Standardised Embedded Data framework for Drones [SEDD]

Abstract:

A number of barriers to entry remain for UAS use in science. One in particular is that of implementing an experiment and UAS specific software stack. Currently this stack is most often developed in-house and customised for a particular UAS-sensor pairing - limiting its reuse. Alternatively, when adaptable a suitable commercial package may be used, but such systems are both costly and usually suboptimal.

In order to address this challenge the Standardised Embedded Data framework for Drones [SEDD] is being developed in μpython. SEDD provides an open source, reusable, and scientist-accessible drop in solution for drone data capture and triage. Targeted at embedded hardware, and offering easy access to standard I/O interfaces, SEDD provides an easy solution for simply capturing data from a sensor. However, the intention is rather to enable more complex systems of multiple sensors, computer hardware, and feedback loops, via 3 primary components.

A data asset manager ensures data assets are associated with appropriate metadata as they are captured. Thereafter, the asset is easily archived or otherwise redirected, possibly to - onboard storage, onboard compute resource for processing, an interface for transmission, another sensor control system, remote storage and processing (such as EarthCube's CHORDS), or to any combination of the above.

A service workflow managerenables easy implementation of complex onboard systems via dedicated control of multiple continuous and periodic services. Such services will include the housekeeping chores of operating a UAS and multiple sensors, but will also permit a scientist to drop in an initial scientific data processing code utilising on-board compute resources beyond the autopilot. Having such capabilities firstly enables easy creation of real-time feedback, to the human- or auto- pilot, or other sensors, on data quality or needed flight path changes. Secondly, compute hardware provides the opportunity to carry out real-time data triage, for the purposes of conserving on-board storage space or transmission bandwidth in inherently poor connectivity environments.

A compute manager is finally included. Depending on system complexity, and given the need for power efficient parallelism, it can quickly become necessary to provide a scheduling service for multiple workflows.