10 papers
P-ARC: Exploiting Subproblem Independence for Parallel Multi-Robot Motion Planning
James D. Motes, Marco Morales, Nancy M. Amato
This paper presents Parallel ARC (P-ARC), a parallel formulation of the Adaptive Robot Coordination (ARC) approach to multi-robot motion planning (MRMP) which exploits subproblem i…
AO-ARC: Almost-Surely Asymptotically Optimal Multi-Robot Motion Planning with ARC
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…
Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement
Isaac Ngui, Courtney McBeth, James D. Motes +2
A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of sear…
Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning
James D. Motes, Marco Morales, Nancy M. Amato
In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning. We develop two vector-accelerated primitives, multi-robot Mo…
Serialized Red-Green-Gray: Quicker Heuristic Validation of Edges in Dynamic Roadmap Graphs
Yulie Arad, Stav Ashur, Marta Markowicz +3
Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in s…
Lazy-DaSH: Lazy Approach for Hypergraph-based Multi-robot Task and Motion Planning
Seongwon Lee, James Motes, Isaac Ngui +2
We introduce Lazy-DaSH, an improvement over the recent state of the art multi-robot task and motion planning method DaSH, which scales to more than double the number of robots and…