  We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel
algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts
Connected Fermat Spiral (CFS) from the computer graphics community to
multi-robot coordination for the first time. MCFS uniquely enables the
orchestration of multiple robots to generate coverage paths that contour around
arbitrarily shaped obstacles, a feature that is notably lacking in traditional
methods. Our framework not only enhances area coverage and optimizes task
performance, particularly in terms of makespan, for workspaces rich in
irregular obstacles but also addresses the challenges of path continuity and
curvature critical for non-holonomic robots by generating smooth paths without
decomposing the workspace. MCFS solves MCPP by constructing a graph of isolines
and transforming MCPP into a combinatorial optimization problem, aiming to
minimize the makespan while covering all vertices. Our contributions include
developing a unified CFS version for scalable and adaptable MCPP, extending it
to MCPP with novel optimization techniques for cost reduction and path
continuity and smoothness, and demonstrating through extensive experiments that
MCFS outperforms existing MCPP methods in makespan, path curvature, coverage
ratio, and overlapping ratio. Our research marks a significant step in MCPP,
showcasing the fusion of computer graphics and automated planning principles to
advance the capabilities of multi-robot systems in complex environments. Our
code is available at https://github.com/reso1/MCFS.
