面向序列约束机器人装配的反向布局搜索
Backward Layout Search for Sequence-Constrained Robotic Assembly
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中文总结 AI 辅助
针对序列约束机器人装配布局规划的挑战，提出反向布局搜索（BLS）算法，通过反向分配零件初始位姿并结合多维度检查与波束选择，在5个装配模型上实现了更优的无碰撞布局规划，减少了评估次数与搜索时间。
中文摘要 AI 辅助
机器人装配布局规划必须确定装配工位以及每个零件的初始位姿，同时确保规定装配序列的无碰撞执行。该问题极具挑战性，因为每次装配步骤后障碍物环境都会发生变化，工作空间中剩余的未装配零件可能会阻碍机器人的运动。我们发现，每次装配步骤的可行性仅取决于当前及后续装配零件的初始位姿。基于这一依赖关系，我们提出反向布局搜索（Backward Layout Search, BLS）算法，按装配的反向顺序分配零件初始位姿。每次扩展操作会执行几何、运动学、抓取及规定运动检查，同时碰撞掩码和候选集过滤会排除不可行的零件初始位姿候选。通过波束选择保留有前景的部分布局，完整布局则通过正向装配顺序下的完整运动规划进行验证。对5个装配模型的实验表明，与匹配的正向搜索相比，BLS能生成无碰撞且可执行的布局，并减少了步骤评估次数和搜索时间。
英文摘要
Robotic assembly layout planning must determine the assembly site and the initial pose of each part while ensuring collision-free execution of a prescribed assembly sequence. This problem is challenging because the obstacle environment changes after each assembly step, and unassembled parts re maining in the workspace may block robot motions. We observe that the feasibility of each assembly step depends only on the initial poses of the current and later-assembled parts. Based on this dependency, we propose Backward Layout Search (BLS), which assigns initial part poses in reverse assembly order. Each expansion performs geometric, kinematic, grasp, and prescribed-motion checks, while collision masks and candidate set filtering remove infeasible initial part pose candidates. Promising partial layouts are retained through beam selection, and complete layouts are validated by full motion planning in forward assembly order. Experiments on five assembly models show that BLS produces collision-free executable layouts and reduces step evaluations and search time compared with a matched forward search.
发表机构
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University（福建农林大学计算机与信息学院）
- School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）
- Department of System Innovation, Graduate School of Engineering Science, Osaka University（大阪大学工程科学研究生院系统创新系）
机构由 AI 辅助整理，请以论文原文为准。
