  Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths
for a set of agents given individual start and goal states. Given the hardness
of MAMP, most of the research related to multi-agent systems has focused on
multi-agent pathfinding (MAPF), which simplifies the problem by assuming a
shared discrete representation of the space for all agents. The Conflict-Based
Search algorithm (CBS) has proven a tractable means of generating optimal
solutions in discrete spaces. However, neither CBS nor other discrete MAPF
techniques can be directly applied to solve MAMP problems because of the
assumption of the shared discrete representation of the agents' state space. In
this work, we solve MAMP problems by adapting the techniques discovered in the
MAPF scenario by CBS to the more general problem with heterogeneous agents in a
continuous space. We demonstrate the scalability teams of up to 32 agents,
demonstrate the ability to handle up to 8 high DOF manipulators, and plan for
heterogeneous teams. In all scenarios, our approach plans significantly faster
while providing higher quality solutions.
