  The aim of coordinated planning is to avoid robot-to-robot collisions in a
multi-robot system, and there are two standard solution approaches: centralized
planning and decoupled planning. Our first contribution is a decoupled planning
approach that ensures C2-continuous control commands with zero velocities at
the start and goal. We benchmark our decoupled approach with a centralized
approach. Contrary to literature, we show that for a standard motion planning
pipeline, such as the one used by MoveIt!, centralized planning is superior to
decoupled planning in dual-arm manipulation: It has a lower computation time
and a higher robustness. Our second contribution is an optimization that
minimizes the rotational motion of an end-effector while considering obstacle
avoidance. We derive the analytic gradients of this optimization problem,
making the algorithm suitable for online motion planning. Our optimization
extends an existing path quality improvement method. Integrating it into our
decoupled approach overcomes its shortcomings and provides a motion planning
pipeline that is robust at up to 99.9% with a planning time of less than 1s and
that computes high-quality paths.
