Abstract: Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof. Xin-She Yang in 2010, with remarkable applications in industrial and scientific domains. However, to the best of authors' knowledge, this algorithm has never been applied so far in the context of swarm robotics. With the aim to fill this gap, this paper introduces the first practical implementation of the bat algorithm in swarm robotics. Our implementation is performed at two levels: a physical level, where we design and build a real robotic prototype; and a computational level, where we develop a robotic simulation framework. A very important feature of our implementation is its high specialization: all (physical and logical) components are fully optimized to replicate the most relevant features of the real microbats and the bat algorithm as faithfully as possible. Our implementation has been tested by its application to the problem of finding a target location within unknown static indoor 3D environments. Our experimental results show that the behavioral patterns observed in the real and the simulated robotic swarms are very similar. This makes our robotic swarm implementation an ideal tool to explore the potential and limitations of the bat algorithm for real-world practical applications and their computer simulations.

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