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.
AI-Short-Video-Engine orchestrates multiple AI modules in an end-to-end pipeline to transform user-defined text prompts into polished short videos. First, the system leverages large language models to generate a storyboard and script. Next, Stable Diffusion creates scene artwork, while bark provides realistic voice narration. The engine assembles images, text overlays, and audio into a cohesive video, adding transitions and background music automatically. Its plugin-based architecture allows customization of each stage: from swapping in alternative text-to-image or TTS models to adjusting video resolution and style templates. Deployed via Docker or native Python, it offers both CLI commands and RESTful API endpoints, enabling developers to integrate AI-driven video production into existing workflows seamlessly.