  A redundant manipulator has multiple inverse kinematics solutions per
end-effector pose. Accordingly, there can be many trajectories for joints that
follow a given endeffector path in the Cartesian space. In this paper, we
present a trajectory optimization of a redundant manipulator (TORM) to
synthesize a trajectory that follows a given end-effector path accurately,
while achieving smoothness and collisionfree manipulation. Our method
holistically incorporates three desired properties into the trajectory
optimization process by integrating the Jacobian-based inverse kinematics
solving method and an optimization-based motion planning approach.
Specifically, we optimize a trajectory using two-stage gradient descent to
reduce potential competition between different properties during the update. To
avoid falling into local minima, we iteratively explore different candidate
trajectories with our local update. We compare our method with state-of-the-art
methods in test scenes including external obstacles and two non-obstacle
problems. Our method robustly minimizes the pose error in a progressive manner
while satisfying various desirable properties.
