Abstract
In this work, two aspects of motion planning for object reconstruction are investigated. First, the effect of using a sampling-based optimal motion planning technique to move a mobile manipulator robot with 8 degrees of freedom, during the reconstruction process, in terms of several performance criteria is studied. Based on those criteria, the results of the reconstruction task using rapidly exploring random tree (RRT) approaches are compared, more specifically RRT* smart versus RRT* versus standard RRT. Second, the problem of defining a convenient stopping probabilistic test to terminate the reconstruction process is addressed. Based on our results, it is concluded that the use of a RRT* improves the measured performance criteria compared with a standard RRT. The simulation experiments show that the proposed stopping test is adequate. It stops the reconstruction process when all the portions of object that are possible to be seen have been covered with the field of view of the sensor.
Similar content being viewed by others
References
Chen S, Li Y, Kwok NM (2011) Active vision in robotic systems: a survey of recent developments. Int J Rob Res 30(11):1343–1377
Cieslewski T, Kaufmann E, Scaramuzza D. (2017) Rapid exploration with multi-rotors: a frontier selection method for high speed flight. In: Proc. IEEE/RSJ int. conf. on intelligent robots and systems
Delmerico J, Isler S, Sabzevari R et al (2018) A comparison of volumetric information gain metrics for active 3D object reconstruction. Auton Robots 42:197. https://doi.org/10.1007/s10514-017-9634-0
Hornung A, Wurm KM, Bennewitz M, Stachniss C, Burgard W (2013) Octomap: an efficient probabilistic 3d mapping framework based on octrees. Auton Robots 34(3):189–206
Isler S, Sabzevari R, Delmerico J, Scaramuzza D (2016) An information gain formulation for active volumetric 3D reconstruction. In: Proc. IEEE int. conf. on robotics and automation, pp 3477–3484
Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Rob Res 30(7):846–894
Kavraki LE, Svestka P, Latombe J-C, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Rob Autom 12(4):566–580
Khalfaoui S, Seulin R, Fougerolle YD, Fofi D (2013) An efficient method for fully automatic 3d digitization of unknown objects. Comput Ind 64(9):1152–1160
Kriegel S, Rink C, Bodenmller T, Suppa M (2013) Efficient next-best-scan planning for autonomous 3d surface reconstruction of unknown objects. J Real-Time Image Process 1–21
LaValle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Rob Res 20(5):378–400
Lozano Albalate MT, Devy M, Sanchiz Marto JM (2002) Perception planning for an exploration task of a 3D environment. In: Proc. int. conf. on pattern recognition, pp 704–707
Nasir J, Islam F, Malik U, Ayaz Y, Hasan O, Khan M, Muhammad MS (2013) RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Rob Syst 10(7)
Noreen I, Khan A, Habib Z (2016) A comparison of RRT, RRT* and RRT*—smart path planning algorithms. Int J Comput Sci Netw Secur 16(10)
Potthast C, Sukhatme G (2014) A probabilistic framework for next best view estimation in a cluttered environment. J Vis Commun Image Represent 25(1):148–164
Sarmiento A, Murrieta-Cid R, Hutchinson S (2005) A sample-based convex cover for rapidly finding an object in a 3-D environment. In: Proc. IEEE int. conf. on robotics and automation, pp 3497–3502
Scott WR, Roth G, Rivest JF (2003) View planning for automated three-dimensional object reconstruction and inspection. ACM Comput Surv (CSUR) 35(1):64–96
Song S, Jo S (2017) Online inspection path planning for autonomous 3d modeling using a micro-aerial vehicle. In: IEEE international conference on robotics and automation, pp 6217–6224, 29 May–3 June, Singapore, Singapore
Song S, Jo S (2018) Surface-based exploration for autonomous 3D modeling. In: IEEE international conference on robotics and automation, Brisbane, Australia
Srinivasan Ramanagopal M, Nguyen APV, Ny J Le (2018) A motion planning strategy for the active vision-based mapping of ground-level structures. IEEE Trans Autom Sci Eng 15(1):356–368
Torabi L, Gupta K (2012) An autonomous 9-dof mobile-manipulator system for in situ 3d object modeling. In: Proc. IEEE/RSJ int. conf. on intelligent robots and systems, pp 4540–4541
Torabi L, Gupta K (2012) An autonomous six-dof eye-in-hand system for in situ 3d object modeling. Int J Rob Res 31(1):82–100
Vasquez-Gomez JI, Sucar LE, Murrieta-Cid R (2014) View planning for 3D object reconstruction with a mobile manipulator robot. In: Proc. IEEE/RSJ int. conf. on intelligent robots and systems, pp 4227–4233
Vasquez-Gomez JI, Sucar LE, Murrieta-Cid R (2017) View/state planning for three-dimensional object reconstruction under uncertainty. Auton Robots 41(1):89–109
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was partially funded by CONACYT project 220796.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (mp4 8390 KB)
Rights and permissions
About this article
Cite this article
Yervilla-Herrera, H., Vasquez-Gomez, J.I., Murrieta-Cid, R. et al. Optimal motion planning and stopping test for 3-D object reconstruction. Intel Serv Robotics 12, 103–123 (2019). https://doi.org/10.1007/s11370-018-0264-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11370-018-0264-y