This paper introduces a novel, markerless, step-by-step, in-situ 3D Augmented
Reality (AR) instruction method and its application - BRICKxAR (Multi 3D
Models/M3D) - for small parts assembly. BRICKxAR (M3D) realistically visualizes
rendered 3D assembly parts at the assembly location of the physical assembly
model (Figure 1). The user controls the assembly process through a user
interface. BRICKxAR (M3D) utilizes deep learning-trained 3D model-based
registration. Object recognition and tracking become challenging as the
assembly model updates at each step. Additionally, not every part in a 3D
assembly may be visible to the camera during the assembly. BRICKxAR (M3D)
combines multiple assembly phases with a step count to address these
challenges. Thus, using fewer phases simplifies the complex assembly process
while step count facilitates accurate object recognition and precise
visualization of each step. A testing and heuristic evaluation of the BRICKxAR
(M3D) prototype and qualitative analysis were conducted with users and experts
in visualization and human-computer interaction. Providing robust 3D AR
instructions and allowing the handling of the assembly model, BRICKxAR (M3D)
has the potential to be used at different scales ranging from manufacturing
assembly to construction.

介绍一种新的无标记、分步、现场三维增强现实（AR）指导方法及其应用 - BRICKxAR（Multi 3D Models/M3D），用于小零件组装。通过使用深度学习训练的三维模型注册，BRICKxAR（M3D）能够在物理组装模型的组装位置逼真地可视化渲染的三维组装零件，并通过用户界面控制组装流程，同时结合多个组装阶段和步骤计数的方式来解决物体识别和跟踪的难题，从而简化复杂的组装过程并实现准确的物体识别和精确的可视化展示。通过对 BRICKxAR（M3D）的原型进行测试和启发式评估，并与可视化和人机交互领域的用户和专家进行定性分析，该方法具有提供强大的三维增强现实指导和处理组装模型的潜力，可应用于从制造组装到建筑施工等不同规模的领域。

基于多个 3D 模型的注册的增强现实（AR）装配指导

Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions  for Assembly

The paper discusses the prerequisites for the creation, technical solutions
and implementation of an automated control system for the assembly of a small
spacecraft. Both the hardware and software implementation of the system that
provides control and logging of the assembly process of individual units at
various workplaces are analyzed. The article presents solutions to reduce the
requirements for equipment used to control the assembly technology, in
particular, to use cameras with a lower resolution, through the use of special
algorithms for the formation and processing of technological marks. A tool is
presented that allows you to control the tightening torques of threaded
connections and limit the tightening torque according to a given algorithm with
wireless control. The developed system provides the functions of not only
control, but also logging of the technological process, which can be useful in
the future when creating a digital twin of the product.

本文讨论了小型航天器组装自动控制系统的创建前提、技术解决方案和实施。分析了系统的硬件和软件实现，提供了对各个工作场所中个体单元组装过程的控制和记录。通过使用特殊算法形成和处理技术标记，提出了减少控制组装技术所需设备要求的解决方案，特别是使用分辨率较低的摄像头。提供了一种工具，可通过无线控制控制拧紧螺纹连接的扭矩，并根据给定算法限制拧紧扭矩。所开发的系统不仅提供了控制功能，还能记录技术过程，这在未来创建产品的数字孪生时可能非常有用。

技术节点装配过程的自动化机器视觉控制系统

Automated machine vision control system for technological nodes assembly  process

This paper addresses the problem of copying an unknown assembly of primitives
with known shape and appearance using information extracted from a single
photograph by an off-the-shelf procedure for object detection and pose
estimation. The proposed algorithm uses a simple combination of physical
stability constraints, convex optimization and Monte Carlo tree search to plan
assemblies as sequences of pick-and-place operations represented by STRIPS
operators. It is efficient and, most importantly, robust to the errors in
object detection and pose estimation unavoidable in any real robotic system.
The proposed approach is demonstrated with thorough experiments on a UR5
manipulator.

该论文提出了一种利用物理稳定性约束和优化算法进行拼装的方法，该方法使用从单个摄影中提取的信息进行对象检测和姿态估计，并且对于任何实际机器人系统中不可避免的对象检测和姿态估计误差具有鲁棒性，并且已在 UR5 机械臂上进行了全面实验。