System-Level Error Propagation and Tail-Risk Amplification in Reference-Based Robotic Navigation

2026-02-08

AI Summary

This work addresses the critical issue of error amplification in reference-frame-guided robotic navigation, where structural perturbations introduced during installation are progressively magnified across a multi-stage geometric perception pipeline, significantly increasing execution-level errors and tail risk. Focusing on a biplanar X-ray navigation system, the study establishes a unified system-level error propagation model and, for the first time, quantifies the dominant role of rotational mounting errors in exacerbating tail risk—thereby transcending conventional evaluation paradigms that focus solely on average accuracy. By integrating first-order analytical uncertainty propagation with Monte Carlo simulations, the framework identifies key sensitivity channels and characterizes worst-case error behavior. Experimental results confirm that rotational misalignment is the primary driver of system-level error amplification, and demonstrate that the proposed model accurately predicts error propagation trends under realistic imaging conditions, offering a theoretical foundation for designing high-reliability navigation systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Image guided robotic navigation systems often rely on reference based geometric perception pipelines, where accurate spatial mapping is established through multi stage estimation processes. In biplanar X ray guided navigation, such pipelines are widely used due to their real time capability and geometric interpretability. However, navigation reliability can be constrained by an overlooked system level failure mechanism in which installation induced structural perturbations introduced at the perception stage are progressively amplified along the perception reconstruction execution chain and dominate execution level error and tail risk behavior. This paper investigates this mechanism from a system level perspective and presents a unified error propagation modeling framework that characterizes how installation induced structural perturbations propagate and couple with pixel level observation noise through biplanar imaging, projection matrix estimation, triangulation, and coordinate mapping. Using first order analytic uncertainty propagation and Monte Carlo simulations, we analyze dominant sensitivity channels and quantify worst case error behavior beyond mean accuracy metrics. The results show that rotational installation error is a primary driver of system level error amplification, while translational misalignment of comparable magnitude plays a secondary role under typical biplanar geometries. Real biplanar X ray bench top experiments further confirm that the predicted amplification trends persist under realistic imaging conditions. These findings reveal a broader structural limitation of reference based multi stage geometric perception pipelines and provide a framework for system level reliability analysis and risk aware design in safety critical robotic navigation systems.

Problem

• error propagation
• tail-risk amplification
• reference-based navigation
• structural perturbations
• system-level reliability

Innovation

• error propagation
• tail-risk amplification
• reference-based navigation
• system-level modeling
• biplanar X-ray