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.
ePH-MAPF Core Features
Efficient prioritized heuristics
Multiple heuristic functions
Incremental path planning
Collision avoidance
Scalable to hundreds of agents
Modular Python implementation
ROS integration examples
ePH-MAPF Pro & Cons
The Cons
No explicit cost or pricing model information is provided.
Limited information on real-world deployment or scalability issues outside simulated environments.
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.
