ArticleFrontiers in robotics and AI2022
A study of error diversity in robotic swarms for task partitioning in foraging tasks.
Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Heterogeneous foraging swarms can be better.Frontiers in robotics and AI · 2024Article
- A blockchain-based information market to incentivise cooperation in swarms of self-interested robots.Scientific reports · 2023Article
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5 authors.
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Abstract
Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality, this is not the case, and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviors. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving the expected swarm behavior.
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