The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable safe and reliable autonomous unmanned flight operations. Unmanned aircraft systems (UAS) are used in a growing number of applications requiring increased vehicle autonomy, such as indoor operations, civilian infrastructure inspection, precision agriculture and aquaculture, remote sensing, wildlife tracking and conservation, and package/medicine delivery. As the diversity and number of applications of autonomous UAS keep growing, platform-independent solutions to onboard autonomy become increasingly important. Potential market segments for the proposed technology are focused on the most challenging use cases such as last-mile delivery, urban infrastructure inspection, and passenger air vehicles (air ambulance, air taxi, air shuttle). Therefore, technological advances in platform-independent onboard autonomy, particularly for small unmanned aircraft systems (sUAS), can have a large positive societal impact by enabling safety and reliability of UAS.
This Small Business Innovation Research (SBIR) Phase I project investigates a universal flight management unit (FMU) for nonlinearly stable and robust autonomy of UAS, in a platform-independent manner. At present, there is a dearth of nonlinearly stable and robust flight stacks that are platform/model-independent and real-time implementable on existing hardware, particularly for sUAS. Two critical challenges to be overcome for reliable platform-independent autonomy of UAS are: (A) dynamic stability in the presence of constraints on onboard processors, sensors and actuators; and (B) robustness to dynamic external uncertainties (e.g., wind, weather) and internal causes (e.g., changing payloads, onboard faults). The scientific objectives of this Phase 1 research are: (1) onboard trajectory planning, control and navigation for autonomous operations of UAS to provide dynamic stability and robustness to disturbances and sensor noise; (2) embedded software-hardware integration of guidance, navigation and control algorithms with commercially available hardware to build a FMU for onboard autonomy; and (3) experimental verification and validation of this FMU on different quadrotor unmanned aerial vehicle platforms, including a flying-wing platform. The underlying framework behind this platform-independent FMU will be nonlinearly stable and robust model-free control and estimation techniques that ensure safe and reliable autonomous operations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
