Graph-Based Planning

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10 papers in the last four weeks, up 233% on the four weeks before. 0.1% of all new papers.

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Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries
Mar 16, 2026Manshi Limbu, Xuan Wang, Gregory J. Stein +2Multi-Robot Motion Planning Multi-Robot Coordination
George Mason University
ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response
Feb 3, 2026Xiaomeng Zhu, Fengming Zhu, Weijie Zhou +8AI Agent Benchmarks Proactive Assistance
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR, China · Tencent, Shenzhen, China · Futian Laboratory, Shenzhen, China +1
STITCHER: Constrained Trajectory Planning in Complex Environments with Real-Time Motion Primitive Search
Oct 16, 2025Helene J. Levy, Brett T. LopezConstrained Motion Planning Trajectory Generation
VECTR Laboratory, University of California, Los Angeles, Los Angeles, CA, USA

Mar 16, 2026, cs.MAJ/K move · Enter open · S save

Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries

Manshi Limbu, Xuan Wang, Gregory J. Stein, Daigo Shishika+1

George Mason University

Multi-Robot Motion Planning Multi-Robot Coordination Graph-Based Planning Safe Motion Planning

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Cooperative multi-robot missions require team of robots to traverse environments where adversaries or hazards with stochastic dynamics induce time-varying traversal risk. While support coordination--where robots assist teammates in traversing risky regions--can significantly reduce mission costs, its effectiveness depends on the team's ability to anticipate future risk. We formulate support-based multi-robot graph traversal problem with stochastically moving adversaries, where future risky regions become uncertain as adversaries move through the environment. When adversaries remain stationary, our formulation reduces to the static risky-edge setting. To address the stochastic case, we model individual adversaries as first-order Markov stay-move processes over graph edges and propagate their occupancy distributions over a finite planning horizon to obtain time-indexed edge-risk forecasts. These forecasts inform the support candidate selection and joint robot path planning. Experimental results show that forecast-informed support decisions consistently lower expected team cost relative to evaluated baselines in stochastic motion settings.

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Latest papers 63

Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries

ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response

STITCHER: Constrained Trajectory Planning in Complex Environments with Real-Time Motion Primitive Search