**Page description appears here**

“3D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA”

Authors: Knut B. Kaldestad, Geir Hovland and David A. Anisi,
Affiliation: University of Agder and ABB
Reference: 2012, Vol 33, No 4, pp. 123-130.

     Valid XHTML 1.0 Strict


Keywords: Collision Detection, Industrial Robot, GPU, CUDA

Abstract: This paper presents adaptable methods for achieving fast collision detection using the GPU and Nvidia CUDA together with Octrees. Earlier related work have focused on serial methods, while this paper presents a parallel solution which shows that there is a great increase in time if the number of operations is large. Two different models of the environment and the industrial robot are presented, the first is Octrees at different resolutions, the second is a point cloud representation. The relative merits of the two different world model representations are shown. In particular, the experimental results show the potential of adapting the resolution of the robot and environment models to the task at hand.

PDF PDF (1194 Kb)        DOI: 10.4173/mic.2012.4.1



DOI forward links to this article:
  [1] Gregory F. Rossano, Carlos Martinez, Mikael Hedelind, Steve Murphy and Thomas A. Fuhlbrigge (2013), doi:10.1109/ISR.2013.6695710


References:
[1] NVIDIA CUDA C (2006). Programming Guide, .
[2] Anisi, D., Gunnar, J., Lillehagen, T., Skourup, C. (2010). Robot Automation in Oil and Gas Facilities: Indoor and Onsite Demonstrations, In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems.IROS. Taipei, Taiwan, pp. 4729--4734 doi:10.1109/IROS.2010.5649281
[3] Anisi, D., Persson, E., Heyer, C., Skourup, C. (2011). Real-World Demonstration of Sensor-Based Robotic Automation in Oil and& Gas Facilities, In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems.IROS. San Francisco, California doi:10.1109/IROS.2011.6094440
[4] von Dziegielewski, A., Hemmer, M., Schomer, E. (2012). High quality conservative surface mesh generation for swept volumes, In Robotics and Automation.ICRA, IEEE Intl. Conf. pp. 764--769 doi:10.1109/ICRA.2012.6224921
[5] Elseberg, J., Magnenat, S., Siegwart, R., Nüchter, A. (2012). Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration, Journal of Software Engineering for Robotics (JOSER). 3(1):2--12.
[6] Faverjon, B. (1984). Obstacle avoidance using an octree in the configuration space of a manipulator, In Robotics and Automation. Proc. IEEE Intl. Conf. on, volume1. pages 504--512 doi:10.1109/ROBOT.1984.1087218
[7] Gregg, C. Hazelwood, K. (2011). Where is the data?, In Performance Analysis of Systems and Software.ISPASS, IEEE Intl. Symposium on. pp. 134 -144 doi:10.1109/ISPASS.2011.5762730
[8] Hayward, V. (1986). Fast collision detection scheme by recursive decomposition of a manipulator workspace, In Robotics and Automation. Proc. IEEE Intl. Conf. on, volume3. pp. 1044--1049 doi:10.1109/ROBOT.1986.1087620
[9] Henrich, D., Wurll, C., Wörn, H. (1998). 6 dof path planning in dynamic environments-a parallel online approach, In Robotics and Automation, Proc. IEEE Intl. Conf., volume1. pp. 330--335 vol.1 doi:10.1109/ROBOT.1998.676417
[10] Kaldestad, K., Hovland, G., Anisi, D. (2012). CAD-Based Training of an Expert System and a Hidden Markov Model for Obstacle Detection in an Industrial Robot Environment, In IFAC Intl. Conf. on Automatic Control in Offshore Oil and Gas Production. Trondheim, Norway doi:10.3182/20120531-2-NO-4020.00036
[11] Kaldestad, K., Hovland, G., Anisi, D. (2012). Obstacle Detection in an Unstructured Industrial Robotic System: Comparison of Hidden Markov Model and Expert System, In 10th IFAC Intl. Symposiums on Robot Control. Dubrovnik, Croatia doi:10.3182/20120905-3-HR-2030.00059
[12] Ramisa, A., Alenya, G., Moreno-Noguer, F., Torras, C. (2012). Using depth and appearance features for informed robot grasping of highly wrinkled clothes, In Robotics and Automation.ICRA, IEEE Intl. Conf. on. pp. 1703--1708 doi:10.1109/ICRA.2012.6225045
[13] Ross, P. (2008). Why cpu frequency stalled, Spectrum, IEEE, 4.4:72 doi:10.1109/MSPEC.2008.4476447
[14] Rusu, R.B. Cousins, S. (2011). 3D is here: Point Cloud Library (PCL), In IEEE Intel. Conf. on Robotics and Automation (ICRA). Shanghai, China doi:10.1109/ICRA.2011.5980567


BibTeX:
@article{MIC-2012-4-1,
  title={{3D Sensor-Based Obstacle Detection Comparing Octrees and Point clouds Using CUDA}},
  author={Kaldestad, Knut B. and Hovland, Geir and Anisi, David A.},
  journal={Modeling, Identification and Control},
  volume={33},
  number={4},
  pages={123--130},
  year={2012},
  doi={10.4173/mic.2012.4.1},
  publisher={Norwegian Society of Automatic Control}
};

News

May 2016: MIC reaches 2000 DOI Forward Links. The first 1000 took 34 years, the next 1000 took 2.5 years.


July 2015: MIC's new impact factor is now 0.778. The number of papers published in 2014 was 21 compared to 15 in 2013, which partially explains the small decrease in impact factor.


Aug 2014: For the 3rd year in a row MIC's impact factor increases. It is now 0.826.


Dec 2013: New database-driven web-design enabling extended statistics. Article number 500 is published and MIC reaches 1000 DOI Forward Links.


Jan 2012: Follow MIC on your smartphone by using the RSS feed.

Smartphone


July 2011: MIC passes 1000 ISI Web of Science citations.


Mar 2010: MIC is now indexed by DOAJ and has received the Sparc Seal seal for open access journals.


Dec 2009: A MIC group is created at LinkedIn and Twitter.


Oct 2009: MIC is now fully updated in ISI Web of Knowledge.