Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan, Sifat; Choi, Wooyeol

doi:10.1109/access.2022.3157626

Cited by 76 publications

(31 citation statements)

References 134 publications

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“…Lu et al 10.3389/fpls.2022.1009630 Frontiers in Plant Science 14 frontiersin.org limitations remain. The geographical extent is confined to the field size, and the consequent localization and individual variability in crop phenotypes limit the portability of monitoring models based on UAV remote sensing (Rezwan and Choi, 2022), so it is impossible to duplicate the inversion laws observed at larger scales or other sites. It is challenging to overcome regional disparities in numerous elements, such as crop types, natural environmental conditions, and human management practices, using satellite remote sensing (Messina and Modica, 2020;Zhou et al, 2020).…”

Section: Challenges and Prospective Researchmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

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Section: Challenges and Prospective Researchmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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“…Deep learning algorithms have been used to develop perception and decision-making systems that enable autonomous flying vehicles to perform complex tasks with high accuracy. For example, deep learning algorithms can be trained to recognize and classify objects in real-time, enabling autonomous flying vehicles to identify obstacles and avoid them [116].…”

Section: Autonomous Flightmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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“…Still, it has a restriction in that it gives a broad overview of all techniques and does not focus on individual lines. The work of [18] surveyed UAVs of different designs with different AI techniques, but it did not overview all methods. Furthermore, the work of [19] defined vital topics connected to UAVs and contemporary machine learning methods and presented a list of relevant courses and surveys.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled UAV Communications: Challenges and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks' high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixth-generation (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.INDEX TERMS Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), and reinforcement learning (RL).

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Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Cited by 76 publications

References 134 publications

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

AI-Enabled UAV Communications: Challenges and Future Directions

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