提升建筑领域的机器人精度：基于模块化因子图框架利用高精度加速度计进行挠度和间隙补偿
Enhancing Robotic Precision in Construction: A Modular Factor Graph-Based Framework to Deflection and Backlash Compensation Using High-Accuracy Accelerometers
AI总结：
本研究提出一种基于因子图框架的模块化方法，结合高精度加速度计补偿挠度和间隙，显著提升建筑机器人定位精度，相比虚拟关节法在xy平面误差阈值降低50%。
摘要：
精确定位在建筑行业中至关重要，而劳动力短缺凸显了自动化的需求。具有长运动链的机器人系统需要到达复杂的工作空间，包括地板、墙壁和天花板。这些要求由于运动链各部分的挠度和间隙等效应，显著影响定位精度。在本工作中，我们提出了一种新颖方法，将挠度和间隙补偿模型与高精度加速度计集成，显著提高了位置精度。我们的方法采用基于因子图公式的模块化框架来估计运动链的状态，利用加速度测量来为模型提供信息。在公开数据集上的广泛测试，反映了真实的建筑扰动，证明了我们方法的优势。所提出的方法在xy平面上将$95\%$误差阈值降低了$50\%$，相比最先进的虚拟关节方法，并在加入基底倾斜补偿时降低了$31\%$。
英文摘要：
Accurate positioning is crucial in the construction industry, where labor shortages highlight the need for automation. Robotic systems with long kinematic chains are required to reach complex workspaces, including floors, walls, and ceilings. These requirements significantly impact positioning accuracy due to effects such as deflection and backlash in various parts along the kinematic chain. In this work, we introduce a novel approach that integrates deflection and backlash compensation models with high-accuracy accelerometers, significantly enhancing position accuracy. Our method employs a modular framework based on a factor graph formulation to estimate the state of the kinematic chain, leveraging acceleration measurements to inform the model. Extensive testing on publicly released datasets, reflecting real-world construction disturbances, demonstrates the advantages of our approach. The proposed method reduces the $95\%$ error threshold in the xy-plane by $50\%$ compared to the state-of-the-art Virtual Joint Method, and by $31\%$ when incorporating base tilt compensation.
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