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.

翻译：本文提出了一种通过集成模糊逻辑安全系统与强化学习算法，实现UR10机械臂路径规划中奇异性检测与规避的综合方法。所提出的系统解决了机器人操控中的关键挑战——奇异性可能导致失控及潜在的设备损坏。我们的混合方法将基于可操作性度量、条件数分析的实时奇异性检测与模糊逻辑决策相结合，并采用稳定的强化学习框架进行自适应路径规划。实验结果表明，该系统在保持与奇异构型安全距离的同时，达到目标位置的成功率为90%。该系统集成了PyBullet仿真用于训练数据收集，并通过URSim连接实现实际部署。