A Proposed Architecture for Autonomous Operations in Backhoe Machines

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

In this work is developed an architecture which consists of four main components: perception system, tasks planning, motion planner, and control systems that allow autonomous operations in backhoe machines. In the first part is described the architecture of control system. A set of techniques for collision mapping of the scene is described and implemented. Moreover, a motion planning system based on Learning from Demonstration using Dynamic Movement Primitives as control policy is proposed, which allows backhoe machines to perform operations in autonomous manner. A statement of reasons is presented, wherein we justified the implementation of such motion system versus planners like \(\text {A}^*\), Probabilistic RoadMap (PRM), Rapidly-exploring Random Tree (RRT), etc. In addition, we present the performance of the architecture in a simulation environment.

Keywords

Autonomous backhoe machine Learning from demonstration  Dynamic movement primitives Perception system 

References

  1. 1.
    Buss, S.R.: Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods. Tech. rep., University of California, San Diego (2009)Google Scholar
  2. 2.
    Hoffmann, H., Pastor, P., Schaal, S.: Dynamic movement primitives for movement generation motivated by convergent force fields in frog. In: International Sympsium on Adaptive Motion of Animals and Machines (AMAM). pp. 1–2 (2008), http://www-clmc.usc.edu/publications//H/HeikoHoffmann_AMAM_2008.pdf
  3. 3.
    Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: in Advances in Neural Information Processing Systems. pp. 1523–1530. MIT Press (2003)Google Scholar
  4. 4.
    Jovanovic, V.T., Sendzimir, T.: On the metrics and coordinate system induced sensitivity in computational kinematics. Tech. rep., Department of Mechanical Engineering. University of Connecticut (1996)Google Scholar
  5. 5.
    Konolige, K.: Small vision systems: Hardware and implementation. In: International Symposium of Robotics Research (1997)Google Scholar
  6. 6.
    Mastalli, C., Cappelletto, J., Acua, R., Terrones, A., Fernndez-Lpez, G.: An Imitation Learning Approach For Truck Loading Operations in Backhoe Machines, chap. 104, pp. 827–830. World Scientific, Baltimore (2012), http://www.worldscientific.com/doi/abs/10.1142/9789814415958_0104
  7. 7.
    Mastalli, C., Ralev, D., Certad, N., Fernndez-Lpez, G.: Asymptotic stability method for pid controller tuning in a backhoe machine. In: ASME Dynamic Systems and Control Conference (2013), https://asme-dscd.papercept.net/conferences/scripts/abstract.pl?ConfID=7&Number=3884
  8. 8.
    Park, D.h., Hoffmann, H., Pastor, P., Schaal, S.: Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields. Humanoids 2008–8th IEEE-RAS International Conference on Humanoid Robots pp. 91–98 (2008), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4755937
  9. 9.
    Pastor, P., Hoffmann, H., Asfour, T., Schaal, S.: Learning and generalization of motor skills by learning from demonstration. In: 2009 IEEE International Conference on Robotics and Automation (2009)Google Scholar
  10. 10.
    Saeedi, P., Lawrence, P.D., Lowe, D.G., Jacobsen, P., Kusalovic, D., Ardron, K., Sorensen, P.H.: An autonomous excavator with vision-based track-slippage control. IEEE Transactions on Control Systems Technology 13(1), 67–84 (2005), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1372547
  11. 11.
    Singh, S., Simmons, R.: Task planning for robotic excavation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 1284–1291 (1992)Google Scholar
  12. 12.
    Singh, S.: Developing plans for robotic excavators. In: Proceedings of the ASCE Conference on Robotics for Challenging Environments. pp. 88–96 (1994)Google Scholar
  13. 13.
    Singh, S.: Synthesis of tactical plans for robotic excavation. Ph.D. thesis, Carnie Mellon University (1995)Google Scholar
  14. 14.
    Stentz, A., Bares, J., Singh, S., Rowe, P.: A robotic excavator for autonomous truck loading. In: Proceedings 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications. vol. 3, pp. 1885–1893. IEEE (1998)Google Scholar
  15. 15.
    Wurm, K., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In: Proceeding of the ICRA 2010 workshop on best practice in 3D perception and modeling for mobile manipulation (2010)Google Scholar
  16. 16.
    Yamamoto, H., Moteki, M., Shao, H., Ootuki, T.: Basic technology toward autonomous hydraulic excavator. 26th International Symposium on Automation and Robotics in Construction (ISARC 2009) pp. 288–295 (2009), http://www.irbnet.de/daten/iconda/CIB14849.pdf
  17. 17.
    Ye, G., Alterovitz, R.: Demonstration-guided motion planning. In. In Proceedings International Symposium on Robotics Research (ISRR) (2011), http://robotics.cs.unc.edu/publications.html

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Istituto Italiano di TecnologiaDepartment of Advanced RoboticsGenoaItaly
  2. 2.Simón Bolívar UniversityMechatronic Research GroupBarutaVenezuela

Personalised recommendations