  Although inverse kinematics of serial manipulators is a well studied problem,
challenges still exist in finding smooth feasible solutions that are also
collision aware. Furthermore, with collaborative service robots gaining
traction, different robotic systems have to work in close proximity. This means
that the current inverse kinematics approaches do not have only to avoid
collisions with themselves but also collisions with other robot arms.
Therefore, we present a novel approach to compute inverse kinematics for serial
manipulators that take into account different constraints while trying to reach
a desired end-effector pose that avoids collisions with themselves and other
arms. Unlike other constraint based approaches, we neither perform expensive
inverse Jacobian computations nor do we require arms with redundant degrees of
freedom. Instead, we formulate different constraints as weighted cost functions
to be optimized by a non-linear optimization solver. Our approach is superior
to the state-of-the-art CollisionIK in terms of collision avoidance in the
presence of multiple arms in confined spaces with no collisions occurring in
all the experimental scenarios. When the probability of collision is low, our
approach shows better performance at trajectory tracking as well. Additionally,
our approach is capable of simultaneous yet decentralized control of multiple
arms for trajectory tracking in intersecting workspace without any collisions.
