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New Goal-Branching LaCAM solver improves multi-agent pathfinding

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Goal-Branching LaCAM, an enhancement to the LaCAM multi-agent pathfinding solver. This new method addresses challenges in dense, corridor-heavy environments by assigning temporary goals to blocking agents, which are then explored as additional search branches. The approach preserves completeness and significantly improves the success rate and speed of finding solutions on dense instances, with only a modest increase in solution cost. AI

IMPACT Improves efficiency for complex pathfinding problems, potentially impacting robotics and logistics.

RANK_REASON Academic paper detailing a new algorithm for multi-agent pathfinding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Goal-Branching LaCAM solver improves multi-agent pathfinding

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Academic paper detailing a new algorithm for multi-agent pathfinding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Stephen Chang, Daniel Harabor ·

    Goal-Branching LaCAM: Temporary Goals as Search Branches in Dense Multi-Agent Pathfinding

    arXiv:2611.07528v1 Announce Type: new Abstract: LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate successors and lazily adding single-agent constraints to explore alternatives. Althou…

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