{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T12:20:33Z","timestamp":1778502033173,"version":"3.51.4"},"reference-count":25,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2016,3,11]],"date-time":"2016-03-11T00:00:00Z","timestamp":1457654400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2017,6]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>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<jats:bold>3D<\/jats:bold>reconstruction techniques.<\/jats:p>","DOI":"10.1017\/s0263574716000084","type":"journal-article","created":{"date-parts":[[2016,3,11]],"date-time":"2016-03-11T08:06:09Z","timestamp":1457683569000},"page":"1327-1340","source":"Crossref","is-referenced-by-count":35,"title":["An incremental sampling-based approach to inspection planning: the rapidly exploring random tree of trees"],"prefix":"10.1017","volume":"35","author":[{"given":"Andreas","family":"Bircher","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kostas","family":"Alexis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ulrich","family":"Schwesinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sammy","family":"Omari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Burri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Siegwart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2016,3,11]]},"reference":[{"key":"S0263574716000084_ref16","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"S0263574716000084_ref15","doi-asserted-by":"crossref","unstructured":"G. Papadopoulos , H. Kurniawati and N. Patrikalakis , \u201cAsymptotically Optimal Inspection Planning using Systems with Differential Constraints,\u201d Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (2013) pp. 4126\u20134133.","DOI":"10.1109\/ICRA.2013.6631159"},{"key":"S0263574716000084_ref14","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20318"},{"key":"S0263574716000084_ref18","doi-asserted-by":"crossref","unstructured":"W. Wang , L. Yan , X. Xu and S. X. Yang , \u201cAn Adaptive Roadmap Guided Multi-RRTs Strategy for Single Query Path Planning,\u201d Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Anchorage, AK, USA (2010) pp. 2871\u20132876.","DOI":"10.1109\/ROBOT.2010.5509529"},{"key":"S0263574716000084_ref21","doi-asserted-by":"crossref","unstructured":"M. W. Achtelik , S. Lynen , M. Chli and R. 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Siegwart , \u201cA Synchronized Visual-Inertial Sensor System with FPGA Pre-Processing for Accurate Real-Time Slam,\u201d Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) IEEE, Hong Kong (2014) pp. 431\u2013437.","DOI":"10.1109\/ICRA.2014.6906892"},{"key":"S0263574716000084_ref1","first-page":"1465","volume":"31","author":"Stumm","year":"2012","journal-title":"Tensor-voting-based navigation for robotic inspection of 3d surfaces using lidar point clouds"},{"key":"S0263574716000084_ref3","first-page":"1048","volume":"32","author":"Englot","year":"2013","journal-title":"Three-dimensional coverage planning for an underwater inspection robot"},{"key":"S0263574716000084_ref19","unstructured":"D. Devaurs , T. Simeon , J. Cort\u00e9s et al., \u201cA Multi-Tree Extension of the Transition-Based RRT: Application to Ordering-and-Pathfinding Problems in Continuous Cost Spaces,\u201d Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA (2014) pp. 2991\u20132996."},{"key":"S0263574716000084_ref10","doi-asserted-by":"publisher","DOI":"10.1016\/B978-044482537-7\/50023-1"},{"key":"S0263574716000084_ref4","doi-asserted-by":"crossref","unstructured":"A. Bircher , K. Alexis , M. Burri , P. Oettershagen , S. Omari , T. Mantel and R. Siegwart , \u201cStructural Inspection Path Planning Via Iterative Viewpoint Resampling with Application to Aerial Robotics,\u201d IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA (May 2015) pp. 6423\u20136430. [Online]. 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