Abstract
Autonomous navigation (i.e., without human intervention) in indoor spaces such as houses and office buildings has many important applications; for instance, in areas affected by building collapse due to natural or artificial disasters. However, it is also a difficult task because any prescribed trajectory can be suddenly interrupted by unexpected obstacles. Arguably, a group of simple autonomous drones driven by swarm intelligence might be more efficient than a sophisticated robot for navigation within such environments. Based on this idea, this work presents a method that applies a powerful swarm intelligence technique called bat algorithm to the autonomous coordinated navigation of a swarm of virtual bots in dynamic indoor environments. Some computational experiments are conducted to test the performance of this approach.
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Acknowledgements
This work has been supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project Ref. #TIN2012-30768, and Toho University.
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Suárez, P., Gálvez, A., Iglesias, A. (2017). Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_19
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