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Programming Khepera II robot for autonomous navigation and exploration using the hybrid architecture
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Source ACM Southeast Regional Conference archive
Proceedings of the 47th Annual Southeast Regional Conference table of contents
Clemson, South Carolina
SESSION: Robotics table of contents
Article No. 31  
Year of Publication: 2009
ISBN:978-1-60558-421-8
Authors
Cen Li  Middle Tennessee State University, Murfreesboro, TN
Bryan Bodkin  Middle Tennessee State University, Murfreesboro, TN
James Lancaster  Middle Tennessee State University, Murfreesboro, TN
Publisher
ACM  New York, NY, USA
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ABSTRACT

This project investigated the feasibility of programming the Khepera II robot for autonomous navigation and exploration using the hybrid robot architecture. At the deliberative layer of the system, the D* Lite algorithm was implemented to find the shortest path between a starting and a destination state, and to perform efficient re-planning during exploration. At the reactive layer, instructions along the shortest path are executed one instruction at a time. Each instruction is executed by following a behavior until a terminator state is reached. Robot exploration is activated when an unexpected world situation is detected along the navigation path. This information is fed to the deliberative layer where the map is updated, and the shortest path was recomputed. A separate visualization module was built to monitor the progress of the navigation and exploration progress. The tool provides a real time feed for the state of robot navigation progress.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Antonelli, G., Arrichiello, F., and Chiaverini, S., "Experiments of Formation Control with Multi-Robot Systems using the Null-Space-Based Behavioral Control", in Proceedings of the 14th Mediterranean Conference on Control and Automation, pp. 1--6, 2006.
 
2
Antonelli, G., Arrichiello, F., and Chiaverini, S., "An Experimental Study of the Entrapment/Escorting Mission for a Multi-Robot System", IEEE Robotics and Automation Magazine. Special Issues on Design, Control, and Applications of Real-World Multi-Robot Systems, vol. 15, n. 1, pp. 22--29, 2008.
3
 
4
Arieo, A. and Gerstner, W., "Hipppocampal spatial model for state space representation in robotic reinforcement learning", in Proceedings of the fifth European Workshop on Reinforcement learning, 2001, p. 1--3, CKI, Utrecht University, 2001.
5
6
 
7
Koenig, S. and Likhachev, M., "Improved fast replanning for robot navigation in unknown terrain", in the proceedings of the IEEE International conference on robotics and automation, pp. 968--975, Washington, DC, May 11--15<sup>th</sup>, 2002.
8
 
9
 
10
S. Nolfi, D. Floreano, Evolutionary Robotics, MIT Press, 2000.
 
11
 
12
Stentz, A., "Optimal and efficient path planning for partially-known environments", in proceedings of the IEEE International conference on robotics and automation, pp. 3310--3317. 1994.
 
13
Stentz, A. "The focused D* algorithm for real-time replanning". In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1652--1659, 1995.

Collaborative Colleagues:
Cen Li: colleagues
Bryan Bodkin: colleagues
James Lancaster: colleagues