Planning Practical Paths for Tentacle Robots

Jing Yang, Robert Codd-Downey, Patrick Dymond, Junquan Xu, Michael Jenkin

Abstract

Robots with many degrees of freedom with one fixed end are known as tentacle robots due to their similarity to the tentacles found on squid and octopus. Tentacle robots offer advantages over traditional robots in many scenarios due to their enhanced flexibility and reachability. Planning practical paths for these devices is challenging due to their high degrees of freedom (DOFs). Sampling-based path planners are a commonly used approach for high DOF planning problems but the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints during the planning process. This paper describes a general sample adjustment method for tentacle robots, which adjusts the randomly generated nodes within their local neighborhood to satisfy soft constraints required by the problem. The approach is demonstrated on a planar tentacle robot composed of ten Robotis Dynamixel AX-12 servos.

References

  1. Bayazit, O. B. (2003). Solving Motion Planning Problems by Iterative Relaxation of Constraints. PhD thesis, Texas A&M University.
  2. Bohlin, R. and Kavraki, L. E. (2000). Path planning using Lazy PRM. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), volume 1, pages 521-528, San Fransisco, CA, USA. IEEE Press, IEEE Press.
  3. Bruce, J. and Veloso, M. (2005). Real-Time Multi-Robot Motion Planning with Safe Dynamics. In Multi-Robot Systems: From Swarms to Intelligent Automata, volume 3.
  4. Buckinham, R. and Graham, A. (2011). Safire - a robotic inspection system for candu feeders. In Proceedings International Conference on CANDU Maintenance.
  5. Canny, J. F. (1988). The Complexity of Robot Motion Planning. MIT Press, Cambridge, MA, USA.
  6. Chirikjian, G. S. and Burdick, J. W. (1990). An obstacle avoidance algorithm for hyper-redundant manipulators. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), volume 1, pages 625-631.
  7. Choset, H. and Henning, W. (1999). A follow-the-leader approach to serpentine robot motion planning. ASCE Journal of Aerospace Engineering.
  8. Garber, M. and Lin, M. C. (2002). Constraint-based motion planning using Voronoi diagrams. In Proceedings International Workshop on Algorithmic Foundations of Robotics (WAFR).
  9. Gayle, R., Redon, S., Sud, A., Lin, M., and Manocha, D. (2007). Efficient motion planning of highly articulated chains using physics-based sampling. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), pages 3319-3326.
  10. Geraerts, R. (2006). Sampling-based Motion Planning: Analysis and Path Quality. PhD thesis, Utrecht University.
  11. Geraerts, R. and Overmars, M. (2005). On improving the clearance for robots in high-dimensional configuration spaces. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 679-684.
  12. Geraerts, R. and Overmars, M. H. (2007). Creating highquality paths for motion planning. International Journal of Robotics Research, 26(8):845-863.
  13. Gerevini, A. and Long, D. (2005). Plan constraints and preferences in pddl3 - the language of the fifth international planning competition. Technical report, University of Brescia.
  14. Hill, B. and Tesar, D. (1997). Design of Mechanical Properties for Serial Manipulators. PhD thesis, University of Texas at Austin.
  15. Hsu, D. (2000). Randomized Single-query Motion Planning in Expansive Spaces. PhD thesis, Stanford University.
  16. Karaman, S. and Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7):846-894.
  17. Kavraki, L. E., Latombe, J.-C., Motwani, R., and Raghavan, P. (1998). Randomized query processing in robot path planning. Journal of Computer and System Sciences, 57(1):50-60.
  18. Kavraki, L. E., Svestka, P., Latombe, J.-C., and Overmars, M. (1996). Probabilistic roadmaps for path planning in high dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4):566- 580.
  19. Khalil, W. and Dombre, E. (2002). Modeling, Identification and Control of Robots. Hermes Penton Ltd.
  20. Kim, J., Pearce, R. A., and Amato, N. M. (2003). Extracting optimal paths from roadmaps for motion planning. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), volume 2, pages 2424-2429.
  21. Kobilarov, M. and Sukhatme, G. S. (2005). Near timeoptimal constrained trajectory planning on outdoor terrain. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), pages 1833- 1840.
  22. Latombe, J.-C. (1991). Robot Motion Planning. Kluwer.
  23. LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press, Cambridge, U.K. Available at http://planning.cs.uiuc.edu.
  24. LaValle, S. M. and Kuffner, J. J. (2000). Rapidly-exploring random trees: Progress and prospects. In Proceedings International Workshop on Algorithmic Foundations of Robotics (WAFR).
  25. Manseur, R. (2006). Robot Modeling and Kinematics. Da Vinci Engineering Press.
  26. Nielsen, C. and Kavraki, L. E. (2000). A two-level fuzzy prm for manipulation planning. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), volume 3, pages 1716-1722. IEEE Press, IEEE Press.
  27. Nieuwenhuisen, D. and Overmars, M. H. (2004). Useful cycles in probabilistic roadmap graphs. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), volume 1, pages 446-452.
  28. Raveh, B., Enosh, A., and Halperin, D. (2011). A little more, a lot better: Improving path quality by a path merging algorithm. IEEE Transactions on Robotics, 27(2):365-371.
  29. Reif, J. H. (1979). Complexity of the mover's problem and generalizations. In Proceedings Annual Symposium on Foundations of Computer Science (SFCS), pages 421-427.
  30. Rollinson, D. and Choset, H. (2011). Virtual chassis for snake robots. In Proceedings IEEE International Conference of Intelligent Robot and Systems (IROS), pages 221-226.
  31. Song, G., Miller, S., and Amato, N. M. (2001). Customizing prm roadmaps at query time. In Proceedings IEEE International Conference on Robotics & Automation (ICRA), pages 1500-1505.
  32. Transeth, A. a., Pettersen, K. y., and Liljebäck, P. (2009). A survey on snake robot modeling and locomotion. Robotica, 27(7):999-1015.
  33. Tsianos, K. I., Sucan, I. A., and Kavraki, L. E. (2007). Sampling-based robot motion planning: Towards realistic applications. Computer Science Review, 1:2-11.
  34. Wein, R., van den Berg, J. P., and Halperin, D. (2005). The visibility-voronoi complex and its applications. In Proceedings Annual Symposium on Computational Geometry (SCG), pages 63-72, New York, NY, USA. ACM.
  35. Zghal, H. and Dubey, R. V. ad Euler, J. A. (1990). Collision avoidance of a multiple degree of freedom redundant manipulator operating through a window. In Proceedings IEEE American Control Conference, pages 2306-2312.
Download


Paper Citation


in Harvard Style

Yang J., Codd-Downey R., Dymond P., Xu J. and Jenkin M. (2013). Planning Practical Paths for Tentacle Robots . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8565-38-9, pages 128-137. DOI: 10.5220/0004263501280137


in Bibtex Style

@conference{icaart13,
author={Jing Yang and Robert Codd-Downey and Patrick Dymond and Junquan Xu and Michael Jenkin},
title={Planning Practical Paths for Tentacle Robots},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2013},
pages={128-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004263501280137},
isbn={978-989-8565-38-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Planning Practical Paths for Tentacle Robots
SN - 978-989-8565-38-9
AU - Yang J.
AU - Codd-Downey R.
AU - Dymond P.
AU - Xu J.
AU - Jenkin M.
PY - 2013
SP - 128
EP - 137
DO - 10.5220/0004263501280137