Achieving cohesive and mobile groups using simple sensory feedback

Type

Student thesis: Master's ThesisMaster of Philosophy

Original languageEnglish
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Award date2022
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Abstract

Biological swarms use emergent behaviour to perform complex tasks with minimal resources. Research has shown that local interactions among neighbours leads to emergent behaviour. However, many questions remain unanswered on how groups can conduct successful collective motion under challenging environmental conditions, competing priorities, and in heterogeneous groups. A full understanding of this process may unlock potential solutions for computational, robotic, and biological problems. We propose a novel behaviour selection algorithm that enables agents to stay in coherent groups while navigating challenging environments. We evaluate our model, using a three-dimensional simulator, in obstacle-free and obstacle-filled environments. We measure the model’s performance in two main ways, it’s ability to create single cohesive groups, and it’s ability to create groups that explore efficiently. We find that our model performs, at least, 18% better than similar models from the literature. We find our model is robust to increases in group size, obstacle density, and speed. Our model requires fewer computational complexities than many similar models, and as such, with further development we believe it has potential to be implemented as an effective multi-robotic platform providing efficient collective motion from low-cost individuals