Advertisement

Opportunities and Challenges with Autonomous Micro Aerial Vehicles

  • Vijay KumarEmail author
  • Nathan Michael
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 100)

Abstract

We survey the recent work on micro-UAVs, a fast-growing field in robotics, outlining the opportunities for research and applications, along with the scientific and technological challenges. Micro-UAVs can operate in three-dimensional environments, explore and map multi-story buildings, manipulate and transport objects, and even perform such tasks as assembly. While fixed-base industrial robots were the main focus in the first two decades of robotics, and mobile robots enabled most of the significant advances during the next two decades, it is likely that UAVs, and particularly micro-UAVs will provide a major impetus for the third phase of development.

Keywords

Model Predictive Control Wind Gust Autonomous Navigation Aerodynamic Model Propellor Blade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    P. Abbeel, Apprenticeship learning and reinforcement learning with application to robotic control. Ph.D. thesis, Stanford University, Stanford, CA, 2008Google Scholar
  2. 2.
    Aeroenvironment nano hummingbird (2011). http://www.avinc.com/nano
  3. 3.
    Ascending Technologies, GmbH. http://www.asctec.de
  4. 4.
    A.G. Bachrach, Autonomous flight in unstructured and unknown indoor environments. Master’s thesis, MIT, Cambridge, MA, 2009Google Scholar
  5. 5.
    C. Bermes, Design and dynamic modeling of autonomous coaxial micro helicopters. Ph.D. thesis, ETH Zurich, Switzerland, 2010Google Scholar
  6. 6.
    C. Bermes, D. Schafroth, S. Bouabdallah, R. Siegwart, Modular simulation model for coaxial rotary wing mavs, in Proceedings of The 2nd International Symposium on Unmanned Aerial Vehicles (2009)Google Scholar
  7. 7.
    M. Blosch, S. Weiss, D. Scaramuzza, R. Siegwart, Vision based MAV navigation in unknown and unstructured environments, in Proceedings of the IEEE International. Conference on Robotics and Automation (Anchorage, AK, 2010), pp. 21–28Google Scholar
  8. 8.
    S. Bouabdallah, Design and control of quadrotors with applications to autonomous flying. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, 2007Google Scholar
  9. 9.
    J. Craig, P. Hsu, S. Sastry, Adaptive control of mechanical manipulators, in Proceedings of the IEEE International. Conference on Robotics and Automation, vol. 3 (1986), pp. 190–195. doi: 10.1109/ROBOT.1986.1087661
  10. 10.
    L. Faruque, J.S. Humbert, Dipteran insect flight dynamics. part 2: lateral-directional motion about hover. J. Theoret. Biol. 265(3), 306–313 (2010). doi: 10.1016/j.jtbi.2010.05.003
  11. 11.
    J. Fink, N. Michael, S. Kim, V. Kumar, Planning and control for cooperative manipulation and transportation with aerial robots. Int. J. Robot. Res. 30(3) (2011)Google Scholar
  12. 12.
    J.H. Gillula, H. Huang, M.P. Vitus, C.J. Tomlin, Design of guaranteed safe maneuvers using reachable sets: autonomous quadrotor aerobatics in theory and practice, in Proceedings of the IEEE International. Conference on Robotics and Automation (Anchorage, AK, 2010), pp. 1649–1654Google Scholar
  13. 13.
    S. Grzonka, G. Grisetti, W. Burgard, Towards a navigation system for autonomous indoor flying, in Proceedings of the IEEE International. Conference on Robotics and Automation (Kobe, Japan, 2009), pp. 2878–2883Google Scholar
  14. 14.
    D. Gurdan, J. Stumpf, M. Achtelik, K. Doth, G. Hirzinger, D. Rus, Energy-efficient autonomous four-rotor flying robot controlled at 1 kHz, in Proceedings of the IEEE International. Conference on Robotics and Automation (Roma, Italy, 2007)Google Scholar
  15. 15.
    H. Huang, G.M. Hoffman, S.L. Waslander, C.J. Tomlin, Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering, in Proceedings of the IEEE International. Conference on Robotics and Automation (Kobe, Japan, 2009), pp. 3277–3282Google Scholar
  16. 16.
    A. Jadbabaie, J. Hauser, On the stability of receding horizon control with a general terminal cost. IEEE Trans. Autom. Control 50(5), 674–678 (2005)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Q. Jiang, V. Kumar, The direct kinematics of objects suspended from cables, in ASME International Design Engineering Technical Conference and Computer and Information in Engineering Conference (2010)Google Scholar
  18. 18.
    Q. Jiang, V. Kumar, in The inverse kinematics of 3-d towing, ed. by J. Lenarcic, M.M. Stanisic. Advances in Robot Kinematics (2010), pp. 321–328Google Scholar
  19. 19.
    H. Kim, D. Shim, S. Sastry, Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles, in Proceedings of the American Control Conference, vol. 5 (Anchorage, AK, 2002), pp. 3576–3581Google Scholar
  20. 20.
    L. Kneip, A. Martinelli, S. Weiss, D. Scaramuzza, R. Siegwart, Closed-form solution for absolute scale velocity determination combining inertial measurements and a single feature correspondence, in Proceedings of the IEEE International Conference on Robotics and Automation (2011), pp. 4546–4553Google Scholar
  21. 21.
    S.M. Lavalle, Planning Algorithms (Cambridge University Press, 2006)Google Scholar
  22. 22.
    T. Lee, M. Leok, N. McClamroch, Geometric tracking control of a quadrotor uav on SE(3), in Proceedings of the IEEE Conference on Decision and Control (2010)Google Scholar
  23. 23.
    M. Likhachev, G. Gordon, S. Thrun, ARA*: anytime A* with provable bounds on sub- optimality. Adv. Neural Inf. Process. Syst. 16 (2003)Google Scholar
  24. 24.
    Q. Lindsey, D. Mellinger, V. Kumar, Construction of cubic structures with quadrotor teams, in Proceedings of Robotics: Science and Systems (Los Angeles, CA, 2011)Google Scholar
  25. 25.
    S. Lupashin, A. Schollig, M. Sherback, R. D’Andrea, A simple learning strategy for high- speed quadrocopter multi-flips, in Proceedings of the IEEE International Conference on Robotics and Automation (Anchorage, AK, 2010), pp. 1642–1648Google Scholar
  26. 26.
    D. Mellinger, V. Kumar, Minimum snap trajectory generation and control for quadrotors, in Proceedings of the IEEE International Conference on Robotics and Automation (Shanghai, China, 2011)Google Scholar
  27. 27.
    D. Mellinger, Q. Lindsey, M. Shomin, V. Kumar, Design, modeling, estimation and control for aerial grasping and manipulation, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (San Francisco, CA, 2011)Google Scholar
  28. 28.
    D. Mellinger, N. Michael, V. Kumar, Trajectory generation and control for precise aggressive maneuvers with quadrotors, in Proceedings of the International Symposium on Experimental Robotics (Delhi, India, 2010)Google Scholar
  29. 29.
    D. Mellinger, M. Shomin, N. Michael, V. Kumar, Cooperative grasping and transport using multiple quadrotors, in International Symposium on Distributed Autonomous System (Lausanne, Switzerland, 2010)Google Scholar
  30. 30.
    B. Mettler, Modeling small-scale unmanned rotorcraft for advanced flight control design. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA, 2001Google Scholar
  31. 31.
    N. Michael, J. Fink, V. Kumar, Cooperative manipulation and transportation with aerial robots. Auton. Rob. 30(1), 73–86 (2011)CrossRefzbMATHGoogle Scholar
  32. 32.
    N. Michael, D. Mellinger, Q. Lindsey, V. Kumar, he grasp multiple micro uav testbed. IEEE Robot. Autom. Mag. (2010)Google Scholar
  33. 33.
    N. Michael, S. Tadokoro, K. Nagatani, K. Ohno, Experiments with air ground coordination for search and rescue in collapsed buildings (2011). Working PaperGoogle Scholar
  34. 34.
    D.S. Miller, G. Gremillion, B. Ranganathan, P.D. Samuel, S. Zarovy, M. Costello, A. Mehta, J.S. Humbert, Challenges present in the development and stabilization of a micro quadrotor helicopter, in Autonomous Weapons Summit and GNC Challenges for Miniature Autonomous Systems Workshop (2010)Google Scholar
  35. 35.
    A.I. Mourikis, N. Trawny, S.I. Roumeliotis, A.E. Johnson, A. Ansar, L. Matthies, Vision-aided inertial navigation for spacecraft entry, descent, and landing. IEEE Trans. Robot. 25(2), 264–280 (2009)CrossRefGoogle Scholar
  36. 36.
    R.M. Murray, M. Rathinam, W. Sluis, Differential flatness of mechanical control systems: a catalog of prototype systems, in Proceedings of the 1995 ASME International Congress and Exposition (1995)Google Scholar
  37. 37.
    G. Niemeyer, J.J. Slotine, Performance in adaptive manipulator control, in Proceedings of the IEEE Conference on Decision and Control, vol. 2 (1988), pp. 1585–1591. doi: 10.1109/CDC.1988.194595
  38. 38.
    M.J.V. Nieuwstadt, R.M. Murray, Real-time trajectory generation for differentially flat systems. Int. J. Robust Nonlinear Control 8, 995–1020 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    R. Ortega, M.W. Spong, Adaptive motion control of rigid robots: a tutorial, in Proceedings of the IEEE Conference on Decision and Control, vol. 2 (1988), pp. 1575–1584. doi: 10.1109/CDC.1988.194594
  40. 40.
    D. Pines, F. Bohorquez, Challenges facing future micro air vehicle development. AIAA J. Aircr. 43(2), 290–305 (2006)CrossRefGoogle Scholar
  41. 41.
    P. Pounds, A. Dollar, Hovering stability of helicopters with elastic constraints, in ASME Dynamic Systems and Control Conference (2010)Google Scholar
  42. 42.
    O. Purwin, R. D’Andrea, Performing aggressive maneuvers using iterative learning control, in Proceedings of the IEEE International Conference on Robotics and Automation (Kobe, Japan, 2009), pp. 1731–1736Google Scholar
  43. 43.
    J. Ratti, G. Vachtsevanos, Towards energy efficiency in micro hovering air vehicles, in IEEE Aerospace Conference (Big Sky, MT, 2011), pp. 1–8Google Scholar
  44. 44.
    S. Saripalli, J.F. Montgomery, G.S. Sukhatme, Vision-based autonomous landing of an un- manned aerial vehicle, in Proceedings of the IEEE International Conference on Robotics and Automation (Washington, DC, 2002), pp. 2799–2804Google Scholar
  45. 45.
    K.W. Sevcik, N. Kuntz, P.Y. Oh, Exploring the effect of obscurants on safe landing zone indentification. J. Intell. Robot. Syst. 57(1–4), 281–295 (2010)CrossRefzbMATHGoogle Scholar
  46. 46.
    C.S. Sharp, O. Shakernia, S.S. Sastry, A vision system for landing an unmanned aerial vehicle, in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2 (Seoul, Korea, 2001), pp. 1720–1727Google Scholar
  47. 47.
    S. Shen, N. Michael, V. Kumar, 3D estimation and control for autonomous flight with constrained computation, in Proceedings of the IEEE International Conference on Robotics and Automation (Shanghai, China, 2011)Google Scholar
  48. 48.
    S. Shen, N. Michael, V. Kumar, Exploration and control for autonomous mapping with aerial robots. Technical report, University of Pennsylvania, 2011Google Scholar
  49. 49.
    R. Tedrake, LQR-Trees: feedback motion planning on sparse randomized trees, in Proceedings of Robotics: Science and Systems (Seattle, WA, 2009)Google Scholar
  50. 50.
    M. Turpin, N. Michael, V. Kumar, Trajectory design and control for aggressive formation flight with quadrotors, in Proceedings of the International Symposium of Robotics Research (Flagstaff, AZ, 2011)Google Scholar
  51. 51.
    U.S. Military Unmanned Aerial Vehicles (UAV) Market Forecast 2010–2015 (2011). http://www.uavmarketresearch.com/
  52. 52.
    J. van der Berg, P. Abbeel, K. Goldberg, LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. Int. J. Robot. Res. 30(7), 895–913 (2011). doi: 10.1177/0278364911406562
  53. 53.
    J. Wen, K. Kreutz-Delgado, The attitude control problem. IEEE Trans. Autom. Control 36(10), 1148–1162 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    L. Whitcomb, A. Rizzi, D. Koditschek, Comparative experiments with a new adaptive controller for robot arms. IEEE Trans. Robot. Autom. 9(1), 59–70 (1993). doi: 10.1109/70.210795 CrossRefGoogle Scholar
  55. 55.
    C.H. Wolowicz, J.S. Bowman, W.P. Gilbert, Similitude requirements and scaling relationships as applied to model testing. Technical report, NASA, 1979Google Scholar
  56. 56.
    J. Yu, A. Jadbabaie, J. Primbs, Y. Huang, Comparison of nonlinear control design techniques on a model of the caltech ducted fan, in IFAC World Congress, IFAC-2c-112 (1999), pp. 53–58Google Scholar
  57. 57.
    S. Zarovy, M. Costello, A. Mehta, A. Flynn, G. Gremillion, D. Miller, B. Ranganathan, J.S. Humbert, P. Samuel, Experimental study of gust effects on micro air vehicles, in AIAA Conference on Atmospheric Flight Mechanics, AIAA-2010–7818 (American Institute of Aeronautics and Astronautics, 2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Mechanical Engineering and Applied Mechanics, GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations