  Finding asymptotically-optimal paths in multi-robot motion planning problems
could be achieved, in principle, using sampling-based planners in the composite
configuration space of all of the robots in the space. The dimensionality of
this space increases with the number of robots, rendering this approach
impractical. This work focuses on a scalable sampling-based planner for coupled
multi-robot problems that provides asymptotic optimality. It extends the dRRT
approach, which proposed building roadmaps for each robot and searching an
implicit roadmap in the composite configuration space. This work presents a new
method, dRRT* , and develops theory for scalable convergence to optimal paths
in multi-robot problems. Simulated experiments indicate dRRT* converges to
high-quality paths while scaling to higher numbers of robots where the naive
approach fails. Furthermore, dRRT* is applicable to high-dimensional problems,
such as planning for robot manipulators
