Smooth Path Planning with Pedestrian Avoidance for Wheeled Robots

Yumiko Suzuki; Simon Thompson; Satoshi Kagami

doi:10.20965/jrm.2010.p0021

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JRM Vol.22 No.1 pp. 21-27

doi: 10.20965/jrm.2010.p0021

(2010)

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Smooth Path Planning with Pedestrian Avoidance for Wheeled Robots

Yumiko Suzuki^,, Simon Thompson^, and Satoshi Kagami^,**

^*Digital Human Research Center, National Institute of Advanced Science and Technology

^**Graduate School of Information Science, Nara Institute of Science and Technology

Received:

December 9, 2008

Accepted:

September 23, 2009

Published:

February 20, 2010

Keywords:

smooth path planning, wheeled robots, pedestrian avoidance, steering set

Abstract

In studying smooth robot path planning with predesigned steering sets including three trajectory types, path planning with a steering set was used to generate trajectories with smooth directional changes. To put path planning to practical use in dynamic environments, robots must be more quickly motion and efficiently, without, for example, endangering pedestrians. Assuming that the trajectories of moving obstacles are predictable, smooth path planning worked in the presence ofmoving obstacles. We defined new path evaluation method suitable for wheeled robots, evaluating our planner experimentally in an office, confirmed the efficiency of our planning.

Cite this article as:

Y. Suzuki, S. Thompson, and S. Kagami, “Smooth Path Planning with Pedestrian Avoidance for Wheeled Robots,” J. Robot. Mechatron., Vol.22 No.1, pp. 21-27, 2010.

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References

[1] N. Nilsson, “Principles of Artificial Intelligence,” Tioga Publishing Company, 1980.
[2] S. M. LaValle, “Planning Algorithms,” chapter 5, pp. 185-186, Cambridge University Press, 2006.
[3] S. Thompson and S. Kagami, “Smooth trajectory planning with obstacle avoidance for car-like mobile robots,” The 23rd Annual Conf. of the Robotics Society of Japan, 2005.
[4] D. Ferguson and A. Stentz, “The field d* algorithm for improved path planning and replanning in uniform and non-uniform cost environments. Robotics Institute,” CMU, Vol.CMU-RITR-, pp. 05-19, June 2005.
[5] L. A. Zadeh, “Fuzzy sets,” Information and control, Vol.8, pp. 338-353, 1965.
[6] P. Garnier and T. Fraichard, “A fuzzy motion controller for a carlike vehicle,” In In Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 1996.
[7] Y. Suzuki, S. Kagami, and J. J. Kuffner, “Path planning with steering set for car-like robots and finding an effective set,” In ROBIO, 2006 IEEE Int. Conf., 2006.
[8] A. Stentz, “Optimal and efficient path planning for partially-known environments,” In Proc. IEEE Int. Conf. on Robotics and Automation, May 1994.
[9] A. Stentz, “The focussed d* algorithm for real-time replanning,” In Proc. of Int. Joint Conf. on Artificial Intelligence, August 1995.
[10] A. Stentz and M. Herbert, “A complete navigation system for goal acquisition in unknown environments,” Autonomous Robots, Vol.2, No.2, pp. 127-145, 1995.
[11] S. Koenig and M. Likhachev, “D* lite,” In Proc. of the National Conference of Artificial Intelligence (AAAI), pp. 476-483, 2002.
[12] T. Horiuchi, M. Kanehara, S. Kagami, and Y. Ehara, “A probabilistic walk path model focused on foot landing points and human step measurement system,” In SMC,2006 IEEE Int. Conf., 2006.
[13] J. H. Lee, T. Tsubouchi, K. Yamamoto, and S. Egawa, “People tracking using a robot in motion with laser range finder,” In Proc. of the 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, October 2006.
[14] T. Tsubouchi and S. Arimoto, “Behavior of a mobile robot navigated by an iterated forecast and planning scheme in the presence of multiple moving obstacles,” Robotics and Automation, Vol.3, pp. 2470-2475, May 1994.
[15] J. A. Reeds and L. A. Shepp, “Optimal path for a car that goes both forwards and backwards,” Vol.145, No.2, 1990.
[16] J. C. Latombe, “Robot Motion Planning,” Kluwer Academic Publishers, 1991.
[17] J. J. Kuffner, “Efficient optimal search of euclidean-cost grids and lattices,” In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, September 2004.
[18] T. Horiuchi, S. Thompson, S. Kagami, and Y. Ehara, “Pedestrian tracking from a mobile robot using a laser range finder,” In Proc. of 2007 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC2007), pp. 931-936, Montreal, Canada, 10, 2007.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] N. Nilsson, “Principles of Artificial Intelligence,” Tioga Publishing Company, 1980.

[2] [2] S. M. LaValle, “Planning Algorithms,” chapter 5, pp. 185-186, Cambridge University Press, 2006.

[3] [3] S. Thompson and S. Kagami, “Smooth trajectory planning with obstacle avoidance for car-like mobile robots,” The 23rd Annual Conf. of the Robotics Society of Japan, 2005.

[4] [4] D. Ferguson and A. Stentz, “The field d* algorithm for improved path planning and replanning in uniform and non-uniform cost environments. Robotics Institute,” CMU, Vol.CMU-RITR-, pp. 05-19, June 2005.

[5] [5] L. A. Zadeh, “Fuzzy sets,” Information and control, Vol.8, pp. 338-353, 1965.

[6] [6] P. Garnier and T. Fraichard, “A fuzzy motion controller for a carlike vehicle,” In In Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, 1996.

[7] [7] Y. Suzuki, S. Kagami, and J. J. Kuffner, “Path planning with steering set for car-like robots and finding an effective set,” In ROBIO, 2006 IEEE Int. Conf., 2006.

[8] [8] A. Stentz, “Optimal and efficient path planning for partially-known environments,” In Proc. IEEE Int. Conf. on Robotics and Automation, May 1994.

[9] [9] A. Stentz, “The focussed d* algorithm for real-time replanning,” In Proc. of Int. Joint Conf. on Artificial Intelligence, August 1995.

[10] [10] A. Stentz and M. Herbert, “A complete navigation system for goal acquisition in unknown environments,” Autonomous Robots, Vol.2, No.2, pp. 127-145, 1995.

[11] [11] S. Koenig and M. Likhachev, “D* lite,” In Proc. of the National Conference of Artificial Intelligence (AAAI), pp. 476-483, 2002.

[12] [12] T. Horiuchi, M. Kanehara, S. Kagami, and Y. Ehara, “A probabilistic walk path model focused on foot landing points and human step measurement system,” In SMC,2006 IEEE Int. Conf., 2006.

[13] [13] J. H. Lee, T. Tsubouchi, K. Yamamoto, and S. Egawa, “People tracking using a robot in motion with laser range finder,” In Proc. of the 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, October 2006.

[14] [14] T. Tsubouchi and S. Arimoto, “Behavior of a mobile robot navigated by an iterated forecast and planning scheme in the presence of multiple moving obstacles,” Robotics and Automation, Vol.3, pp. 2470-2475, May 1994.

[15] [15] J. A. Reeds and L. A. Shepp, “Optimal path for a car that goes both forwards and backwards,” Vol.145, No.2, 1990.

[16] [16] J. C. Latombe, “Robot Motion Planning,” Kluwer Academic Publishers, 1991.

[17] [17] J. J. Kuffner, “Efficient optimal search of euclidean-cost grids and lattices,” In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, September 2004.

[18] [18] T. Horiuchi, S. Thompson, S. Kagami, and Y. Ehara, “Pedestrian tracking from a mobile robot using a laser range finder,” In Proc. of 2007 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC2007), pp. 931-936, Montreal, Canada, 10, 2007.

Smooth Path Planning with Pedestrian Avoidance for Wheeled Robots

Yumiko Suzuki*,**, Simon Thompson**, and Satoshi Kagami*,**

Yumiko Suzuki^,, Simon Thompson^, and Satoshi Kagami^,**