Joint Constraint

Joint constraint research focuses on accurately modeling and managing the limitations on the movement of joints in various systems, from robotic manipulators to human limbs. Current research employs diverse approaches, including data-driven methods like support vector machines to learn realistic joint boundaries from motion capture data, and model-based techniques incorporating kinematic constraints within Gaussian process regression or iterative convex hull methods for improved prediction and control. This work is crucial for enhancing the safety, efficiency, and performance of robotic systems and for advancing biomechanical modeling and analysis of human movement, particularly in applications like human-robot collaboration and rehabilitation.