Collective behaviour empowers biological species to achieve remarkable swarm level results through distributed actions. For example, social spiders achieve coordinated activities that can lead to plausible outcomes such as preying, mating, or building webs. Developers of simulated swarm systems are increasingly moving away from insinuating individualistic robotic devices, gravitating towards collective swarms to achieve common goals. However, for this to happen, it is imperative to understand the principles that underpin successful simulated coordination of robotic devices in swarms. In this article, we investigate the principles behind artificial swarm systems built on the instinctive behaviours of simulated social spider-like devices (SS-bots). We classify these principles into six interconnected knowledge domains, including (a) environment, (b) SS-bot architecture, (c) SS-bot mission planning, (d) SS-bot communication, (e) SS-bot operators, and (f) metadata and swarm-level data. The key features of each domain are discussed, and an SS-bot ontology is proposed.
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