Planning of Pushing Manipulation by a Mobile Robot Considering Cost of Recovery Motion
Takahiro Saito, Yuichi Kobayashi, Tatsuya Naruse
2014
Abstract
This paper presents a planning method of pushing manipulation by a mobile robot. It is sometimes very useful if the robot can take recovery action, namely, re-approaching and re-pushing, when it turns out to be ineffective to keep current pushing motion. The proposed planning framework is based on the idea of mode switching, where three modes; approaching, pushing and re-pushing, are considered. The pushing motion is first built with dynamic programming, which provides value function of the state. Based on the value, planning of re-approaching to the object and re-pushing is conducted using a value iteration algorithm extended to state space with uncertainty. The proposed planning framework was evaluated in simulation, and it was shown that it provides more effective behaviour of the robot by recovery motion at an appropriate timing.
References
- Bertsekas, D. (2005). Dynamic Programming and Optimal Control. Athena Scientific.
- Ghosh, A., Chowdhury, A., Konar, A., and Janarthanan, R. (2012). Multi-robot cooperative box-pushing problem using multi-objective particle swarm optimization technique. In Information and Communication Technologies, pages 272-277.
- Kobayashi, Y. and Hosoe, S. (2010). Planning-space shift motiongeneration : variable-space motion planning toward flexible extension of body schema. In Journal of Intelligent and Robotic Systems, volume 62, pages 467-500.
- Kondo, T. and Ito, K. (2003). A study on designing controller for peg-pushing robot by using reinforcement learning with adaptive state recruitment strategy. In SICE Annual Conference.
- Mas, I. and Kitts, C. (2012). Object manipulation using cooperative mobile multi-robot systems. In Proceedings of the World Congress on Engineering and Computer Science.
- Morimoto, J. and Doya, K. (2001). Acquisition of standup behavior bya real robot using hierarchical rinforcement learning. In Robotics and Autonomous Systems, volume 36(1), pages 37-51.
- Mukai, T., Hirano, S., H.Nakashima, and Kato, Y. (2010). Development of a nursing-care assistant robot riba that can lift a human in its arms. In Intelligent Robots and Systems.
- Nagatani, K., Kiribayashi, S., and Tadokoro, S. (2011). Redesign of rescue mobile robot quince. In Safety, Security, and Rescue Robotics.
- Sekiguchi, T., Kobayashi, Y., Shimizu, A., and Kaneko, T. (2012). Online learning of optimal robot behavior for object manipulation using mode switching. In Proc. of IEEE Int. Symposium on Robotic and Sensors Environments, volume 61-66.
- Sutton, R. S. and G.Barto, A. (1998). Reinforcement Learning. MIT Press.
- Theodorou, E., Buchli, J., and Schaal., S. (2010). Reinforcement learning of motor skills in high dimensions:a path integral approach. In In International Conference on Robotics and Automa-tion.
- Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. The MIT Press.
- Tribelhorn, B., Mudd, C. H., and Dodds, Z. (2007). Evaluating the roomba: A low-cost, ubiquitous platform for robotics research and education. In Robotics and Automation.
- van der Schaft, A. and Schumacher, H. (2000). An introduction to hybrid. In Dynamical Systems.
Paper Citation
in Harvard Style
Saito T., Kobayashi Y. and Naruse T. (2014). Planning of Pushing Manipulation by a Mobile Robot Considering Cost of Recovery Motion . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 322-327. DOI: 10.5220/0005156203220327
in Bibtex Style
@conference{ncta14,
author={Takahiro Saito and Yuichi Kobayashi and Tatsuya Naruse},
title={Planning of Pushing Manipulation by a Mobile Robot Considering Cost of Recovery Motion},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={322-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005156203220327},
isbn={978-989-758-054-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Planning of Pushing Manipulation by a Mobile Robot Considering Cost of Recovery Motion
SN - 978-989-758-054-3
AU - Saito T.
AU - Kobayashi Y.
AU - Naruse T.
PY - 2014
SP - 322
EP - 327
DO - 10.5220/0005156203220327