A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads

Chen, Jingyu; Ma, Ruidong; Oyekan, John

doi:10.1016/j.robot.2023.104489

Cited by 7 publications

(1 citation statement)

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“…Real-world industrial environments are the source of the data, which spans several sectors and use cases. The study utilizes a blend of quantitative and qualitative methodologies, and extensive testing is conducted on deep learning algorithms to assess their effectiveness across several industrial domains [16]- [20]. The methodology combines analytical techniques, algorithm assessment, and practical data gathering to provide a thorough grasp of deep learning's implications for Industry 5.0.…”

Section: Goals Of the Researchmentioning

confidence: 99%

Deep Learning Algorithms in Industry 5.0: A Comprehensive Experimental Study

Shchepkina,

Chandramauli,

Ahuja

et al. 2024

BIO Web Conf.

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This extensive experimental research provides strong empirical proof of the revolutionary power of deep learning algorithms when integrated into Industry 5.0. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Transformers are a few examples of deep learning algorithms that have shown remarkable accuracy rates of 92.3%, 88.7%, and 95.1%, respectively. Furthermore, the processing durations, which vary between 15 and 25 milliseconds, confirm their ability to make decisions in real time. The abundance of various data accessible in Industry 5.0 is highlighted by data collection sources such as picture databases (300 GB), text corpora (150 GB), equipment records (250 GB), and IoT sensor data (500 GB). The significant energy savings, shown by 20% reductions across a range of machine types, highlight the financial and ecological advantages of deep learning integration. Moreover, the noteworthy improvements in production quality, exhibiting up to 50% reductions in defect rates, highlight the potential of deep learning in quality assurance. These results provide tangible proof of the critical roles deep learning algorithms play in streamlining production lines, increasing energy economy, and boosting product quality in the ever-changing Industry 5.0 environment.

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Section: Goals Of the Researchmentioning

confidence: 99%