Research Article
Investigation of Cue-Based Aggregation Behaviour in Complex Environments
@INPROCEEDINGS{10.1007/978-3-030-67540-0_2, author={Shiyi Wang and Ali E. Turgut and Thomas Schmickl and Barry Lennox and Farshad Arvin}, title={Investigation of Cue-Based Aggregation Behaviour in Complex Environments}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Swarm robotics Aggregation BEECLUST}, doi={10.1007/978-3-030-67540-0_2} }- Shiyi Wang
Ali E. Turgut
Thomas Schmickl
Barry Lennox
Farshad Arvin
Year: 2021
Investigation of Cue-Based Aggregation Behaviour in Complex Environments
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_2
Abstract
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, usingMONArobots. 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.
