Prospective Architectures for Onboard vs Cloud-based Decision Making for Unmanned Aerial Systems



Published Oct 2, 2017
Shankar Sankararaman Christopher Teubert


This paper investigates propsective architectures for decisionmaking in unmanned aerial systems. When these unmanned vehicles operate in urban environments, there are several sources of uncertainty that affect their behavior, and decisionmaking algorithms need to be robust to account for these different sources of uncertainty. It is important to account for several risk-factors that affect the flight of these unmanned systems, and facilitate decision-making by taking into consideration these various risk-factors. In addition, there are several technical challenges related to autonomous flight of unmanned aerial systems; these challenges include sensing, obstacle detection, path planning and navigation, trajectory generation and selection, etc. Many of these activities require significant computational power and in many situations, all of these activities need to be performed in real-time. In order to efficiently integrate these activities, it is important to develop a systematic architecture that can facilitate realtime decision-making. Four prospective architectures are discussed in this paper; on one end of the spectrum, the first architecture considers all activities/computations being performed onboard the vehicle whereas on the other end of the spectrum, the fourth and final architecture considers all activities/computations being performed in the cloud, using a new service known as “Prognostics as a Service” that is being developed at NASA Ames Research Center. The four different architectures are compared, their advantages and disadvantages are explained and conclusions are presented.

How to Cite

Sankararaman, S., & Teubert, C. (2017). Prospective Architectures for Onboard vs Cloud-based Decision Making for Unmanned Aerial Systems. Annual Conference of the PHM Society, 9(1).
Abstract 308 | PDF Downloads 102



prognostics, software, unmanned systems, decision-making

Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. Geoscience and Remote Sensing, IEEE Transactions on, 47(3), 722–738.
Caballero, F., Merino, L., Ferruz, J., & Ollero, A. (2009). Unmanned aerial vehicle localization based on monocular vision and online mosaicking. Journal of Intelligent and Robotic Systems, 55(4-5), 323–343.
Cai, G., Feng, L., Chen, B. M., & Lee, T. H. (2008). Systematic design methodology and construction of UAV helicopters. Mechatronics, 18(10), 545–558.
Chakrabarty, A., Morris, R. A., Bouyssounouse, X., & Hunt, R. (2017). An integrated system for autonomous search and track with a small unmanned aerial vehicle. In AIAA Information Systems-AIAA Infotech@ Aerospace (p. 0671).
Coombes, M., McAree, O., Chen, W.-H., & Render, P. (2012). Development of an autopilot system for rapid prototyping of high level control algorithms. In Control (CONTROL), 2012 UKACC International Conference on (pp. 292–297).
Corke, P., Hrabar, S., Peterson, R., Rus, D., Saripalli, S., & Sukhatme, G. (2004). Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle. In Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 IEEE International Conference on (Vol. 4, pp. 3602–3608).
D’Souza, S. N. (2017). Developing a Generalized Trajectory Modeling Framework for Small UAS Performance in the Presence of Wind. In AIAA Information Systems-AIAA Infotech@ Aerospace (p. 0447).
Ducard, G. J. (2009). Fault-tolerant flight control and guidance systems: Practical methods for small unmanned aerial vehicles. Springer Science & Business Media.
Everaerts, J., et al. (2008). The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 1187–1192.
Gupte, S., Mohandas, P. I. T., & Conrad, J. M. (2012). A survey of quadrotor unmanned aerial vehicles. In Southeastcon, 2012 proceedings of ieee (pp. 1–6).
Haldar, A., & Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design. John Wiley.
Hening, S., Ippolito, C. A., Krishnakumar, K. S., Stepanyan, V., & Teodorescu, M. (2017). 3D LiDAR SLAM Integration with GPS/INS for UAVs in Urban
GPS-Degraded Environments. In AIAA Information Systems-AIAA Infotech@ Aerospace (p. 0448).
Hohenbichler, M., & Rackwitz, R. (1983). First-order concepts in system reliability. Structural safety, 1(3), 177–188.
Jang, D.-S., Ippolito, C. A., Sankararaman, S., & Stepanyan, V. (2017). Concepts of airspace structures and system analysis for uas traffic flows for urban areas. In AIAA Information Systems-AIAA Infotech@ Aerospace (p. 0449).
Jun, M., & DAndrea, R. (2003). Path planning for unmanned aerial vehicles in uncertain and adversarial environments. In Cooperative control: models, applications and algorithms (pp. 95–110). Springer.
Kim, B. S., & Calise, A. J. (1997). Nonlinear flight control using neural networks. Journal of Guidance, Control, and Dynamics, 20(1), 26–33.
Kim, H. J., Shim, D. H., & Sastry, S. (2002). Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles. In American Control Conference, 2002. Proceedings of the 2002 (Vol. 5, pp.3576–3581).
Kim, J.-H., & Sukkarieh, S. (2003). Airborne simultaneous localisation and map building. In Robotics and automation, 2003. proceedings. icra’03. ieee international conference on (Vol. 1, pp. 406–411).
Kim, K., Kim, T., Lee, K., & Kwon, S. (2011). Fuel cell system with sodium borohydride as hydrogen source for unmanned aerial vehicles. Journal of Power Sources, 196(21), 9069–9075.
Kopardekar, P. H. (2014, April). Unmanned Aerial System (UAS) Traffic Management (UTM): Enabling Low-Altitude Airspace and UAS Operations (Tech. Rep. No. NASA/TM2014218299). Moffett Field, CA 94035, USA: NASA Ames Research Center.
Krishnakumar, K., Ippolito, C., Kopardekar, P., Melton, J., Stepanyan, V., Sankararaman, S., & Nikaido, B. (2017). Safe Autonomous Flight Environment
(SAFE50) for the Notional Last “50 ft” of Operation of “55 lb” Class of UAS. In AIAA SciTech Conference, Grapevine, Texas, USA.
Marsh, R., Vukson, S., Surampudi, S., Ratnakumar, B., Smart, M., Manzo, M., & Dalton, P. (2001). Li-Ion batteries for aerospace applications. Journal of power sources, 97, 25–27.
McAree, O., & Chen, W.-H. (2013). Artificial situation awareness for increased autonomy of unmanned aerial systems in the terminal area. Journal of Intelligent & Robotic Systems, 1–11.
Ollero, A., & Maza, I. (2007). Multiple heterogeneous unmanned aerial vehicles. Springer Publishing Company, Incorporated.
Ruff, H. A., Narayanan, S., & Draper, M. H. (2002). Human interaction with levels of automation and decision-aid fidelity in the supervisory control of multiple simulated unmanned air vehicles. Presence: Teleoperators and virtual environments, 11(4), 335–351.
Rysdyk, R. (2006). Unmanned aerial vehicle path following for target observation in wind. Journal of guidance, control, and dynamics, 29(5), 1092–1100.
Saha, B., Koshimoto, E., Quach, C., Hogge, E., Strom, T., Hill, B., & Goebel, K. (2011). Predicting battery life for electric UAVs. AIAA Infotech@ Aerospace.
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52, 228–247.
Sankararaman, S., Daigle, M. J.,&Goebel, K. (2014). Uncertainty quantification in remaining useful life prediction using first-order reliability methods. Reliability, IEEE Transactions on, 63(2), 603–619.
Sankararaman, S., & Kalmanje, K. (2017). Towards a computational framework for autonomous decision-making in unmanned aerial vehicles. In AIAA Information Systems-AIAA Infotech@ Aerospace (p. 0446).
Shakernia, O., Vidal, R., Sharp, C. S., Ma, Y., & Sastry, S. (2002). Multiple view motion estimation and control for landing an unmanned aerial vehicle. In Robotics and Automation, 2002. Proceedings. ICRA’02. IEEE International Conference on (Vol. 3, pp. 2793–2798).
Sharp, C. S., Shakernia, O., & Sastry, S. S. (2001). A vision system for landing an unmanned aerial vehicle. In Robotics and Automation, 2001. Proceedings 2001 ICRA. IEEE International Conference on (Vol. 2, pp. 1720–1727).
Shim, D. H., Chung, H., & Sastry, S. S. (2006). Conflict-free navigation in unknown urban environments. Robotics & Automation Magazine, IEEE, 1 (3), 27–33.
Sigurd, K., & How, J. (2003). UAV trajectory design using
total field collision avoidance. American Institute of
Aeronautics and Astronautics.
Sinopoli, B., Micheli, M., Donato, G., & Koo, T. J. (2001).
Vision based navigation for an unmanned aerial vehicle.
In Robotics and Automation, 2001. Proceedings
2001 ICRA. IEEE International Conference on (Vol. 2,
pp. 1757–1764).
Stepanyan, V., & Krishnakumar, K. S. (2017). Estimation,
navigation and control of multi-rotor drones in an urban
wind field. In AIAA Information Systems-AIAA Infotech@
Aerospace (p. 0670).
Wang, J., Garratt, M., Lambert, A., Wang, J. J., Han, S., &
Sinclair, D. (2008). Integration of gps/ins/vision sensors
to navigate unmanned aerial vehicles. The International
Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences, 37, 963–970.
Yang, G., & Kapila, V. (2002). Optimal path planning for
unmanned air vehicles with kinematic and tactical constraints.
In Decision and Control, 2002, Proceedings of
the 41st IEEE Conference on (Vol. 2, pp. 1301–1306).
Technical Research Papers

Most read articles by the same author(s)

1 2 > >>