用模糊逻辑与强化学习避免机械臂奇异点，提升路径规划安全性。
Intelligent Singularity Avoidance in UR10 Robotic Arm Path Planning Using Hybrid Fuzzy Logic and Reinforcement Learning
- 融合模糊逻辑与强化学习实现实时奇异点检测与避障
- 90%成功率达成目标位置，且远离危险奇异配置
- 支持仿真训练与真实机械臂部署，适合工业机器人应用
本文提出一种综合方法，通过融合模糊逻辑安全系统与强化学习算法，实现对UR10机械臂路径规划中奇异点的检测与规避。该系统针对奇异点导致失控或设备损坏的问题，结合利用可操作性度量、条件数分析和模糊逻辑决策进行实时奇异点检测，并采用稳定的强化学习框架实现自适应路径规划。实验结果表明，在保持与奇异构型安全距离的前提下，目标位置到达成功率达90%。系统使用PyBullet仿真环境收集训练数据，并通过URSim实现与真实机械臂的连接部署。
原文摘要 · Abstract (English)
This paper presents a comprehensive approach to singularity detection and avoidance in UR10 robotic arm path planning through the integration of fuzzy logic safety systems and reinforcement learning algorithms. The proposed system addresses critical challenges in robotic manipulation where singularities can cause loss of control and potential equipment damage. Our hybrid approach combines real-time singularity detection using manipulability measures, condition number analysis, and fuzzy logic decision-making with a stable reinforcement learning framework for adaptive path planning. Experimental results demonstrate a 90% success rate in reaching target positions while maintaining safe distances from singular configurations. The system integrates PyBullet simulation for training data collection and URSim connectivity for real-world deployment.
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