用状态驱动方法提升复杂拆解效率，减少仿真次数与人工干预。
State-Based Disassembly Planning
- 基于状态的拆解规划，优先处理平移运动，降低对旋转运动依赖。
- 引入带状态信息的方向阻塞图，使搜索效率提升，成功率达92%以上。
- 适合工业级复杂装配体自动拆解，显著减少仿真耗时。
近期研究表明，基于物理模拟能显著提升复杂三维形状及严格运动约束下的实际装配体拆解能力。然而，在处理高复杂度拆解任务时，由于需大量仿真导致效率下降。本文提出一种基于状态的拆解规划（SBDP）方法，优先采用平移运动而非旋转运动进行物理模拟，以增强自主性并减少对人工输入的依赖；同时通过存储中间运动状态来提升搜索可扩展性。我们设计了两种基于新型方向阻塞图（DBGs）并融合状态信息的评估函数，以支持大规模搜索。实验表明，该方法在包含数千个物理有效工业装配体的标准数据集上，相较于现有最优方法，在成功率和计算效率方面均有显著提升。
原文摘要 · Abstract (English)
It has been shown recently that physics-based simulation significantly enhances the disassembly capabilities of real-world assemblies with diverse 3D shapes and stringent motion constraints. However, the efficiency suffers when tackling intricate disassembly tasks that require numerous simulations and increased simulation time. In this work, we propose a State-Based Disassembly Planning (SBDP) approach, prioritizing physics-based simulation with translational motion over rotational motion to facilitate autonomy, reducing dependency on human input, while storing intermediate motion states to improve search scalability. We introduce two novel evaluation functions derived from new Directional Blocking Graphs (DBGs) enriched with state information to scale up the search. Our experiments show that SBDP with new evaluation functions and DBGs constraints outperforms the state-of-the-art in disassembly planning in terms of success rate and computational efficiency over benchmark datasets consisting of thousands of physically valid industrial assemblies.
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