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arXiv · 2504.02492

Industrial Internet Robot Collaboration System and Edge Computing Optimization

Haopeng Zhao·Dajun Tao·Tian Qi·Jingyuan Xu·Zijie Zhou·Lipeng Liu
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Abstract

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.

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Haopeng Zhao, Dajun Tao, Tian Qi, Jingyuan Xu, Zijie Zhou, Lipeng Liu. 2026-01-29. Industrial Internet Robot Collaboration System and Edge Computing Optimization. https://arxiv.org/abs/2504.02492

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