Path planning for an identification mission of an Autonomous Underwater Vehicle in a lemniscate form

Barua, Ayushman; Kalwa, Jörg; Shardt, Yuri A.W.; Glotzbach, Thomas

doi:10.1016/j.ifacol.2018.09.503

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…There have been several approaches to modelling ocean currents. In most literature [21], [25], [26], [27], [28], [29], ocean current is assumed to be constant, irrational and bounded The body-fixed relative velocity of the AUV is therefore given as…”

Section: Ocean Currentsmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

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Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented.INDEX TERMS Machine learning, local path planning, autonomous underwater vehicle (AUV), real-time path planning, underwater.

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Section: Ocean Currentsmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

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“…For example, the multi-robot task system [72] aims at performing tasks automatically by control aerial and ground vehicles. The robot path planning [73] needs the help of AI technology to plan the optimal path, such as Ayushman et al [74] used the method of "chase the rabbit" to plan the path of the lemniscate shape, and the real underwater experiment by an autonomous underwater vehicle (AUV) showed that the algorithm has faster calculation speed.…”

Section: Agricultural Robot Controlmentioning

confidence: 99%

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Wang

et al. 2021

Agriculture

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Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.

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RETRACTED ARTICLE: Road vehicle recognition algorithm in safety assistant driving based on artificial intelligence

Liang

2021

Soft Comput

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Path planning for an identification mission of an Autonomous Underwater Vehicle in a lemniscate form

Cited by 6 publications

References 6 publications

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

RETRACTED ARTICLE: Road vehicle recognition algorithm in safety assistant driving based on artificial intelligence

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