# Multi-Dimensional AGV Path Planning in 3D Warehouses Using Ant Colony Optimization and Advanced Neural Networks

arXiv: https://arxiv.org/abs/2504.01985 · HTML: https://mlsanity.com/paper/2504.01985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2302.08224/) (2023)
- [Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization](https://mlsanity.com/paper/2310.07985/) (2023)
- [DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems](https://mlsanity.com/paper/2210.04123/) (2022)
- [Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems](https://mlsanity.com/paper/2012.10638/) (2020)
- [Simulation-guided Beam Search for Neural Combinatorial Optimization](https://mlsanity.com/paper/2207.06190/) (2022)
- [DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization](https://mlsanity.com/paper/2309.14032/) (2023)
- [The Transformer Network for the Traveling Salesman Problem](https://mlsanity.com/paper/2103.03012/) (2021)
- [Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem](https://mlsanity.com/paper/2304.09407/) (2023)
- [Self-Improved Learning for Scalable Neural Combinatorial Optimization](https://mlsanity.com/paper/2403.19561/) (2024)
- [How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem](https://mlsanity.com/paper/2209.10913/) (2022)
- [ASP: Learn a Universal Neural Solver!](https://mlsanity.com/paper/2303.00466/) (2023)
- [Neural Combinatorial Optimization: a New Player in the Field](https://mlsanity.com/paper/2205.01356/) (2022)
- [Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation](https://mlsanity.com/paper/2412.10163/) (2024)

## Abstract (arXiv)

Within modern warehouse scenarios, the rapid expansion of e-commerce and increasingly complex, multi-level storage environments have exposed the limitations of traditional AGV (Automated Guided Vehicle) path planning methods--often reliant on static 2D models and expert-tuned heuristics that struggle to handle dynamic traffic and congestion. Addressing these limitations, this paper introduces a novel AGV path planning approach for 3D warehouse environments that leverages a hybrid framework combining ACO (Ant Colony Optimization) with deep learning models, called NAHACO (Neural Adaptive Heuristic Ant Colony Optimization). NAHACO integrates three key innovations: first, an innovative heuristic algorithm for 3D warehouse cargo modeling using multidimensional tensors, which addresses the challenge of achieving superior heuristic accuracy; second, integration of a congestion-aware loss function within the ACO framework to adjust path costs based on traffic and capacity constraints, called CARL (Congestion-Aware Reinforce Loss), enabling dynamic heuristic calibration for optimizing ACO-based path planning; and third, an adaptive attention mechanism that captures multi-scale spatial features, thereby addressing dynamic heuristic calibration for further optimization of ACO-based path planning and AGV navigation. NAHACO significantly boosts path planning efficiency, yielding faster computation times and superior performance over both vanilla and state-of-the-art methods, while automatically adapting to warehouse constraints for real-time optimization. NAHACO outperforms state-of-the-art methods, lowering the total cost by up to 24.7% on TSP benchmarks. In warehouse tests, NAHACO cuts cost by up to 41.5% and congestion by up to 56.1% compared to previous methods.