１．算法与数据结构库；２．已经实现动态数组／双向链表／栈／队列／堆／链式哈希表／二叉搜索树／红黑树．均以C++模板类实现，可用于实际项目中替代STL的容器类及数据结构学习．３．已经实现图／深度优先／广度优先／单源最短路径／所有结点对最短路径／最小生成树／快速排序／归并排序／字符串匹配算法．可用于实际项目及算法学习．４．对分治／迭代算法设计方法进行总结，对动态规划／贪心算法针对几个经典问题做了具体实现．

武器目标分配问题——动态规划算法

The OpenMORE project offers the essential tools for rapid robot path replanning during trajectory execution. It provides a robust architecture that manages both the replanning process and the execution of the trajectory. It also includes a library of sampling-based path replanning algorithms to efficiently handle dynamic changes in the environment.

General-purpose, multi-agent framework for automating tasks using AI agents composed in teams for conversation-based collaboration.

Path planning using self-adjusting roadmaps for unknown environments

This project simulates a dynamic patrolling system in an unknown 2D continuous space

Model-agnostic multi-agent orchestration built on CrewAI. YAML-driven agent and MCP catalogs, dynamic LLM planning, sessions, and pluggable execution — mix Ollama, OpenAI, Anthropic, HuggingFace, and your own tools without rewriting orchestration logic.

An open-source framework for task-based agent swarms with dynamic parallelization, routing, and execution topology.