where both the visible layer and the label layer are the input sample data, and the hidden layer can be used to sample the joint probability distribution and conditional probability distribution of the visible and label layer data. Since an animation belongs to only one style, the label layer can be coded as “single heat”; that is, the label layer is set as a binary neuron, and only the neuron corresponding to the label has a value of 1 and is active [ 21 , 22 ].…”

[Retracted] Design of Intelligent Diagnosis and Treatment System for Ophthalmic Diseases Based on Deep Neural Network Model

“…Since ocular pathologies often have no obvious symptoms in their early stages and are easily overlooked by patients, resulting in irreversible visual impairment of varying degrees by the time patients come to the clinic with ocular symptoms, routine screening, and early diagnosis of ocular diseases are critical [ 1 , 2 ]. Meanwhile, the rich biological information of fundus images can reflect other tissues, organs, or systems and is expected to be applied.…”