A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

Yuan, Jianya; Wang, Hongjian; Lin, Changjian; Liu, Dawei; Yu, Dan

doi:10.1109/access.2019.2894626

Cited by 70 publications

(42 citation statements)

References 29 publications

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“…At the same time, the network can transfer the knowledge to new scenarios [21]. Yuan et al propose a dynamic path planning method based on a gated recurrent unit-recurrent neural network for path planning in an undiscovered space [22]. Tai et al design a hierarchical structure that adopts a convolutional neural network to avoid indoor obstacles [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

et al. 2020

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The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The blackhole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.

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Section: Literature Reviewmentioning

confidence: 99%

Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

et al. 2020

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“…Other health areas such as antibiotic resistance outbreaks [48] and influenza outbreaks [49,50] utilized multivariate regression models. Different algorithms such as deep neural network [51,52], long short-term memory model (LSTM) [53] and gated recurrent unit (GRU)-based model [54] have been successfully applied in various forecasts. The methods rely on specific-less estimation error and running time on data sets with characteristics of multivariate, sequential and time-series data.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

et al. 2020

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The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

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“…Other health areas such as antibiotic resistance outbreaks [41] and influenza outbreaks [42,43] are also used multivariate regression models. Different algorithms such as deep neural network [44,45], long short-term memory model (LSTM) [46], and gated recurrent Unit(GRU)-based model [47] are successfully applied in various forecasting. The methods rely on specific less estimation error and running time on artificial network suitable data sets with characteristics of multivariate, sequential and time-series.…”

Section: (C) Multivariate Regression In Aimentioning

confidence: 99%

Neural Network Based Country Wise Risk Prediction of COVID-19

Pal¹,

Sekh²,

Kar³

et al. 2020

Preprint

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The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

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A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

Cited by 70 publications

References 29 publications

Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

Path Planning Method With Improved Artificial Potential Field—A Reinforcement Learning Perspective

Neural Network Based Country Wise Risk Prediction of COVID-19

Neural Network Based Country Wise Risk Prediction of COVID-19

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