Industrial Internet Robot Collaboration System and Edge Computing Optimization
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 3 Apr 2025.
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18 of 30 references.
References · 18
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- 2025Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learningcited by 6
- 2025Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPTcited by 5
- 2024An Improved Dung Beetle Optimizer for Random Forest Optimizationcited by 2
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Citing papers per year: 2025: 2.
2 papers.