Korea Advanced Institute of Science and Technology (KAIST)
Abstract
 
A redundant manipulator can have many trajectories
for joints that follow a given end-effector path in
the Cartesian space, since it has multiple inverse kinematics
solutions per end-effector pose. While maintaining accuracy
with the given end-effector path, it is challenging to
quickly synthesize a feasible trajectory that satisfies robot-specific
constraints and is collision-free against obstacles,
especially when the given end-effector path passes around
obstacles. 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 collision-free 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 also accelerate our optimizer by adaptively
determining the stop of the current exploration based on the
observation of optimization results.We compare our method
with five prior methods in test scenes, including external
obstacles and two non-obstacle problems. Furthermore, we
analyze our optimizer performance by experimenting with
three different configurations of robots. Our method robustly
minimizes the pose error in a progressive manner while satisfying
various desirable properties.