ePH-MAPF

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ePH-MAPF is an open-source Python framework that implements efficient prioritized heuristics for multi-agent path finding. It offers scalable, collision-free path planning across static grid maps, supports multiple heuristic functions, and integrates easily with robotics and simulation platforms like ROS.
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May 18 2025
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ePH-MAPF

ePH-MAPF

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ePH-MAPF
ePH-MAPF is an open-source Python framework that implements efficient prioritized heuristics for multi-agent path finding. It offers scalable, collision-free path planning across static grid maps, supports multiple heuristic functions, and integrates easily with robotics and simulation platforms like ROS.
Added on:
Social & Email:
Platform:
May 18 2025
--
Featured

What is ePH-MAPF?

ePH-MAPF provides an efficient pipeline for computing collision-free paths for dozens to hundreds of agents on grid-based maps. It uses prioritized heuristics, incremental search techniques, and customizable cost metrics (Manhattan, Euclidean) to balance speed and solution quality. Users can select between different heuristic functions, integrate the library into Python-based robotics systems, and benchmark performance on standard MAPF scenarios. The codebase is modular and well-documented, enabling researchers and developers to extend it for dynamic obstacles or specialized environments.

Who will use ePH-MAPF?

  • Robotics researchers
  • AI developers
  • Game developers
  • Simulation engineers
  • Academic researchers

How to use the ePH-MAPF?

  • Step1: Clone the GitHub repository: git clone https://github.com/ai4co/eph-mapf
  • Step2: Install Python dependencies: pip install -r requirements.txt
  • Step3: Prepare or load a grid-based map and agent start/goal positions
  • Step4: Choose heuristic functions and configure parameters in the config file
  • Step5: Run the planner: python run_mapf.py --config config.yaml
  • Step6: Analyze the output paths and performance logs
  • Step7: Integrate the library into your simulation or ROS nodes

Platform

  • mac
  • windows
  • linux

ePH-MAPF's Core Features & Benefits

The Core Features

  • Efficient prioritized heuristics
  • Multiple heuristic functions
  • Incremental path planning
  • Collision avoidance
  • Scalable to hundreds of agents
  • Modular Python implementation
  • ROS integration examples

The Benefits

  • Fast computation times
  • High scalability
  • Easy integration into projects
  • Customizable heuristics
  • Open-source with documentation

ePH-MAPF's Main Use Cases & Applications

  • Warehouse multi-robot navigation
  • Game AI character path planning
  • Crowd simulation modeling
  • Drone swarm coordination
  • Traffic management for autonomous vehicles

ePH-MAPF's Pros & Cons

The Pros

Improves multi-agent coordination through selective communication enhancements.
Effectively resolves conflicts and deadlocks using prioritized Q value-based decisions.
Combines neural policies with expert single-agent guidance for robust navigation.
Uses an ensemble method to sample the best solutions from multiple solvers, boosting performance.
Open-source code available facilitating reproducibility and further research.

The Cons

No explicit cost or pricing model information is provided.
Limited information on real-world deployment or scalability issues outside simulated environments.

FAQs of ePH-MAPF

ePH-MAPF Company Information

Analytic of ePH-MAPF

Visit Over Time

Monthly Visits
208
Avg Visit Duration
00:00:00
Page Per Visit
1.01
Bounce Rate
50.09%
Aug 2025 - Oct 2025 All Traffic

Geography

Top 1 Regions
Germany
100%
Aug 2025 - Oct 2025 Worldwide Desktop Only

Traffic Sources

Search
42.63%
Direct
36.71%
Referrals
13.74%
Social
3.77%
Paid Referrals
1.81%
Mail
0.22%
Aug 2025 - Oct 2025 Desktop Only

ePH-MAPF Reviews

5/5
Do You Recommend ePH-MAPF? Leave a Comment Below!

ePH-MAPF's Main Competitors and alternatives?

  • Conflict-Based Search (CBS)
  • Enhanced CBS (ECBS)
  • M* algorithm
  • Prioritized Planning
  • LNS-based MAPF frameworks

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