STRIPS-based Multi-Drone Delivery in a Horn-Shaped Map

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Tianyu Qu  •  Yiming Li  •  Jing Yang
abstract

This project presents an intelligent drone delivery path planning and simulation system based on the STRIPS (Stanford Research Institute Problem Solver) algorithm implemented through Kotlin-Prolog hybrid programming. The system addresses the growing demand for autonomous logistics solutions by providing comprehensive planning capabilities for multi-drone delivery scenarios within complex network topologies. Key innovations include a Horn-shaped map network modeling realistic urban delivery environments, energy-aware planning algorithms that optimize battery consumption and recharging strategies, multi-package delivery coordination supporting simultaneous operations, and a sophisticated JavaFX graphical user interface enabling real-time visualization and interactive simulation control. 

The implementation successfully demonstrates autonomous path planning capabilities across diverse operational scenarios including simple drone movements, single and multi-package deliveries, emergency low-energy situations, and complex multi-drone coordination tasks. The system achieves full automation of the planning process through STRIPS-based reasoning while providing comprehensive tools for scenario configuration, plan visualization, execution monitoring, and performance analysis. Validation through extensive testing confirms the system’s ability to handle complex delivery networks involving multiple drones, packages, and operational constraints including energy limitations, payload capacities, and network connectivity restrictions. 

The project delivers significant value for both academic research in AI planning algorithms and practical applications in logistics automation. The modular architecture supports extensibility for advanced algorithms while the intuitive interface facilitates educational demonstrations and research experimentation. Results demonstrate successful integration of classical AI planning theory with modern software engineering practices, creating a robust foundation for autonomous delivery system development.

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