解决工业场景下机器人动态避障与高效路径规划难题
Industrial Internet Robot Collaboration System and Edge Computing Optimization
- 分行为控制变量，构建能量函数优化路径
- 仿真显示偏差≤5cm，收敛时间<10ms，路径更短
- 适合需要低延迟的工业机器人协同系统
在工业互联网环境中，移动机器人需在随机障碍布局和速度扰动下生成无碰撞全局路径。本文建模了具有非完整约束的差速驱动机器人，将运动分解为避障、转向目标和接近目标三类行为以参数化控制变量。全局路径规划被表述为带约束的优化问题，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，结合模拟退火算法搜索近似全局最优解，有效避免局部极小。执行阶段使用模糊控制器，基于航向与侧向误差输出轮速差实现快速修正；同时讨论边缘计算以降低机器人-服务器通信量与延迟。Matlab 2024仿真结果显示路径偏差在±5 cm内，收敛时间小于10 ms，路径长度优于两种基线方法。该方法显著提升了实际应用中全局导航的鲁棒性。
原文摘要 · Abstract (English)
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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