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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: andreas.bircher@mavt.ethz.ch, ulrich.schwesinger@mavt.ethz.ch, sammy.omari@mavt.ethz.ch, michael.burri@mavt.ethz.ch, rsiegwart@ethz.ch
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: andreas.bircher@mavt.ethz.ch, ulrich.schwesinger@mavt.ethz.ch, sammy.omari@mavt.ethz.ch, michael.burri@mavt.ethz.ch, rsiegwart@ethz.ch
Sammy Omari
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: andreas.bircher@mavt.ethz.ch, ulrich.schwesinger@mavt.ethz.ch, sammy.omari@mavt.ethz.ch, michael.burri@mavt.ethz.ch, rsiegwart@ethz.ch
Michael Burri
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: andreas.bircher@mavt.ethz.ch, ulrich.schwesinger@mavt.ethz.ch, sammy.omari@mavt.ethz.ch, michael.burri@mavt.ethz.ch, rsiegwart@ethz.ch
Roland Siegwart
Affiliation:
Autonomous Systems Lab at ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland E-mails: andreas.bircher@mavt.ethz.ch, ulrich.schwesinger@mavt.ethz.ch, sammy.omari@mavt.ethz.ch, michael.burri@mavt.ethz.ch, rsiegwart@ethz.ch
*
*Corresponding author. E-mail: kalexis@unr.edu
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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 

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