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

Meng Wang

Abstract:

Achieving accurate control performance of the endeffector
is critical for practical applications of aerial manipulator.
However, due to the presence of floating-base disturbance fromthe
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).