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Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites

  • Sierra A. Adibi (a1), Scott Forer (a2), Jeremy Fries (a2) and Logan Yliniemi (a2)
Abstract

With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.

Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s−1 and at an angle of approach of <1° degree.

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References
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Alexis, K., Nikolakopoulos, G. & Tzes, A. 2011. Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances’. Control Engineering Practice 19(10), 11951207.
Anderson, J. D. 2010. Fundamentals of Aerodynamics, 5th edition. McGraw-Hill Education.
Bauer, C. 1995. Ground state-fly state transition control for unique-trim aircraft flight control system. US Patent 5,446,666. 29 August. US Patent and Trademark Office https://www.google.com/patents/US5446666.
Baxt, W. 1991. Use of an artificial neural network for the diagnosis of myocardial infarction. Annals of Internal Medicine 115(11), 843848.
Cully, A., Clune, J., Tarapore, D. & Mouret, J. 2015. Robots that can adapt like animals. Nature 521(7553), 503507.
Drela, M. 2013. XFOIL 6.99 subsonic airfoil development system (software). Released under the GNU GPL. http://web.mit.edu/drela/Public/web/xfoil/.
Ecarlat, P., Cully, A., Maestre, C. & Doncieux, S. 2015. Learning a high diversity of object manipulations through an evolutionary-based babbling. In Proceedings of the Workshop Learning Object Affordances, IROS, 1–2.
Federal Aviation Administration (FAA) 2008. On Landings Part II. Technical Report FAA-P-8740-12 AFS-8 (2008) HQ 101128, FAA.
Federal Aviation Administration (FAA) 2015. Registration and Marking Requirements for Small Unmanned Aircraft. Technical report 80 FR 78593, FAA.
Foster, J. & Neuman, F. 1970. Investigation of a digital automatic aircraft landing system in turbulence. NASA Technical Note D-6066, National Aeronautics and Space Administration.
Gautam, A., Sujit, P. & Saripalli, S. 2014. A survey of autonomous landing techniques for UAVs. In 2014 International Conference on Unmanned Aircraft Systems (ICUAS), 1210–1218. IEEE.
Green, W. E. & Oh, P. Y. 2006. Autonomous hovering of a fixed-wing micro air vehicle. In Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, 2164–2169. IEEE.
Hornik, K., Stinchcombe, M. & White, H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359366.
Jorgensen, C. C. & Schley, C. 1995. A neural network baseline problem for control of aircraft flare and touchdown. In Neural Networks for Control, Miller, W. T., Sutton, R. S., Werbos, P. J. (eds). A Bradford Book (March 2, 1995). 403.
Kim, H., Jordan, M. I., Sastry, S. & Ng, A. Y. 2003. Autonomous helicopter flight via reinforcement learning. In Advances in Neural Information Processing Systems.
Lehman, J. & Stanley, K. O. 2011. Abandoning objectives: evolution through the search for novelty alone. Evolutionary Computation 19(2), 189223.
Lewis, F., Jagannathan, S. & Yesildirak, A. 1998. Neural Network Control of Robot Manipulators and Non-Linear Systems. CRC Press.
Lewis, M. S. 2011. Beaufort Wind Chart – Estimating Winds Speeds. Technical report, National Oceanic and Atmospheric Administration.
Li, G. & Baker, S.P. 2007. Crash risk in general aviation. JAMA 297(14), 15961598.
Li, W. & Harris, D. 2006. Pilot error and its relationship with higher organizational levels: HFACS analysis of 523 accidents. Aviation, Space, and Environmental Medicine 77(10), 10561061.
Mellit, A. & Pavan, A. M. 2010. A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 84(5), 807821.
Mouret, J. & Clune, J. 2015. Illuminating search spaces by mapping elites. Cornell University Library, Preprint - April 21, 2015. arXiv preprint arXiv:1504.04909.
Nguyen, A. M., Yosinski, J. & Clune, J. 2015. Innovation engines: automated creativity and improved stochastic optimization via deep learning. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, 959–966. ACM.
Oncu, M. & Yildiz, S. 2014. An Analysis of Human Causal Factors in Unmanned Aerial Vehicle (UAV) Accidents. PhD thesis, Naval Postgraduate School.
Saeed, A. S., Younes, A. B., Islam, S., Dias, J., Seneviratne, L. & Cai, G. 2015. A review on the platform design, dynamic modeling and control of hybrid UAVs. In 2015 International Conference on Unmanned Aircraft Systems (ICUAS), 806–815. IEEE.
Shappell, S., Detwiler, C., Holcomb, K., Hackworth, C., Boquet, A. & Wiegmann, D. A. 2007. Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system.. Human Factors 49(2), 227242.
Shepherd, J. III & Tumer, K. 2010. Robust neuro-control for a micro quadrotor. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, 1131–1138. ACM.
Watson, D. M., Hardy, G. H. & Warner, D. N. Jr 1983. Flight-test of the glide-slope track and flare-control laws for an automatic landing system for a powered-lift STOL airplane. NASA Technical Paper 2128, NASA Scientific and Technical Information Branch.
Yliniemi, L., Agogino, A. K. & Tumer, K. 2014a. Multirobot coordination for space exploration. AI Magazine 4(35), 6174.
Yliniemi, L., Agogino, A. & Tumer, K. 2014b. Simulation of the introduction of new technologies in air traffic management. Adaptive Learning Agents, Part 3. Connection Science 27(3), 269287.
Yliniemi, L. & Tumer, K. 2014. PaCcET: an objective space transformation to iteratively convexify the pareto front. In 10th International Conference on Simulated Evolution and Learning (SEAL).
Yliniemi, L. & Tumer, K. 2015. Complete coverage in the multi-objective PaCcET framework. In Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1525–1526.
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The Knowledge Engineering Review
  • ISSN: 0269-8889
  • EISSN: 1469-8005
  • URL: /core/journals/knowledge-engineering-review
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