  This paper addresses a variant of multi-agent path finding (MAPF) in
continuous space and time. We present a new solving approach based on
satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The
standard MAPF is a task of navigating agents in an undirected graph from given
starting vertices to given goal vertices so that agents do not collide with
each other in vertices of the graph. In the continuous version
(MAPF$^\mathcal{R}$) agents move in an $n$-dimensional Euclidean space along
straight lines that interconnect predefined positions. For simplicity, we work
with circular omni-directional agents having constant velocities in the 2D
plane. As agents can have different sizes and move smoothly along lines, a
non-colliding movement along certain lines with small agents can result in a
collision if the same movement is performed with larger agents. Our SMT-based
approach for MAPF$^\mathcal{R}$ called SMT-CBS$^\mathcal{R}$ reformulates the
Conflict-based Search (CBS) algorithm in terms of SMT concepts. We suggest lazy
generation of decision variables and constraints. Each time a new conflict is
discovered, the underlying encoding is extended with new variables and
constraints to eliminate the conflict. We compared SMT-CBS$^\mathcal{R}$ and
adaptations of CBS for the continuous variant of MAPF experimentally.
