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.

The Java Action Linearprogram module provides a specialized action for the LightJason framework that allows agents to model and solve linear optimization tasks. Users can configure objective coefficients, add equality and inequality constraints, select solution methods, and run the solver within an agent’s reasoning cycle. Once executed, the action returns the optimal variable values and objective score which agents can use for subsequent planning or execution. This plug-and-play component abstracts solver complexity while maintaining full control over problem definitions through Java interfaces.

Multi-Agent-AI-Models-and-Path-Planning provides a comprehensive toolkit for developing and testing multi-agent systems combined with classical and modern path planning methods. It includes implementations of algorithms such as A*, Dijkstra, RRT, and potential fields, alongside customizable agent behavior models. The framework features simulation and visualization modules, allowing seamless scenario creation, real-time monitoring, and performance analysis. Designed for extensibility, users can plug in new planning algorithms or agent decision models to evaluate cooperative navigation and task allocation in complex environments.