Enhancing Robotic Precision in Construction: A Modular Factor Graph-Based Framework to Deflection and Backlash Compensation Using High-Accuracy Accelerometers

TL;DR

This paper presents a modular factor graph-based framework utilizing high-accuracy accelerometers to compensate for deflection and backlash, significantly improving robotic positioning accuracy in construction tasks.

Contribution

It introduces a novel integration of deflection and backlash models with accelerometers within a factor graph framework for enhanced robotic positioning accuracy.

Findings

Reduces 95% error threshold in xy-plane by 50% over the Virtual Joint Method.

Achieves 31% error reduction when including base tilt compensation.

Demonstrates effectiveness on real-world construction disturbance datasets.

Abstract

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.