Abstract
This paper presents a dynamic task allocation algorithm for multiple robots to visit multiple targets. This algorithm is specifically designed for the environment where robots have dissimilar starting and ending locations. And the constraint of balancing the number of targets visited by each robot is considered. More importantly, this paper takes into account the dynamicity of multi-robot system and the obstacles in the environment. This problem is modeled as a constrained MTSP which can not be transformed to TSP and can not be solved by classical Ant Colony System (ACS). The Modified Ant Colony System (MACS) is presented to solve this problem and the unvisited targets are allocated to appropriate robots dynamically. The simulation results show that the output of the proposed algorithm can satisfy the constraints and dynamicity for the problem of multi-robot task allocation.
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References
Stack, J.R., Smith, C.M., Hyland, J.C.: Efficient reacquisition path planning for multiple autonomous underwater robots. In: Ocean 2004 - MTS/IEEE Techno-Ocean 2004 Bridges across the Oceans, pp. 1564–1569 (2004)
Zhong, Y., Gu, G.C., Zhang, R.B.: New way of path planning for underwater robot group. Journal of Harbin Engineering University 24(2), 166–169 (2003)
Xu, Z.Z., Li, Y.P., Feng, X.S.: Constrained Multi-objective Task Assignment for UUVs using Multiple Ant Colonies System. In: The 2008 ISECS International Colloquium on Computing, Communication, Control, and Management, pp. 462–466 (2008)
Fogel, D.B.: A parallel processing approach to a multiple traveling salesman problem using evolutionary programming. In: Proceedings of the fourth annual symposium on parallel processing, pp. 318–326 (1990)
Song, C., Lee, K., Lee, W.D.: Extended simulated annealing for augmented TSP and multi-salesmen TSP. In: Proceedings of the international joint conference on neural networks, vol. 3, pp. 2340–2343 (2003)
Ryan, J.L., Bailey, T.G., Moore, J.T., Carlton, W.B.: Reactive Tabu search in unmanned aerial reconnaissance simulations. In: Proceedings of the 1998 winter simulation conference, vol. 1, pp. 873–879 (1998)
Tang, L., Liu, J., Rong, A., Yang, Z.: A multiple traveling salesman problem model for hot rolling scheduling in Shangai Baoshan Iron & Steel Complex. European Journal of Operational Research 124, 267–282 (2000)
Modares, A., Somhom, S., Enkawa, T.: A self-organizing neural network approach for multiple traveling salesman and robot routing problems. International Transactions in Operational Research 6, 591–606 (1999)
Pan, J.J., Wang, D.W.: An ant colony optimization algorithm for multiple traveling salesman problem. In: Proceedings of the first international conference on innovative computing, information and control (2006)
Kara, I., Bektas, T.: Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 1449–1458 (2006)
Xu, Z.Z., Li, Y.P., Feng, X.S.: A Hierarchical control system for heterogeneous multiple uuv cooperation task. Robot 30(2), 155–159 (2008)
Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
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Xu, Z., Xia, F., Zhang, X. (2009). Multi-Robot Dynamic Task Allocation Using Modified Ant Colony System. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_32
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DOI: https://doi.org/10.1007/978-3-642-05253-8_32
Publisher Name: Springer, Berlin, Heidelberg
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