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.

翻译：在工业互联网环境中，移动机器人必须在随机障碍物布局以及指令线速度和角速度随机扰动的条件下生成无碰撞全局路径。本文对具有非完整约束的差速驱动机器人进行建模，将运动分解为避障、目标转向和目标趋近行为以参数化控制变量。全局路径规划被构建为约束优化问题，并转化为平衡路径长度与碰撞惩罚的加权能量函数。采用三层神经网络表示规划模型，同时利用模拟退火算法搜索近全局最小值并规避局部极值陷阱。在执行阶段，模糊控制器利用航向误差与横向偏移误差输出轮速差以实现快速校正；文中讨论了边缘侧计算以减少机器人与服务器间的通信流量与延迟。基于Matlab 2024的仿真结果显示：路径偏差保持在±5厘米内，收敛时间小于10毫秒，且所得路径较两种基准方法更短。该方法在实践中提升了全局导航的鲁棒性。