Abstract
This work presents an experiment-driven aerodynamic disturbance modeling technique that leverages experiences from past flights to construct a predictive model of the exogenous forces acting on an aerial robot. Specifically, we consider operation in turbulent air stemming from the interaction between wind and nearby surfaces. To construct the disturbance model, we employ Locally Weighted Projection Regression and relate the aerial robot’s state and an experimentally learned freestream wind model to the disturbance forces estimated during flight through the environment. The approach is experimentally validated through a set of flight tests in an indoor environment with artificially generated turbulent airflow that illustrate the computation of this disturbance model, its generalizability across flow conditions, and its utility for disturbance-aware motion planning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hoffmann, G.M., Huang, H., Waslander, S.L., Tomlin, C.J.: Quadrotor helicopter flight dynamics and control: Theory and experiment. In: Proceedings of the AIAA Guidance, Navigation, and Control Conferrnce, vol. 2, Hilton Head, USA, August 2007
Bangura, M., Mahony, R.: Nonlinear dynamic modeling for high performance control of a quadrotor. In: Proceedings of the Australian Conference on Robotics and Automation, Wellington, New Zealand, pp. 1–10, December 2012
Omari, S., Hua, M.D., Ducard, G., Hamel, T.: Nonlinear control of VTOL UAVs incorporating flapping dynamics. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp. 2419–2425, November 2013
Leishman, R., Macdonald, J., Beard, R., McLain, T.: Quadrotors and accelerometers: state estimation with an improved dynamic model. IEEE Control Syst. Mag. 34(1), 28–41 (2014)
Abeywardena, D., Wang, Z., Dissanayake, G., Waslander, S., Kodagoda, S.: Model-aided state estimation for quadrotor micro air vehicles amidst wind disturbances. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, USA, pp. 4813–4818, September 2014
Grande, R.C., Chowdhary, G., How, J.P.: Experimental validation of bayesian nonparametric adaptive control using gaussian processes. J. Aero. Inf. Syst. 11(9), 565–578 (2014)
Mohamed, A., Abdulrahim, M., Watkins, S., Clothier, R.: Development and flight testing of a turbulence mitigation system for micro air vehicles. J. Field Robot. 33(5), 639–660 (2016)
Ranganathan, B.N., Penskiy, I., Dean, W., Bergbreiter, S., Humbert, J.S.: Bio-inspired wind frame state sensing and estimation for mav applications. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, pp. 2729–2735, September 2015
Gremillion, G.M., Castano, L.M., Humbert, J.S.: Disturbance rejection with distributed acceleration and strain sensing. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, vol. 2, Kissimmee, USA, pp. 1623–1639, January 2015
Yeo, D.W., Sydney, N., Paley, D.A.: Onboard flow sensing for rotary-wing uav pitch control in wind. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, vol. 4, National Harbor, USA, pp. 2445–2455, January 2016
Desaraju, V., Michael, N.: Hierarchical adaptive planning in environments with uncertain, spatially-varying disturbance forces. In: Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China, pp. 5171–5176, May 2014
Lawrance, N., Sukkarieh, S.: Path planning for autonomous soaring flight in dynamic wind fields. In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 2499–2505, May 2011
Hollinger, G.A., Pereira, A.A., Binney, J., Somers, T., Sukhatme, G.S.: Learning uncertainty in ocean current predictions for safe and reliable navigation of underwater vehicles. J. Field Robot. 33(1), 47–66 (2016)
Galway, D., Etele, J., Fusina, G.: Modeling of urban wind field effects on unmanned rotorcraft flight. J. Aircraft 48(5), 1613–1620 (2011)
Bartholomew, J., Calway, A., Mayol-Cuevas, W.: Learning to predict obstacle aerodynamics from depth images for micro air vehicles. In: Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China, pp. 4967–4973, May 2014
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Vijayakumar, S., D’Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Comput. 17(12), 2602–2634 (2005)
Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: Proceedings of the SPIE, vol. 3068, Orlando, USA, pp. 182–193, July 1997
Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19(3), 20–32 (2012)
Hovakimyan, N., Cao, C.: L1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation, vol. 21. SIAM, Philadelphia (2010)
Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In: Proceedings of the International Symposium of Robotics Research, Singapore, December 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yao, J.W., Desaraju, V.R., Michael, N. (2017). Experience-Based Models of Surface Proximal Aerial Robot Flight Performance in Wind. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-50115-4_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50114-7
Online ISBN: 978-3-319-50115-4
eBook Packages: EngineeringEngineering (R0)