Industrial Internet Robot Collaboration System and Edge Computing Optimization
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908794899922944 |
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| author | Zhao, Haopeng Tao, Dajun Qi, Tian Xu, Jingyuan Zhou, Zijie Liu, Lipeng |
| author_facet | Zhao, Haopeng Tao, Dajun Qi, Tian Xu, Jingyuan Zhou, Zijie Liu, Lipeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Industrial Internet Robot Collaboration System and Edge Computing Optimization Zhao, Haopeng Tao, Dajun Qi, Tian Xu, Jingyuan Zhou, Zijie Liu, Lipeng Robotics Artificial Intelligence 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. |
| title | Industrial Internet Robot Collaboration System and Edge Computing Optimization |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.02492 |