  This paper explores general multi-robot task and motion planning, where
multiple robots in close proximity manipulate objects while satisfying
constraints and a given goal. In particular, we formulate the plan refinement
problem--which, given a task plan, finds valid assignments of variables
corresponding to solution trajectories--as a hybrid constraint satisfaction
problem. The proposed algorithm follows several design principles that yield
the following features: (1) efficient solution finding due to sequential
heuristics and implicit time and roadmap representations, and (2) maximized
feasible solution space obtained by introducing minimally necessary
coordination-induced constraints and not relying on prevalent simplifications
that exist in the literature. The evaluation results demonstrate the planning
efficiency of the proposed algorithm, outperforming the synchronous approach in
terms of makespan.
