Marco Morales

James D. Motes, Marco Morales, Nancy M. Amato

This paper presents Parallel ARC (P-ARC), a parallel variant of the Adaptive Robot Coordination (ARC) approach to multi-robot motion planning (MRMP). P-ARC proposes a parallel variant for each of the three main stages in ARC: initial individual solutions, conflict detection, and conflict resolution, exploiting the independence created by ARC's decomposition of the MRMP problem. Additionally, we employ an OR-parallel multi-start strategy to both ARC and P-ARC, creating a hybrid parallel strategy OR-P-ARC. We evaluate the impact of the different parallel strategies for ARC using a set of scaling 2D mobile and planar manipulator scenarios with up to 128 robots to control for conflicts and work distribution across the stages of ARC. Additionally, we demonstrate planning time speedups approaching 4X over the sequential version for large Panda multi-manipulator teams in real-world inspired scenarios when deploying 16 CPU cores.

James D. Motes, Marco Morales, Nancy M. Amato

We present AO-ARC, an anytime multi-robot motion planning (MRMP) method that achieves initial solution times on par with state-of-the-art MRMP feasibility solvers while converging faster and more reliably than existing anytime MRMP methods as the number of robots increases. AO-ARC adapts the AO-x meta-algorithm for converting feasibility solvers into anytime algorithms by iteratively calling the original ARC method on bounded MRMP instances under a makespan cost metric. This exploits the adaptive (de)coupling of ARC while maintaining the consistent cost bound across robot (de)compositions needed for AO-x. We provide theoretical analysis proving the asymptotic optimality properties of AO- ARC and conduct empirical evaluation on a set of 2D scenarios with different levels of coordination complexity and a 3D manipulator scenario representative of real-world applications.