Robotic Arm Motion Planning Avoiding Singularity
Developed motion planning algorithms for robotic arms to avoid singularity issues, enhancing stability and precision in complex tasks. Implemented solutions leveraging advanced kinematics and real-time obstacle detection.
Mentor
Hsiu-Mei Chou
Division of Virtual-Real Integration
National Center for High-Performance Computing
Team
Sheng-Kai Chen
Project Developer & Paper Author
Abstract
This research 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.