Robot Actions Planning Algorithms in Multi-Agent System

Article Preview

Abstract:

In this paper, the basic information about multi-agent systems is given. The authors propose robot control algorithms for managing virtual autonomous warehouses, where the task performed by the robots is transportation between specific locations in the warehouse and a number of distribution points. Algorithms control the work of a single robot, including the cooperation with other robots in the environment as well as collisions avoidance. Different routing algorithms are evaluated through simulations focusing on service time and waiting time of executing tasks. The impact of the proposed algorithms on energy consumption was also checked, since this is important for the working time between battery charges.

You might also be interested in these eBooks

Info:

Periodical:

Solid State Phenomena (Volume 223)

Pages:

221-230

Citation:

Online since:

November 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] A. Marchewka, Z. Lutowski B. Marciniak, M. Śrutek, S. Bujnowski, Control Algorithms in Multi-agent environment, Image Processing & Communications, Vol. 17, No. 3, (2012) pp.47-62.

DOI: 10.2478/v10248-012-0021-3

Google Scholar

[2] I. Beker, V. Jevtić, D. Dobrilović, Shortest-path algorithms as a tools for inner transportation optimization, International Journal of Industrial Engineering and Management (IJIEM), Vol. 3 No 1, (2012) pp.39-45.

Google Scholar

[3] T. Marciniak, Z. Lutowski, S. Bujnowski, D. Boroński, P. Czajka, Dual-Band Experimental System For subsurface Cracks Testing, Materials Science Forum Vol. 726 - Fatigue Failure and Fracture Mechanics, Trans Tech Publications, (2012) pp.222-226.

DOI: 10.4028/www.scientific.net/msf.726.222

Google Scholar

[4] B. Marciniak, T. Marciniak, Z. Lutowski, S. Bujnowski, Usage of digital image corelation IN analysis of cracking, Image Processing and Communications. Vol. 17, No 3, (2012) pp.21-29.

DOI: 10.2478/v10248-012-0019-x

Google Scholar

[5] L. Peng, H.Y. Liu, Decision-making and simulation in multi-agent robot system based on PSO-neural network, International Conference on Robotics and Biomimetics, (2007) pp.1763-1768.

DOI: 10.1109/robio.2007.4522432

Google Scholar

[6] S. Sung-Oog, L. Jung-Oog, B. Doo-Kwon, A Mobile Agent-based Multi-Robot Design Method for High-Assurance, 10th IEEE High Assurance Systems Engineering Symposium,. HASE '07, (2007) pp.389-390.

DOI: 10.1109/hase.2007.30

Google Scholar

[7] P. Guerrero, J.R. del Solar, M. Romero, L. Herrera, An integrated multi-agent decision making framework for robot soccer, 6th Latin American Robotics Symposium (LARS), (2009) pp.1-6.

DOI: 10.1109/lars.2009.5418321

Google Scholar

[8] B. Boryna, B. Dubalski, P. Kiedrowski, A. Zabłudowski: Errors Nature in Indoors Low Power 433 MHz Wireless Network, Image Processing and Communications Challenges 2, Springer Verlag, Advances in Intelligent and Soft Computing 84, (2010).

DOI: 10.1007/978-3-642-16295-4_43

Google Scholar

[9] P. Kiedrowski, B. Dubalski, B. Boryna, J. Lis: Bezprzewodowa sieć telemetryczna zrealizowana w oparciu o układy nadawczo-odbiorcze krótkiego zasięgu, Rynek Energii nr 1 (92), (2011) p.108–114.

Google Scholar

[10] M.A. Khamis, W. Gomaa, Enhanced multiagent multi-objective reinforcement learning for urban traffic light control, International Conference on Machine Learning and Applications (ICMLA), (2012) pp.586-591.

DOI: 10.1109/icmla.2012.108

Google Scholar

[11] L. Chun-Jie, Building of searching behavior analysis models on multi-agent intelligent agent technology, Symposium on Electrical & Electronics Engineering, (2012) pp.574-577.

DOI: 10.1109/eeesym.2012.6258722

Google Scholar