Abstract
This paper presents an integrated method based on a modified PSO algorithm and a grouping strategy for swarm robots to search multiple targets simultaneously. The number of robot groups is determined autonomously according to the actual searched environment and the amount of potential targets. A simulation platform is designed to demonstrate the searching process and to verify the method. Comparisons are performed to show the superior of the studied method. Results show that the proposed method has good adaptability and high success rate in the searching multiple targets.
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Acknowledgements
This research is supported by the Fundamental Research Funds for the Central Universities (No. 2014KJ032, 20153683), by Shanghai Pujiang Program (No. 15PJ1408400) and the Key Basic Research Project of ‘Shanghai Science and Technology Innovation Plan’ (No. 15JC1403300). Meanwhile, this work is also partially supported by the State Key Laboratory of Robotics and Systems (Harbin Institute of Technology) (No. SKLRS-2015-ZD-03), and the National Science Foundation of China (No. 51579053). All these supports are highly appreciated.
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Tang, Q., Yu, F., Ding, L. (2016). A Grouping Method for Multiple Targets Search Using Swarm Robots. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_51
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DOI: https://doi.org/10.1007/978-3-319-41009-8_51
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