Advertisement

Autonomous Robots

, Volume 40, Issue 6, pp 1059–1078 | Cite as

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

  • Andreas Bircher
  • Mina Kamel
  • Kostas AlexisEmail author
  • Michael Burri
  • Philipp Oettershagen
  • Sammy Omari
  • Thomas Mantel
  • Roland Siegwart
Article

Abstract

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.

Keywords

Coverage planning Aerial robots Autonomous inspection 

Notes

Acknowledgments

This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement No.644128, AEROWORKS.

References

  1. Acar, E. U., Choset, H., & Lee, J. Y. (2006). Sensor-based coverage with extended range detectors. IEEE Transactions on Robotics, 22(1), 189–198.CrossRefGoogle Scholar
  2. Alexis, K., Nikolakopoulos, G., & Tzes, A. (2012). Model predictive quadrotor control: Attitude, altitude and position experimental studies. Control Theory & Applications, IET, 6(12), 1812–1827.MathSciNetCrossRefGoogle Scholar
  3. Atkar, P., Conner, D. C., Greenfield, A., Choset, H., & Rizzi, A. A. (2004). Uniform coverage of simple surfaces embedded in R 3 for auto-body painting. In Sixth workshop on the algorithmic foundations of robotics, Utrecht/Zeist, The Netherlands.Google Scholar
  4. Autopilot, P. (2015). http://pixhawk.org/.
  5. Bircher, A., & Alexis, K. (2015) Structural inspection planner code release (Online). https://github.com/ethz-asl/StructuralInspectionPlanner.
  6. Bircher, A., Alexis, K., Burri, M., Kamel, M., Oettershagen, P., Omari, S., Mantel, T., & Siegwart, R. (2015a) Structural inspection planner dataset release (Online). https://github.com/ethz-asl/StructuralInspectionPlanner/wiki/Example-Results.
  7. Bircher, A., Alexis, K., Burri, M., Oettershagen, P., Omari, S., Mantel, T., & Siegwart, R. (2015b). Structural inspection path planning via iterative viewpoint resampling with application to aerial robotics. In IEEE international conference on robotics and automation (ICRA) (pp. 6423–6430).Google Scholar
  8. Boon, K., & Lovelace, D. C. (2014). The domestic use of unmanned aerial vehicles. Oxford: Oxford University Press.Google Scholar
  9. Burri, M., Nikolic, J., Hurzeler, C., Caprari, G., & Siegwart, R. (2012). Aerial service robots for visual inspection of thermal power plant boiler systems. In 2nd International conference on applied robotics for the power industry (CARPI) (pp. 70–75).Google Scholar
  10. Camacho, E. F., & Bordons, C. (2003). Model predictive control. Berlin: Springer.zbMATHGoogle Scholar
  11. Choset, H., & Pignon, P. (1998). Coverage path planning: The boustrophedon cellular decomposition. In Field and service robotics (pp. 203–209) Springer.Google Scholar
  12. Dantzig, G., Fulkerson, R., & Johnson, S. (1954). Solution of a large-scale traveling-salesman problem. Journal of the Operations Research Society of America, 2(4), 393–410.MathSciNetCrossRefGoogle Scholar
  13. Domahidi, A. (October 2012). FORCES: Fast optimization for real-time control on embedded systems. http://forces.ethz.ch.
  14. Ferreau, H., Kirches, C., Potschka, A., Bock, H., & Diehl, M. (2014). qpOASES: A parametric active-set algorithm for quadratic programming. Mathematical Programming Computation, 6(4), 327–363.MathSciNetCrossRefzbMATHGoogle Scholar
  15. Gabriely, Y., & Rimon, E. (2002). Spiral-stc: An on-line coverage algorithm of grid environments by a mobile robot. In Proceedings of IEEE international conference on robotics and automation, ICRA’02. (vol. 1, pp. 954–960). IEEEGoogle Scholar
  16. Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258–1276.CrossRefGoogle Scholar
  17. González-Baños, H. (2001). A randomized art-gallery algorithm for sensor placement. In Proceedings of the seventeenth annual symposium on Computational geometry (pp. 232–240). ACM.Google Scholar
  18. Helsgaun, K. (2000). An effective implementation of the lin-kernighan traveling salesman heuristic. European Journal of Operational Research, 126(1), 106–130.MathSciNetCrossRefzbMATHGoogle Scholar
  19. Hert, S., Tiwari, S., & Lumelsky, V. (1996). A terrain-covering algorithm for an auv. In Underwater Robots (pp. 17–45) Springer.Google Scholar
  20. Hover, F. S., Eustice, R. M., Kim, A., Englot, B., Johannsson, H., Kaess, M., et al. (2012). Advanced perception, navigation and planning for autonomous in-water ship hull inspection. The International Journal of Robotics Research, 31(12), 1445–1464.CrossRefGoogle Scholar
  21. ICARUS: Unmanned search and rescue. http://www.fp7-icarus.eu/.
  22. Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30(7), 846–894.CrossRefzbMATHGoogle Scholar
  23. Kroll, A., Baetz, W., & Peretzki, D. (May 2009). On autonomous detection of pressured air and gas leaks using passive ir-thermography for mobile robot application. In International conference on robotics and automation, 2009. ICRA ’09. IEEE (pp. 921–926).Google Scholar
  24. Leutenegger, S. (2014). Unmanned solar airplanes: Design and algorithms for efficient and robust autonomous operation. Ph.D. Dissertation, ETH Zurich.Google Scholar
  25. Leutenegger, S., Melzer, A., Alexis, K., & Siegwart, R. (2014). Robust state estimation for small unmanned airplanes. In IEEE Multiconference on systems and control (MSC), Antibes, France.Google Scholar
  26. Leutenegger, S., Melzer, A., Alexis, K., & Siegwart, R. (2014). Robust state estimation for small unmanned airplanes. In IEEE multi-conference on systems and control.Google Scholar
  27. Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations research, 21(2), 498–516.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Luo, C., Yang, S. X., Stacey, D. A., & Jofriet, J. C. (2002). A solution to vicinity problem of obstacles in complete coverage path planning. In Proceedings of IEEE international conference on robotics and automation, ICRA’02 (vol. 1, pp. 612–617). IEEE.Google Scholar
  29. 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
  30. Nikolic, J., Rehder, J., Burri, M., Gohl, P., Leutenegger, S., Furgale, P. T., & Siegwart, R. Y. (2014). A Synchronized Visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM. In IEEE International conference on robotics and automation (ICRA).Google Scholar
  31. Noth, A. (2008). Design of solar powered airplanes for continuous flight. Ph.D. dissertation, ETH Zurich.Google Scholar
  32. Oettershagen, P., Melzer, A., Leutenegger, S., Alexis, K., & Siegwart, R. (2014). Explicit model predictive control and \({\cal L}_1\)-navigation strategies for fixed-wing UAV path tracking. In 22nd Mediterranean conference on control & automation (MED).Google Scholar
  33. Oettershagen, P., Melzer, A., Mantel, T., Rudin, K., Lotz, R., Siebenmann, D., Leutenegger, S., Alexis, K., & Siegwart, R. (May 2015). A solar-powered hand-launchable uav for low-altitude multi-day continuous flight. In IEEE International conference on robotics and automation (ICRA) (accepted).Google Scholar
  34. O’rourke, J. (1987). Art gallery theorems and algorithms (Vol. 57). Oxford: Oxford University Press.zbMATHGoogle Scholar
  35. Papadopoulos, G., Kurniawati, H., & Patrikalakis, N. M. (2013). Asymptotically optimal inspection planning using systems with differential constraints. In IEEE International conference on robotics and automation (ICRA) (pp. 4126–4133). IEEE.Google Scholar
  36. Park, S., Deyst, J., & How, J. P. (2004). A new nonlinear guidance logic for trajectory tracking. In AIAA guidance, navigation, and control conference and exhibit (pp. 16–19).Google Scholar
  37. RotorS: An MAV gazebo simulator. https://github.com/ethz-asl/rotors_simulator.’
  38. Skybotix, A. G. http://www.skybotix.com/.
  39. Stevens, Brian L., & Lewis, Frank L. (1992). Aircraft control and simulation. Hoboken: Wiley.Google Scholar
  40. Zelinsky, A., Jarvis, R. A., Byrne, J., & Yuta, S. (1993). Planning paths of complete coverage of an unstructured environment by a mobile robot. Proceedings of International Conference on Advanced Robotics, 13, 533–538.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Andreas Bircher
    • 1
  • Mina Kamel
    • 1
  • Kostas Alexis
    • 1
    • 2
    Email author
  • Michael Burri
    • 1
  • Philipp Oettershagen
    • 1
  • Sammy Omari
    • 1
  • Thomas Mantel
    • 1
  • Roland Siegwart
    • 1
  1. 1.ETH ZurichZurichSwitzerland
  2. 2.University of NevadaRenoUSA

Personalised recommendations