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cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

Quick Heuristic Validation of Edges in Dynamic Roadmap Graphs

Yulie Arad, Stav Ashur, Nancy M. Amato

In this paper we tackle the problem of adjusting roadmap graphs for robot motion planning to non-static environments. We introduce the "Red-Green-Gray" paradigm, a modification of…

cs.RO2025

An Analysis of Constraint-Based Multi-Agent Pathfinding Algorithms

Hannah Lee, James D. Motes, Marco Morales +1

This study informs the design of future multi-agent pathfinding (MAPF) and multi-robot motion planning (MRMP) algorithms by guiding choices based on constraint classification for c…

cs.RO2025

ERUPT: An Open Toolkit for Interfacing with Robot Motion Planners in Extended Reality

Isaac Ngui, Courtney McBeth, André Santos +6

We propose the Extended Reality Universal Planning Toolkit (ERUPT), an extended reality (XR) system for interactive motion planning. Our system allows users to create and dynamical…

cs.RO2025

Edge Nearest Neighbor in Sampling-Based Motion Planning

Stav Ashur, Nancy M. Amato, Sariel Har-Peled

Neighborhood finders and nearest neighbor queries are fundamental parts of sampling based motion planning algorithms. Using different distance metrics or otherwise changing the def…