# Industrial Internet Robot Collaboration System and Edge Computing Optimization

arXiv: https://arxiv.org/abs/2504.02492 · HTML: https://mlsanity.com/paper/2504.02492/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [WcDT: World-centric Diffusion Transformer for Traffic Scene Generation](https://mlsanity.com/paper/2404.02082/) (2024)
- [Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion](https://mlsanity.com/paper/2501.04606/) (2025)
- [CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models](https://mlsanity.com/paper/2404.01663/) (2024)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models](https://mlsanity.com/paper/2405.04135/) (2024)
- [NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training](https://mlsanity.com/paper/2409.09582/) (2024)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Robustness of Large Language Models Against Adversarial Attacks](https://mlsanity.com/paper/2412.17011/) (2024)
- [CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference](https://mlsanity.com/paper/2501.15852/) (2025)
- [AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility](https://mlsanity.com/paper/2501.00113/) (2024)
- [DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning](https://mlsanity.com/paper/2407.03332/) (2024)
- [Deep Semantic Graph Learning via LLM based Node Enhancement](https://mlsanity.com/paper/2502.07982/) (2025)
- [Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation](https://mlsanity.com/paper/2501.02414/) (2025)
- [Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning](https://mlsanity.com/paper/2503.07340/) (2025)
- [Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT](https://mlsanity.com/paper/2502.05694/) (2025)
- [An Improved Dung Beetle Optimizer for Random Forest Optimization](https://mlsanity.com/paper/2411.17738/) (2024)

## Built on by (most cited first, 3 of 3)

- [Automated Parking Trajectory Generation Using Deep Reinforcement Learning](https://mlsanity.com/paper/2504.21071/) (2025)
- [Application of YOLOv8 in monocular downward multiple Car Target detection](https://mlsanity.com/paper/2505.10016/) (2025)
- [ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction](https://mlsanity.com/paper/2505.10027/) (2025)

## Abstract (arXiv)

In industrial Internet environments, mobile robots must generate collision-free global routes under stochastic obstacle layouts and random perturbations in commanded linear and angular velocities. This paper models a differential-drive robot with nonholonomic constraints, then decomposes motion into obstacle avoidance, target turning, and target approaching behaviors to parameterize the control variables. Global path planning is formulated as a constrained optimization problem and converted into a weighted energy function that balances path length and collision penalties. A three-layer neural network represents the planning model, while simulated annealing searches for near-global minima and mitigates local traps. During execution, a fuzzy controller uses heading and lateral-offset errors to output wheel-speed differentials for rapid correction; edge-side computation is discussed to reduce robot-server traffic and latency. Matlab 2024 simulations report deviation within +-5 cm, convergence within 10 ms, and shorter paths than two baseline methods. The approach improves robustness of global navigation in practice.