Robot sensing and navigation data
Uses costmaps, zones, point clouds, odometry, and command streams collected from a Unitree Go2 robotics workflow.
Project
Autonomous Robotic Factory-Floor Assessment
A Unitree Go2 robotics stack for converting factory-floor observations into entropy-based measures of organization, disorder, and run-to-run change.
Overview
Robot telemetry turned into factory-floor assessment metrics.
This project uses a mobile robotic sensing stack to assess factory-floor organization from spatial, motion, and command-stream data.
The system converts costmaps, zone definitions, point clouds, odometry, and robot commands into normalized entropy metrics, anomaly scores, and run-level summaries for comparing factory-floor conditions over time.
Poster
Innovation Factory assessment workflow
System
What the assessment stack computes
Entropy-based run summaries
Computes occupancy entropy, zone spatial entropy, point-cloud eigentropy, trajectory entropy, zone visit entropy, and command-stream entropy.
Run-to-run change detection
Produces Jensen-Shannon run comparisons, anomaly scores, and normalized AUC over time for comparing floor states and identifying disorder.
Impact
Making factory observations easier to review.
Repeatable assessment
Connects robot telemetry to manufacturing-style analytics for comparing runs and making layout disorder easier to diagnose.
Manuscript in preparation
Targeting Robotics and Computer-Integrated Manufacturing with a focus on autonomous robotic entropy-based factory-floor assessment.
Links