Abstract
The multi-robot task allocation problem is a fundamental problem in robotics research area. The problem roughly consists of finding an optimal allocation of tasks among several robots to reduce the mission cost to a minimum. As mentioned in Chap. 6, extensive research has been conducted in the area for answering the following question: Which robot should execute which task? In this chapter, we design different solutions to solve the MRTA problem. We propose four different approaches: an improved distributed market-based approach (IDMB), a clustering market-based approach (CM-MTSP), a fuzzy logic-based approach (FL-MTSP), and Move-and-Improve approach. These approaches must define how tasks are assigned to the robots. The IDBM, CM-MTSP, and Move-and-Improve approaches are based on the use of an auction process where bids are used to evaluate the assignment. The FL-MTSP is based on the use of the fuzzy logic algebra to combine objectives to be optimized.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Trigui, Sahar, Anis Koubaa, Omar Cheikhrouhou, Habib Youssef, Hachemi Bennaceur, Mohamed-Foued Sriti, and Yasir Javed. 2014. A distributed market-based algorithm for the multi-robot assignment problem. Procedia Computer Science, 32(Supplement C): 1108–1114. The 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014), the 4th International Conference on Sustainable Energy Information Technology (SEIT-2014).
Trigui, Sahar, Anis Koubâa, Omar Cheikhrouhou, Basit Qureshi, and Habib Youssef. 2016. A clustering market-based approach for multi-robot emergency response applications. In 2016 international conference on autonomous robot systems and competitions (ICARSC), 137–143. IEEE.
Trigui, Sahar, Omar Cheikhrouhou, Anis Koubaa, Uthman Baroudi, and Habib Youssef. 2016. Fl-mtsp: A fuzzy logic approach to solve the multi-objective multiple traveling salesman problem for multi-robot systems. Soft Computing: 1–12.
Kuhn, W., and Harold. 1955. The hungarian method for the assignment problem. Naval Research Logistics (NRL) 2 (1–2): 83–97.
Bernardine Dias, M., Robert Zlot, Nidhi Kalra, and Anthony Stentz. 2006. Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE 94 (7): 1257–1270.
Golmie, Nada, Yves Saintillan, and David H Su. 1999. A review of contention resolution algorithms for IEEE 802.14 networks. IEEE Communications Surveys 2 (1):2–12.
Chan, Zeke S.H., Lesley Collins, and N. Kasabov. 2006. An efficient greedy k-means algorithm for global gene trajectory clustering. Expert Systems with Applications 30 (1): 137–141.
Zadeh, L.A. 1965. Fuzzy sets. Information and Control 8 (3): 338–353.
Zadeh, Lotfi Asker. 1975. The concept of a linguistic variable and its application to approximate reasoning – I. Information Sciences 8 (3): 199–249.
Kirk, Joseph. 2011. Traveling-salesman-problem-genetic-algorithm. http://www.mathworks.com/matlabcentral/fileexchange/13680-traveling-salesman-problem-genetic-algorithm.
Mamdani, Ebrahim H., and Sedrak Assilian. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7 (1): 1–13.
Takagi, Tomohiro, and Michio Sugeno. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 1: 116–132.
Webots simulation scenarios. 2014. http://www.iroboapp.org/index.php?title=Videos.
Lin, Shen. 1973. An effective heuristic algorithm for the traveling-salesman problem. Operations Research 21 (2): 498–516.
Braun, Heinrich. 1991. On solving travelling salesman problems by genetic algorithms. In Parallel problem solving from nature, 129–133. Berlin: Springer.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Koubaa, A. et al. (2018). Different Approaches to Solve the MRTA Problem. In: Robot Path Planning and Cooperation. Studies in Computational Intelligence, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-77042-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-77042-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77040-6
Online ISBN: 978-3-319-77042-0
eBook Packages: EngineeringEngineering (R0)