ePH-MAPF
0
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.
AI Platforms & Frameworks
Avatars & Personas
Development Environment
Other
#pathfinding
#collision avoidance
#multi-agent path planning
#robotics
#Heuristic algorithms
#dynamic obstacles
#Python integration
#ROS robotics
#efficiency optimization
#planning algorithms
#modular code
#open-source software
#Performance Benchmarking
#grid-based navigation
#custom heuristics
#agent coordination
#solution quality
#incremental search
#scalability
...
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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.
AI Platforms & Frameworks
Avatars & Personas
Development Environment
Other
#pathfinding
#collision avoidance
#multi-agent path planning
#robotics
#Heuristic algorithms
#dynamic obstacles
#Python integration
#ROS robotics
#efficiency optimization
#planning algorithms
#modular code
#open-source software
#Performance Benchmarking
#grid-based navigation
#custom heuristics
#agent coordination
#solution quality
#incremental search
#scalability
...
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
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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