Session 4 Multi Agent Path Finding Information Guide

  1. Background of Session 4 Multi Agent Path Finding
  2. Important Facts
  3. Recent Updates
  4. Deep Dive
  5. Future Outlook

Background of Session 4 Multi Agent Path Finding

Full Session 4: Multi-Agent Path Finding Guide
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Important Facts

Full Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding Guide
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Recent Updates

Full Upgrading Multi-Agent Pathfinding for the Real World Guide
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Multi-Agent Path Finding (MAPF)
Multi-Agent Path Finding (MAPF)
RCT real time multi-agent path finding and collision avoidance algorithm.
RCT real time multi-agent path finding and collision avoidance algorithm.
CBS Multi-Agent Pathfinding Explained Simply | AI Algorithm Guide
CBS Multi-Agent Pathfinding Explained Simply | AI Algorithm Guide
Distributed Multi-agent Navigation Based on ORCA and MAPF solving
Distributed Multi-agent Navigation Based on ORCA and MAPF solving
The Centralised method for solving Multi-Agent Pathfinding problems
The Centralised method for solving Multi-Agent Pathfinding problems
IROS-21-talk: Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions
IROS-21-talk: Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions
X*: Anytime Multi-Agent Path Finding for Sparse Domains using Window-Based Iterative Repairs - Full
X*: Anytime Multi-Agent Path Finding for Sparse Domains using Window-Based Iterative Repairs - Full
Multi-Agent Path Finding (MAPF) - Final Presentation
Multi-Agent Path Finding (MAPF) - Final Presentation
Conflict-Based Search for Explainable Multi-Agent Path Finding
Conflict-Based Search for Explainable Multi-Agent Path Finding
Real-Time Multi-Agent Pathfinding on Unreal Engine 4
Real-Time Multi-Agent Pathfinding on Unreal Engine 4
Efficient Deep Learning for Multi Agent Path Finding
Efficient Deep Learning for Multi Agent Path Finding

Deep Dive

Data is compiled from public records and verified media reports.

Last Updated: October 19, 2026

Future Outlook

Full ICAPS 2020: Zhang et al. on Multi-Agent Path Finding with Mutex ... Update
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Disclaimer: Disclaimer: All information is compiled from publicly available data, media reports, and analysis. Actual details may vary.

Summary

Keisuke Okumura, Manao Machida, Xavier Défago & Yasumasa Tamura. "Priority Inheritance with Backtracking This talk aims to invite you to the forefront of MAPF research directly This is a re-recording of my invited talk at EurMAPF-25, ... ICAPS 2020 talk on the paper Han Zhang, Jiaoyang Li, Pavel Surynek, Sven Koenig, T. K. Satish Kumar. Two teams of 5 robots playing in RoboCup MSL league are simulated, each player has to move to a different place every Centralised A* (CA*) is an incredibly simple and intuitive approach to solving MAPF problems using a global centralized controller ... Paper: ieeexplore.ieee.org/document/9636683 Arxiv: arxiv.org/pdf/2103.04516.pdf Google Scholar: ... We present background and detailed overview of the Windowed Anytime Final Project Presentation RBE550: Motion Planning J. Kottinger, S. Almagor, and M. Lahijanian, “Conflict-Based Search This video is a presentation of a MAPF plugin available on GitHub: github.com/MShepelin/RealTimeMAPF. Video by Natalie R Abreu (University of Southern California) AAAI-22 Undergraduate Consortium Efficient Deep Learning

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