Millimeter-Level Pick and Peg-in-Hole Task Achieved by Aerial Manipulator

Achieving accurate control performance of the end-effector is critical for practical applications of aerial manipulator. However, due to the presence of floating-base disturbance from the unmanned aerial vehicle (UAV) platform and the kinematic error amplification effect from multilink structure of the manipulator, it is extremely challenging to ensure the high-precision performance of aerial manipulator. Building upon the philosophy of disturbance rejection, we propose a predictive optimization scheme that allows aerial manipulator to successfully execute millimeter-level flying pick and peg-in-hole task. First, the error amplification effect of the floating base is quantitatively analyzed by virtue of the aerial manipulator kinematics. Intuitively, it is found that if the further motion of the UAV platform is well predicted, the manipulator can directly counteract the floating disturbance by following a modified reference trajectory. Hence, a learning-based prediction approach is leveraged to rapidly forecast the UAV platform motion online. Subsequently, an optimization controller is formulated to follow the reference trajectory by incorporating multiple practical constraints of aerial manipulator. Flight tests demonstrate that this study goes a step further to achieve higher accuracy of the end-effector than the existing results (centimeter-level).