mlsanityData to 7 Oct 2026

Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning

arXiv 2408.16633 ↗First posted 29 Aug 2024cs.RO cs.AIcited by 19

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Abstract (arXiv)

With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing. This study focuses on automated picking systems in warehouses, utilizing deep learning and reinforcement learning technologies to enhance picking efficiency and accuracy while reducing system failure rates. Through empirical analysis, we demonstrate the effectiveness of these technologies in improving robot picking performance and adaptability to complex environments. The results show that the integrated machine learning model significantly outperforms traditional methods, effectively addressing the challenges of peak order processing, reducing operational errors, and improving overall logistics efficiency. Additionally, by analyzing environmental factors, this study further optimizes system design to ensure efficient and stable operation under variable conditions. This research not only provides innovative solutions for logistics automation but also offers a theoretical and empirical foundation for future technological development and application.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 29 Aug 2024.

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16 of 34 references.

References · 16

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  6. 2023Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networkscited by 22
  7. 2024A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Productcited by 22
  8. 2024Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learningcited by 17
  9. 2023Source-free Domain Adaptive Human Pose Estimationcited by 15
  10. 2024A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimationcited by 15
  11. 2024Attention Mechanism and Context Modeling System for Text Mining Machine Translationcited by 15
  12. 2024A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Modelcited by 12
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  1. 2022RAIN: RegulArization on Input and Network for Black-Box Domain Adaptationcited by 9
  2. 2024Exploiting Diffusion Prior for Out-of-Distribution Detectioncited by 7
  3. 2019Learning low-dimensional state embeddings and metastable clusters from time series datacited by 6
  4. 2023GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognitioncited by 3

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