Adaptive Constrained Kinematic Control Using Partial or Complete Task-Space Measurements

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry challenging tasks. Most of the current works assume that robot model is precise enough task at hand. However, increasing demands and safety requirements robotic applications, there a need controller compensates online inaccuracies. We propose adaptive based on quadratic programming, which uses partial or complete task-space measurements to compensate calibration errors. Our method validated experiments show increased accuracy compared state-of-the-art strategy.