Toward a Closed-Loop Autonomous Sensing Framework for UAS-Based Particulate Matter Mapping - Reliford, Anaiya; Smith, Sonya T. (Proceedings of the AAAI Symposium Series, 2026)
Small uncrewed aerial systems (sUAS) offer a powerfulcapability for high-resolution characterization of fineparticulate matter (PM2.5) within the atmospheric boundarylayer; however, their operational utility is limited byrotor-induced aerodynamic disturbances, platform-dependentsensor bias, and the absence of integrated architecturesthat couple sensing, learning, and control. This paperpresents an end-to-end autonomous particulate mattersensing framework that unifies sensor calibration andvalidation, CFD-guided sensor placement, custom payloadintegration, synchronized onboard computation,physics-informed measurement correction, and autonomousflight execution within a single system architecture.Computational fluid dynamics of a DJI Matrice 100quadcopter is used to identify an aerodynamically quiescentsampling region for an Alphasense OPC-N3 optical particlecounter, while a companion-computer-based pipelinesynchronizes particle measurements with vehicle telemetryusing MAVLink during autonomous flight. To mitigateflight-induced measurement distortion, residual-basedregression models incorporating telemetry andphysics-derived aerodynamic features are employed tocorrect airborne PM2.5 observations relative to stationaryreferences. The resulting corrected particulate estimatesprovide a reliable perceptual state for spatial mapping andhigher-level autonomy. Field experiments demonstrate stableautonomous data acquisition, consistent sensor–telemetrysynchronization, and statistically meaningful reduction ofairborne–stationary measurement discrepancies.Collectively, this framework establishes a scalablefoundation for closed-loop airborne particulate mattersensing and represents a transferable autonomy patternapplicable to tactical autonomous systems that mustintegrate perception, learning, and control to operateunder real-world uncertainty.