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Anqing Wang, Longwei Li, Haoliang Wang, Bing Han and Zhouhua Peng
In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from loc...
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Jia Wang, Tianyi Tao, Daohua Lu, Zhibin Wang and Rongtao Wang
The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging...
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Jinxiong Gao, Xu Geng, Yonghui Zhang and Jingbo Wang
Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constrain...
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Yanfeng Li, Hsin Guan, Xin Jia and Chunguang Duan
A scenario vehicle in autonomous driving simulations is a dynamic entity that is expected to perform trustworthy bidirectional interaction tasks with the autonomous vehicle under test. Modeling interactive behavior can not only facilitate better predicti...
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Justin Edwards and Mohamed El-Sharkawy
Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable acc...
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Ruinan Chen, Jie Hu, Xinkai Zhong, Minchao Zhang and Linglei Zhu
Existing environment modeling approaches and trajectory planning approaches for intelligent vehicles are difficult to adapt to multiple scenarios, as scenarios are diverse and changeable, which may lead to potential risks. This work proposes a cognitive ...
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Chengxi Wu, Yuewei Dai, Liang Shan and Zhiyu Zhu
This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a f...
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Jiqing Du, Dan Zhou and Sachiyo Arai
This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists o...
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Dan Yu, Hongjian Wang, Benyin Li, Zhao Wang, Jingfei Ren and Xiaoning Wang
The assessment of multiple incoming autonomous underwater vehicles (multi-AUVs) and threat prioritization are critical to underwater defense. To solve problems troubling multi-AUV threat assessment solutions, such as difficult data analysis, high subject...
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Nurul I. Sarkar and Sonia Gul
Recent advancements in unmanned aerial vehicles (UAVs) have proven UAVs to be an inevitable part of future networking and communications systems. While many researchers have proposed UAV-assisted solutions for improving traditional network performance by...
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