How Should a Robot Configure Its Laser Scanner for Inspection?
For robotic inspection tasks, this work addresses the underexplored problem of sensing parameter configuration, offering a practical solution that enhances measurement quality.
The paper tackles the problem of configuring a laser scanner's sensing parameters for robotic inspection, proposing SenseHD which uses hyperdimensional associative memory to select stable sensing regimes. Experiments show it significantly improves inspection reliability while being lightweight and efficient.
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.