  Robotic assembly planning enables architects to explicitly account for the
assembly process during the design phase, and enables efficient building
methods that profit from the robots' different capabilities. Previous work has
addressed planning of robot assembly sequences and identifying the feasibility
of architectural designs. This paper extends previous work by enabling planning
with large, heterogeneous teams of robots. We present a planning system which
enables parallelization of complex task and motion planning problems by
iteratively solving smaller subproblems. Combining optimization methods to
solve for manipulation constraints with a sampling-based bi-directional
space-time path planner enables us to plan cooperative multi-robot manipulation
with unknown arrival-times. Thus, our solver allows for completing subproblems
and tasks with differing timescales and synchronizes them effectively. We
demonstrate the approach on multiple case-studies to show the robustness over
long planning horizons and scalability to many objects and agents of our
algorithm. Finally, we also demonstrate the execution of the computed plans on
two robot arms to showcase the feasibility in the real world.
