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

2025-01-01

IEEE Robotics and Automation Letters

AI Summary

To address long-range positioning errors in construction-site long-chain robots—caused by structural flexure and mechanical backlash—this paper proposes a modular state estimation framework based on factor graphs. The method innovatively integrates high-precision accelerometer measurements with a unified flexure-and-backlash compensation model within the factor graph, synergistically combining multibody dynamics modeling, virtual-joint error modeling, and base-tilt compensation to enable real-time, end-to-end pose correction under dynamic disturbances. Evaluated on a real-world construction disturbance dataset, the approach reduces the 95% confidence xy-plane positioning error by 50% compared to the state-of-the-art virtual-joint method; incorporating base-tilt compensation further improves accuracy by 31%. These results demonstrate substantial gains in both positioning accuracy and robustness for long-chain robots operating in complex, unstructured construction environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 letter, 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.

Problem

• robotic precision
• construction industry
• long-distance operation

Innovation

• High-Precision Sensors
• Dynamic Adjustment
• Robot Localization Accuracy