RESEARCH ON MOTOR LEARNING AND CONTROL OF MULTI-DOF BIONIC MANIPULATOR, 87-93.
Jianjun Lan
Keywords
Action learning, inertial measurement unit, Kalman filter, servo control, attitude mapping
Abstract
Motion planning of robotic systems needs to be engineered by
professionals, and how to quickly and simply adjust the motion of
the manipulator when external tasks change is of major importance
to us. We present an action planning method by only using magnetic
and inertial measurement unit (MIMU), the entire system consists of
a human arm attitude measurement unit and a bionic manipulator
control unit. Robotic manipulators can perform fast action learning
from the pose data of the operator’s arm instead of complex motion
redesigns. The Kalman filter algorithm is used in inertial sensor data
fusion, and the fusion of the values recorded from the inertial sensors
can be decomposed into the rotation angles of the servos using a
rigid body transformation using the Lie group theory. Evaluation
tests were performed separately in the LabVIEW platform and on
a real robotic system, and the results from the real-time tests show
that the method successfully reproduces the movements performed
by the operator.
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