Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.
翻译:议会规划是现代工业制造产品组装、维修和再循环的核心。尽管其重要性和历史悠久的研究,但机械组装计划在最终组装状态下仍是一个棘手的问题。这是因为处理任意的三维形状的复杂性和现实世界组装所需的高度受限运动。在这项工作中,我们提出了一种新方法,以高效地规划现实世界组装的有形合理的组装运动和序列。我们的方法利用组装原则和物理模拟来有效探索一个缩小的搜索空间。为了评估我们方法的普遍性,我们定义了由数千个具有物理效力的工业组装和所需的各种组装动作组成的大型数据集。我们在这一新基准上的实验表明,我们取得了最先进的成功率,并且与其他基线算法相比,计算效率最高。我们的方法还把轮换组(例如螺丝和谜团)和80个部分组装配在几分钟内解决。