Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-25T02:01:19.842Z Has data issue: false hasContentIssue false
  • English
  • Français

An incremental sampling-based approach to inspection planning: the rapidly exploring random tree of trees

Published online by Cambridge University Press:  11 March 2016

Andreas Bircher ,
Kostas Alexis ,
Ulrich Schwesinger ,
Sammy Omari ,
Michael Burri  and
Roland Siegwart
Andreas Bircher
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Kostas Alexis*
Affiliation:
University of Nevada, Reno, 1664 N. Virginia St., 89557, Reno, NV, US
Ulrich Schwesinger
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Sammy Omari
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Michael Burri
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Roland Siegwart
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

A new algorithm, called rapidly exploring random tree of trees (RRTOT) is proposed, that aims to address the challenge of planning for autonomous structural inspection. Given a representation of a structure, a visibility model of an onboard sensor, an initial robot configuration and constraints, RRTOT computes inspection paths that provide full coverage. Sampling based techniques and a meta-tree structure consisting of multiple RRT* trees are employed to find admissible paths with decreasing cost. Using this approach, RRTOT does not suffer from the limitations of strategies that separate the inspection path planning problem into that of finding the minimum set of observation points and only afterwards compute the best possible path among them. Analysis is provided on the capability of RRTOT to find admissible solutions that, in the limit case, approach the optimal one. The algorithm is evaluated in both simulation and experimental studies. An unmanned rotorcraft equipped with a vision sensor was utilized as the experimental platform and validation of the achieved inspection properties was performed using 3D reconstruction techniques.

Keywords

Inspection path planningCoverage path planningAerial roboticsUnmanned aerial vehicles
Type
Articles
Information
Robotica , Volume 35 , Issue 6 , June 2017 , pp. 1327 - 1340
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Stumm, E., Breitenmoser, A., Pomerleau, F., Pradalier, C. and Siegwart, R., “Tensor-voting-based navigation for robotic inspection of 3d surfaces using lidar point clouds,” 31 (12), 1465–1488 (2012).Google Scholar
2. Burri, M., Nikolic, J., Hurzeler, C., Caprari, G. and Siegwart, R., “Aerial Service Robots for Visual Inspection of Thermal Power Plant Boiler Systems,” Applied Robotics for the Power Industry, 2012 2nd International Conference on Zurich, Switzerland (2012) pp. 70–75.Google Scholar
3. Englot, B. and Hover, F. S., “Three-dimensional coverage planning for an underwater inspection robot,” 32 (9–10), 1048–1073 (2013).Google Scholar
4. Bircher, A., Alexis, K., Burri, M., Oettershagen, P., Omari, S., Mantel, T. and Siegwart, R., “Structural Inspection Path Planning Via Iterative Viewpoint Resampling with Application to Aerial Robotics,” IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA (May 2015) pp. 6423–6430. [Online]. Available: https://github.com/ethz-asl/StructuralInspectionPlanner Google Scholar
5. Bircher, A., Kamel, M., Alexis, K., Burri, M., Oettershagen, P., Omari, S., Mantel, T. and Siegwart, R., “Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots,” Autonomous Robots, Springer US, 1–25 (2015). DOI: 10.1007/s10514-015-9517-1, ISSN: 1573–7527.Google Scholar
6. Galceran, E. and Marc, M. Carreras, “A survey on coverage path planning for robotics,” Robot. Auton. Syst. 61 (12), 1258–1276 (2013).Google Scholar
7. Barraquand, J. and Latombe, J.-C., “Robot motion planning: A distributed representation approach,” Int. J. Robot. Res. 10, 628–649 (1991).Google Scholar
8. Choset, H., “Coverage for robotics–a survey of recent results,” Ann. Math. Artif. Intell. 31 (1–4), 113–126 (2001).Google Scholar
9. Zelinsky, A., Jarvis, R. A., Byrne, J. and Yuta, S., “Planning Paths of Complete Coverage of an Unstructured Environment by a Mobile Robot,” Proceedings of International Conference on Advanced Robotics vol. 13, Tsukuba (1993) pp. 533–538.Google Scholar
10. Urrutia, J., “Art Gallery and Illumination Problems,” In: Handbook of Computational Geometry, Instituto de Mathematicas, Universidad Nacional Autonoma de Mexico: Mexico (2000) pp. 973–1027.CrossRefGoogle Scholar
11. Shmoys, D., Lenstra, J., Kan, A. and Lawler, E., The Traveling Salesman Problem. Lenstra, J. K., Kan, A. R., Lawler, E. L., Shmoys, D. B., editors. The traveling salesman problem: a guided tour of combinatorial optimization. John Wiley & Sons (1985).Google Scholar
12. Englot, B. and Hover, F. S., “Sampling-Based Sweep Planning to Exploit Local Planarity in the Inspection of Complex 3d Structures,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Vilamoura (2012) pp. 4456–4463.Google Scholar
13. Danner, T. and Kavraki, L. E., “Randomized planning for short inspection paths,” IEEE International Conference in Robotics and Automation, 2000, Apr 24, Vol. 2, pp. 971–976, San Francisco, CA, USA.Google Scholar
14. Blaer, P. S. and Allen, P. K., “View planning and automated data acquisition for three-dimensional modeling of complex sites,” J. Field Robot. 26 (11–12), 865–891.CrossRefGoogle Scholar
15. Papadopoulos, G., Kurniawati, H. and Patrikalakis, N., “Asymptotically Optimal Inspection Planning using Systems with Differential Constraints,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (2013) pp. 4126–4133.Google Scholar
16. Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30 (7), 846–894 (2011).Google Scholar
17. Plaku, E., Bekris, K. E., Chen, B. Y., Ladd, A. M. and Kavraki, L., “Sampling-based roadmap of trees for parallel motion planning,” IEEE Trans. Robot. 21 (4), 597–608 (2005).Google Scholar
18. Wang, W., Yan, L., Xu, X. and Yang, S. X., “An Adaptive Roadmap Guided Multi-RRTs Strategy for Single Query Path Planning,” Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Anchorage, AK, USA (2010) pp. 2871–2876.Google Scholar
19. Devaurs, D., Simeon, T., Cortés, J. et al., “A Multi-Tree Extension of the Transition-Based RRT: Application to Ordering-and-Pathfinding Problems in Continuous Cost Spaces,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA (2014) pp. 2991–2996.Google Scholar
20. Hsu, D., Kindel, R., Latombe, J. C. and Rock, S., “Randomized kinodynamic motion planning with moving obstacles,” The International Journal of Robotics Research, 21 (3), 233–255 (2002 March). doi: 10.1177/027836402320556421.Google Scholar
21. Achtelik, M. W., Lynen, S., Chli, M. and Siegwart, R., “Inversion Based Direct Position Control and Trajectory Following for Micro Aerial Vehicles,” IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), Tokyo, JP (2013) pp. 2933–2939.Google Scholar
22. Nikolic, J., Rehder, J., Burri, M., Gohl, P., Leutenegger, S., Furgale, P. T. and Siegwart, R., “A Synchronized Visual-Inertial Sensor System with FPGA Pre-Processing for Accurate Real-Time Slam,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) IEEE, Hong Kong (2014) pp. 431–437.Google Scholar
23. Ascending Technologies GmbH, “http://www.asctec.de/”.Google Scholar
24. Autodesk Inc., “http://www.123dapp.com/catch”.Google Scholar
25. Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C. and Burgard, W., “OctoMap: An efficient probabilistic 3D mapping framework based on octrees,” Auton. Robots 34 (3), 189–206 (2013).Google Scholar

Bircher supplementary material

Bircher supplementary material 1

Download Bircher supplementary material(Video)
Video 9.8 MB