Akademik Veri Yönetim Sistemi
16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020, Shanghai, Çin, 16 - 18 Ekim 2020, cilt.350, ss.18-36
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among different behaviours such as foraging and flocking performed by social animals; aggregation behaviour is often considered as the most basic and fundamental one. Aggregation behaviour has been studied in different domains for over a decade. In most of these studies, the settings are over-simplified that are quite far from reality. In this paper, we investigate cue-based aggregation behaviour using BEECLUST in a complex environment having two cues –one being the local optimum and the other being the global optimum– with an obstacle between the two cues. The robotic validation of the BEECLUST strategy in a complex environment is the main motivation of this paper. We measured the aggregation size on both cues with and without the obstacle varying the number of robots. The simulations were performed on a custom open-source simulation platform, Bee-Ground, using MONA robots. The results showed that the aggregation behaviour with BEECLUST strategy was able to overcome a certain degree of environmental complexities revealing the robustness of the method. We also verified these results using our stock-flow model.