We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) for both
unweighted and weighted terrains, which aims to minimize the coverage time, defined as the …

We perform a systematic exploration of the principle of Space Utilization Optimization (SUO)
as a heuristic for planning better individual paths in a decoupled multi-robot path planner …

For enabling efficient, large-scale coordination of unmanned aerial vehicles (UAV s) under
the labeled setting, in this work, we develop the first polynomial time algorithm for the …

Optimal Multi-Robot Path Planning (MRPP) has garnered significant attention due to its
many applications in domains including warehouse automation, transportation, and swarm …

Collision-free path planning is indispensable for the multi-robot system. Many existing multi-
robot path planning algorithms may no longer work properly in the narrow-lane environment …

Abstract Multi-Agent Path Finding (MAPF) is NP-hard to solve optimally, even on graphs,
suggesting no polynomial-time algorithms can compute exact optimal solutions for them …

We present a decentralized path-planning algorithm for navigating multiple differential-drive
robots in dense environments. In contrast to prior decentralized methods, we propose a …

To date, multi-robot path planning has primarily been addressed by centralized solvers,
typically aiming to maintain optimality. However, given its NP-hard nature, directly applying …

Multi-Robot Path Planning (MRPP) on graphs, equivalently known as Multi-Agent Path
Finding (MAPF), is a well-established NP-hard problem with critically important applications …

Multi-agent path finding (MAPF) is a challenging problem widely employed in automated
docks and warehouse systems. However, when the above scenarios require car-like agents …