Autonomous Robots

, Volume 40, Issue 4, pp 631–655 | Cite as

Aerial robotic contact-based inspection: planning and control

  • Kostas Alexis
  • Georgios Darivianakis
  • Michael Burri
  • Roland Siegwart
Article

Abstract

The challenge of aerial robotic contact-based inspection is the driving motivation of this paper. The problem is approached on both levels of control and path-planning by introducing algorithms and control laws that ensure optimal inspection through contact and controlled aerial robotic physical interaction. Regarding the flight and physical interaction stabilization, a hybrid model predictive control framework is proposed, based on which a typical quadrotor becomes capable of stable and active interaction, accurate trajectory tracking on environmental surfaces as well as force control. Convex optimization techniques enabled the explicit computation of such a controller which accounts for the dynamics in free-flight as well as during physical interaction, ensures the global stability of the hybrid system and provides optimal responses while respecting the physical limitations of the vehicle. Further augmentation of this scheme, allowed the incorporation of a last-resort obstacle avoidance mechanism at the control level. Relying on such a control law, a contact-based inspection planner was developed which computes the optimal route within a given set of inspection points while avoiding any obstacles or other no-fly zones on the environmental surface. Extensive experimental studies that included complex “aerial-writing” tasks, interaction with non-planar and textured surfaces, execution of multiple inspection operations and obstacle avoidance maneuvers, indicate the efficiency of the proposed methods and the potential capabilities of aerial robotic inspection through contact.

Keywords

Aerial robots Physical interaction control Hybrid MPC 

References

  1. AEROWORKS, Collaborative Aerial Robotic Workers. http://www.aeroworks2020.eu/.
  2. AIRobots, Innovative Aerial Service Robots for Remote Inspection by Contact, http://airobots.ing.unibo.it/.
  3. Alexis, K. (2013). ASLquad System Identification. ETH Zurich, http://www.asl.ethz.ch/people/alexisk/personal/ASLquadID, Tech. Rep., June 2013.
  4. Alexis, K., Huerzeler, C., & Siegwart, R. (2013). Hybrid modeling and control of a coaxial unmanned rotorcraft interacting with its environment through contact. 2013 International conference on robotics and automation (ICRA) (pp. 5397–5404). Karlsruhe.Google Scholar
  5. Alexis, K., Nikolakopoulos, G., & Tzes, A. (2010). Design and experimental verification of a constrained finite time optimal control scheme for the attitude control of a quadrotor helicopter subject to wind gusts. 2010 International conference on robotics and automation (pp. 1636–1641). Alaska: Anchorage.Google Scholar
  6. ARCAS, Aerial Robotics Collaborative Assembly System. http://www.arcas-project.eu/
  7. Ascending Technologies GmbH, http://www.asctec.de/.
  8. Baotic, M. (2005). Optimal control of piecewise affine systems. Ph.D. dissertation, Swiss Federal Institute of Technology Zurich.Google Scholar
  9. Beardmore, R. (2010). Indicative friction coefficients. http://www.roymech.co.uk/.
  10. Bellens, S., De Schutter, J., & Bruyninckx, H. (2012). A hybrid pose/wrench control framework for quadrotor helicopters. IEEE International conference on robotics and automation (ICRA) (pp. 2269–2274).Google Scholar
  11. Bemporad, A. (2002). An efficient technique for translating mixed logical dynamical systems into piecewise affine systems. In Proceedings of the 41st IEEE conference on decision and control, 2002 (vol. 2, pp. 1970–1975).Google Scholar
  12. Bemporad, A. (2004). Efficient conversion of mixed logical dynamical systems into an equivalent piecewise affine form. IEEE transactions on automatic control (vol.4(5), pp. 832 – 838).Google Scholar
  13. Bemporad, A. (2003). Modeling, control, and reachability analysis of discrete-time hybrid systems. Sienna: University of Sienna.Google Scholar
  14. Bircher, A., & Alexis, K., Structural inspection planner open–source toolbox, https://github.com/ethz-asl/StructuralInspectionPlanner.
  15. Bircher, A., Alexis, K., Burri, M., Oettershagen, P., Omari, S., Mantel, T., & Siegwart, R. (2015). Structural inspection path planning via iterative viewpoint resampling with application to aerial robotics. IEEE international conference on robotics and automation (ICRA). Seattle, Washington, May 26–30, 2015.Google Scholar
  16. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefMATHGoogle Scholar
  17. Burri, M., Nikolic, J., Hurzeler, C., Caprari, G., & Siegwart, R. (2012). Aerial service robots for visual inspection of thermal power plant boiler systems. In 2012 2nd International conference on applied robotics for the power industry.Google Scholar
  18. Chang, P., & Liu, S. (2003). Recent research in nondestructive evaluation of civil infrastructures. Journal of Materials in Civil Engineering, 15(3), 298–304.CrossRefMathSciNetGoogle Scholar
  19. Darivianakis, G., Alexis, K., Burri, M., & Siegwart, R. (2014). Hybrid predictive control for aerial robotic physical interaction towards inspection operations. In 2014 IEEE international conference on robotics and automation (ICRA) pp. 53–58.Google Scholar
  20. Dryanovski, I., Valenti, R. G., & Xiao, J. (2013). An open-source navigation system for micro aerial vehicles. Autonomous Robots, 34(3), 177–188.CrossRefGoogle Scholar
  21. Fumagalli, M., Naldi, R., Macchelli, A., Forte, F., Keemink, A. Q. L., Stramigioli, S., et al. (2014). Developing an aerial manipulator prototype: Physical interaction with the environment. IEEE Robotics Automation Magazine, 21, 41–50.CrossRefGoogle Scholar
  22. Gioioso, G., Franchi, A., Salvietti, G., Scheggi, S., & Prattichizzo, D. (2014). The flying hand: A formation of UAVs for cooperative aerial tele-manipulation. Proceedings of IEEE international conference on robotics and automation (pp. 4335–4341). Hong Kong.Google Scholar
  23. Goebel, R., Sanfelice, R. G., & Teel, A. R. (2012). Hybrid dynamical systems: Modeling, stability, and robustness. Princeton, NJ: Princeton University Press.Google Scholar
  24. Helsgaun, K. (2009). General k-opt submoves for the linkernighan tsp heuristic. Mathematical Programming Computation, vol. 1(2–3):119–163. [Online]. Available: doi:10.1007/s12532-009-0004-6.
  25. Helsgaun, K. (2000). An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research, 126(1), 106–130.CrossRefMathSciNetMATHGoogle Scholar
  26. Herceg, M., Kvasnica, M., Jones, C., & Morari, M. (2013). Multi-Parametric Toolbox 3.0. In Proceedings of the European control conference (pp. 502–510), Zürich, July 17–19.Google Scholar
  27. Huber, F., Kondak, K., Krieger, K., Sommer, D., Schwarzbach, M., Laiacker, M., Kossyk, I., Parusel, S., Haddadin, S., & Albu-Schaffer, A. (2013). First analysis and experiments in aerial manipulation using fully actuated redundant robot arm. IEEE/RSJ International conference on intelligent robots and systems (IROS) (pp. 3452–3457), Japan.Google Scholar
  28. Huerzeler, C. (2013). Modeling and design of unmanned rotorcraft systems for contact based inspection. Ph.D. dissertation, Swiss Federal Institute of Technology Zurich.Google Scholar
  29. Huerzeler, C., Alexis, K., & Siegwart, R. (2013). Configurable real-time simulation suite for coaxial rotor uavs. 2013 International conference on robotics and automation (ICRA) (pp. 309–316). Karlsruhe.Google Scholar
  30. Jiang, G., & Voyles, R. (2013). Hexrotor UAV platform enabling dextrous interaction with structures-flight test. 2013 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–6).Google Scholar
  31. Johnson, D., & McGeoch, L. (1995). The traveling salesman problem: A case study in local optimization. Heuristics for the Traveling Salesman Problem.Google Scholar
  32. Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. arXiv preprint  arXiv:1005.0416.
  33. Kouramas, K., & Dua, V., Hybrid parametric model-based control (pp. 25–48). Wiley-VCH Verlag GmbH and Co. KGaA.Google Scholar
  34. Kvasnica, M., Rauova, I., & Fikar, M. (2010). Automatic code generation for real-time implementation of model predictive control. In 2010 IEEE International symposium on computer-aided control system design (CACSD) (pp. 993–998).Google Scholar
  35. Kvasnica, M. (2009). Real-time model predictive control via multi-parametric programming: Theory and tools. Saarbruecken: VDM Verlag.Google Scholar
  36. Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21(2), 498–516.CrossRefMathSciNetMATHGoogle Scholar
  37. Ljung, L. (1999). System identification: Theory for the user (2nd ed.). Upper Saddle River, NJ: Prentice Hall Inc.Google Scholar
  38. Loefberg, J. (2004). Yalmip : A toolbox for modeling and optimization in MATLAB. In Proceedings of the CACSD Conference, Taipei. [Online]. Available: http://users.isy.liu.se/johanl/yalmip.
  39. Manubens, M., Devaurs, D., Ros, L., & Corté, J. (2013). Motion planning for 6-D manipulation with aerial towed-cable systems. Robotics: Science and systems 2013 (RSS).Google Scholar
  40. Marconi, L., & Naldi, R. (2012). Control of aerial robots: Hybrid force and position feedback for a ducted fan. IEEE Control Systems, 32, 43–65.CrossRefMathSciNetGoogle Scholar
  41. Marconi, L., Naldi, R., & Gentili, L. (2011). Modelling and control of a flying robot interacting with the environment. Automatica, 47(12), 2571–2583.CrossRefMathSciNetMATHGoogle Scholar
  42. Mellinger, D., Lindsey, Q., Shomin, M., & Kumar, V. (2011). Design, modeling, estimation and control for aerial grasping and manipulation. IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2668–2673).Google Scholar
  43. Mellinger, D., Shomin, M., Michael, N., & Kumar, V. (2013). Cooperative grasping and transport using multiple quadrotors. In Distributed autonomous robotic systems, ser. Springer tracts in advanced robotics (Vol. 83, pp. 545–558). Berlin: Springer.Google Scholar
  44. Metni, N., & Hamel, T. (2007). A UAV for bridge inspection: Visual servoing control law with orientation limits. Automation in Construction, 17(1), 3–10.CrossRefGoogle Scholar
  45. Montserrat Manubens, L. R., Devaurs, D., & Cortes, J. (2013). Motion planning for 6-d manipulation with aerial towed-cable systems. In 2013 Robotics science and systems (RSS), Berlin.Google Scholar
  46. Orsag, M., Korpela, C., Pekala, M., & Oh, P. (2013). Stability control in aerial manipulation. In American control conference (ACC) (pp. 5581–5586).Google Scholar
  47. Orsag, M., Korpela, C., Stjepan, S., & Oh, P. (2014). Hybrid adaptive control for aerial manipulation. Journal of Intelligent and Robotic Systems, 73, 693–707.CrossRefGoogle Scholar
  48. Papachristos, C., & Tzes, A. (2013). Large object pushing via a direct longitudinally-actuated unmanned tri-tiltrotor. In Mediterranean conference on control automation (MED) (pp. 173–178).Google Scholar
  49. Parra-Vega, V., Sanchez, A., Izaguire, C., Garcia, O., & Ruiz-Sanchez, F. (2013). Toward aerial grasping and manipulation with multiple UAVs. The Journal of Intelligent and Robotic Systems, 70, 575–593.CrossRefGoogle Scholar
  50. Popov, V. L. (2010). Contact mechanics and friction. Berlin: Springer.CrossRefMATHGoogle Scholar
  51. Potočnik, B., Mušič, G., & Zupančič, B. (2004). A new technique for translating discrete hybrid automata into piecewise affine systems. Mathematical and Computer Modelling, 10(1), 41–57.MATHGoogle Scholar
  52. Pounds, P. E. I., & Dollar, A. M. (2014). Stability of Helicopters in Compliant Contact Under PD-PID Control. IEEE Transactions on Robotics (Vol. 30).Google Scholar
  53. Pounds, P. E. I., Bersak, D., & Dollar, A. (2011). The yale aerial manipulator: Grasping in flight. In 2011 IEEE international conference on robotics and automation (ICRA) (pp. 2974–2975).Google Scholar
  54. Rens, K., Wipf, T., & Klaiber, F. (1997). Review of nondestructive evaluation techniques of civil infrastructure. Journal of Performance of Constructed Facilities, 11(4), 152–160.CrossRefGoogle Scholar
  55. Ruggiero, F., Cacace, J., Sadeghian, H., Lippiello, V. (2014). Impedance control of VToL UAVs with a momentum-based external generalized forces estimator. IEEE International conference on robotics and automation (ICRA) (pp. 2093–2099), May 31–June 7.Google Scholar
  56. Shmoys, D., Lenstra, J., Kan, A., & Lawler, E. (1985). The traveling salesman problem. Chichester: Wiley.MATHGoogle Scholar
  57. Sreenath, K., & Kumar, V. (2013). Dynamics, control and planning for cooperative manipulation of payloads suspended by cables from multiple quadrotor robots. In 2013 Robotics science and systems (RSS), Berlin.Google Scholar
  58. Srikanth, M. B., Soto, A., Annaswamy, A., Lavretsky, E., & Slotine, J.-J. (2011). Controlled manipulation with multiple quadrotors. AIAA Conference on guidance, navigation and control.Google Scholar
  59. Torrisi, F. D., Bemporad, A. (2002) HYSDEL—HYbrid System DEscription Language, The Hybrid Systems Group, IFA–ETHZ, http://control.ee.ethz.ch/~hybrid/hysdel/.
  60. Valette, S., & Chassery, J.-M. (2004). Approximated centroidal voronoi diagrams for uniform polygonal mesh coarsening. Computer Graphics Forum, 23(3), 381–389.CrossRefGoogle Scholar
  61. Yuskel, B., Secchi, C., Bulthof, H., & Franchi, A. (2014). A nonlinear force observer for quadrotors and application to physical interactive tasks. IEEE/ASME international conference on advanced intelligent mechatronics (AIM) (pp. 433–440).Google Scholar
  62. Yuskel, B., Secchi, C., Bulthoff, H. H., & Franchi, A. (2014). A nonlinear force observer for quadrotors and application to physical interactive tasks. IEEE/ASME international conference on advanced intelligent mechatronics (AIM) (pp. 433-440).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Kostas Alexis
    • 1
  • Georgios Darivianakis
    • 2
  • Michael Burri
    • 1
  • Roland Siegwart
    • 1
  1. 1.ETH ZurichZurichSwitzerland
  2. 2.ETH ZurichZurichSwitzerland

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