mlsanityData to 14 Oct 2026

Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout

arXiv 2411.06128 ↗First posted 9 Nov 2024cs.RO cs.AIcited by 14

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Abstract (arXiv)

In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses Dijkstra algorithm to plan the global optimal path, thus ensuring high navigation accuracy and significantly improving the efficiency of path planning. Specifically, PPO enables robots to quickly adapt and optimize action strategies in dynamic environments through its stable policy updating mechanism. Dijkstra's algorithm ensures global optimal path planning in static environment. Finally, through the comparison experiment and analysis of the proposed framework with the traditional algorithm, the results show that the PP-D method has significant advantages in improving the accuracy of navigation prediction and enhancing the robustness of the system. Especially in complex warehouse layout, PP-D method can find the optimal path more accurately and reduce collision and stagnation. This proves the reliability and effectiveness of the robot in the study of complex warehouse layout navigation algorithm.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 9 Nov 2024.

Builds on

23 of 50 references.

References · 23

  1. 2023Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Modelscited by 104
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  3. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 56
  4. 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitatecited by 47
  5. 2018Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagerycited by 43
  6. 2024MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editingcited by 38
  7. 2024Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformerscited by 35
  8. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
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  10. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  11. 2024Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understandingcited by 16
  12. 2024AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computingcited by 15
11 more
  1. 2024AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Waycited by 15
  2. 2024DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environmentcited by 11
  3. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  4. 2024Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERTcited by 9
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  11. 2024Research on Dynamic Data Flow Anomaly Detection based on Machine Learningcited by 2

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Cited by year

Citing papers per year: 2025: 14.

14 papers.

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  5. 2025Industrial Internet Robot Collaboration System and Edge Computing Optimizationcited by 3
  6. 2025Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systemscited by 2
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  8. 2025A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspectivecited by 1
  9. 2025Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Userscited by 1
  10. 2025Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervisioncited by 1
  11. 2025Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Modelscited by 1
  12. 2025Gradient Boosting Decision Tree with LSTM for Investment Predictioncited by 0
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