Assembly robots have been widely used in the manufacturing industry. However, performing precise assembly tasks still poses a great challenge for robots due to numerous sources of the uncertainties such as fixtures, end effectors, or actuators, and the most critical technical bottleneck is to find the best search strategy to improve positioning accuracy in assembling. Search strategies are evaluated in terms of executing time, precision, stability, and applicable geometries and features of parts. This review is highly motivated to gain the state of the art of search strategies and identify some technical means which can further improve existing algorithms. Without losing the generality of this review discussion, robotic assemblies for peg-in-hole (PiH) are focused. Search strategies are classified and discussed based on different criteria respectively such as the number and types of sensors, and the dimensions of search spaces. To tackle with the assembly operations in an ill-structured environment, a search strategy has to fuse and utilize the sensing data from multiple sources such as the integration of force sensing and vision, and the search strategies with the sensing cooperation are explored. As the conclusion, the future research directions on search strategies for automated assembling have been discussed.

装配机器人已广泛应用于制造业。然而，由于夹具、末端执行器或执行器等不确定性来源众多，执行精确装配任务仍然对机器人构成巨大挑战，最关键的技术瓶颈是找到最佳搜索策略以提高装配定位精度。搜索策略根据执行时间、精度、稳定性以及适用的几何形状和零件特征进行评估。本次审查旨在获得最先进的搜索策略并确定一些可以进一步改进现有算法的技术手段。在不失本次审查讨论的一般性的情况下，重点关注孔中钉 (PiH) 的机器人组件。搜索策略分别根据传感器的数量和类型、搜索空间的维度等不同的标准进行分类和讨论。为了解决结构不良环境中的组装操作，搜索策略必须融合和利用来自多个来源的传感数据，例如力传感和视觉的集成，并探索具有传感合作的搜索策略。作为结论，讨论了自动化组装搜索策略的未来研究方向。并探索了具有感知合作的搜索策略。作为结论，讨论了自动化组装搜索策略的未来研究方向。并探索了具有感知合作的搜索策略。作为结论，讨论了自动化组装搜索策略的未来研究方向。