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.

翻译：近期研究表明，基于物理的仿真能显著提升对具有多样化三维形状和严格运动约束的真实世界装配体的拆卸能力。然而，在处理需要大量仿真和较长仿真时间的复杂拆卸任务时，其效率会受到影响。本文提出一种基于状态的拆卸规划方法，该方法优先采用基于物理的平移运动仿真而非旋转运动，以促进自主性，减少对人机交互的依赖，同时存储中间运动状态以提高搜索的可扩展性。我们引入了两种源自新型方向性阻塞图的新型评估函数，这些图通过状态信息得到增强，从而扩展了搜索规模。实验表明，在包含数千个物理有效工业装配体的基准数据集上，采用新型评估函数和方向性阻塞图约束的基于状态拆卸规划方法，在成功率和计算效率方面均优于当前最先进的拆卸规划方法。