How Should a Robot Configure Its Laser Scanner for Inspection?

2026-06-19

AI Summary

This work addresses the critical yet underexplored challenge of optimizing sensing parameters for laser scanners to enhance measurement quality. While prior research has predominantly focused on motion and viewpoint planning, effective methods for parameter configuration remain lacking. To bridge this gap, the study formulates sensing parameter selection as a discrete perception-action decision problem conditioned on high-level instructions and introduces a hyperdimensional associative memory mechanism to enable lightweight and robust configuration decisions. Evaluated on a real robotic platform, the proposed approach significantly improves detection reliability compared to baseline methods, demonstrating superior efficiency and robustness in supporting autonomous inspection tasks.

Abstract

Robotic inspection relies on accurate sensing to acquire high-fidelity geometric measurements for defect detection and metrology. While prior work has focused on robot motion and viewpoint planning, how to configure sensing parameters remains largely underexplored, despite their decisive impact on measurement quality. We propose SenseHD, a robotic sensing system that formulates scanner configuration as an instruction-conditioned sensing decision. Instead of predicting precise parameter values, SenseHD treats sensing parameters as discrete sensing actions and selects stable sensing regimes through hyperdimensional associative memory. Experiments on a real robotic inspection platform demonstrate that SenseHD robustly selects appropriate sensing configurations and significantly improves inspection reliability, while remaining lightweight and efficient compared to baseline methods.

Problem

• robotic inspection
• laser scanner configuration
• sensing parameters
• measurement quality
• geometric measurements

Innovation

• scanner configuration
• instruction-conditioned sensing
• hyperdimensional computing
• robotic inspection
• sensing actions