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

arXiv: https://arxiv.org/abs/2411.06128 · HTML: https://mlsanity.com/paper/2411.06128/

## 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 (most cited first, 23 of 23)

- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
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- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing](https://mlsanity.com/paper/2404.19026/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm](https://mlsanity.com/paper/2405.06652/) (2024)
- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way](https://mlsanity.com/paper/2409.14577/) (2024)
- [DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment](https://mlsanity.com/paper/2409.03930/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT](https://mlsanity.com/paper/2410.01987/) (2024)
- [Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields](https://mlsanity.com/paper/2406.18311/) (2024)
- [Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model](https://mlsanity.com/paper/2410.17545/) (2024)
- [Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks](https://mlsanity.com/paper/2310.17805/) (2023)
- [Learning low-dimensional state embeddings and metastable clusters from time series data](https://mlsanity.com/paper/1906.00302/) (2019)
- [Predicting NVIDIA's Next-Day Stock Price: A Comparative Analysis of LSTM, MLP, ARIMA, and ARIMA-GARCH Models](https://mlsanity.com/paper/2405.08284/) (2024)
- [An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction](https://mlsanity.com/paper/2409.16676/) (2024)
- [Research on Dynamic Data Flow Anomaly Detection based on Machine Learning](https://mlsanity.com/paper/2409.14796/) (2024)

## Built on by (most cited first, 14 of 14)

- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method](https://mlsanity.com/paper/2504.07983/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)
- [Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems](https://mlsanity.com/paper/2507.17774/) (2025)
- [Efficient Cold-Start Recommendation via BPE Token-Level Embedding Initialization with LLM](https://mlsanity.com/paper/2509.13179/) (2025)
- [A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective](https://mlsanity.com/paper/2509.03673/) (2025)
- [Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users](https://mlsanity.com/paper/2509.09066/) (2025)
- [Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision](https://mlsanity.com/paper/2509.11476/) (2025)
- [Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Models](https://mlsanity.com/paper/2512.22249/) (2025)
- [Gradient Boosting Decision Tree with LSTM for Investment Prediction](https://mlsanity.com/paper/2505.23084/) (2025)
- [Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms](https://mlsanity.com/paper/2509.04751/) (2025)
- [AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control](https://mlsanity.com/paper/2510.05443/) (2025)

## 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.