Name
Abstract | ||
|---|---|---|
Autonomous aerial transportation will be a fixture of future robotic societies, simultaneously requiring more stringent safety requirements and fewer resources for characterization than current commercial air transportation. More robust, adaptable, self-state estimation will be necessary to create such autonomous systems. We present a modular, scalable, distributed pressure sensing skin for aerodynamic state estimation of a large, flexible aerostructure. This skin used a network of 22 nodes that performed in situ computation and communication of data collected from 74 pressure sensors, which were embedded into the skin panels of an ultra-lightweight 14-foot wingspan made from commutable, lattice-based subcomponents, and tested at NASA Langley Research Center's 14X22 wind tunnel. The density of the pressure sensors allowed for the use of a novel distributed algorithm to generate estimates of the wing lift contribution that were more accurate than the direct integration of the pressure distribution over the wing surface. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IROS.2018.8593664 | 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Lift (force),Computer science,Control engineering,Real-time computing,Distributed algorithm,Pressure sensor,Autonomous system (Internet),Wind tunnel,Modular design,Aerodynamics,Scalability | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Cellucci | 1 | 3 | 1.77 |
| Nicholas Cramer | 2 | 0 | 0.34 |
| Sean Shan-Min Swei | 3 | 0 | 1.69 |