Buchanan Edgar, Alden Kieran, Pomfret Andrew, Timmis Jon, Tyrrell Andy M
School of Physics, Engineering and Technology, University of York, York, United Kingdom.
School of Computer Science, University of Sunderland, Sunderland, United Kingdom.
Front Robot AI. 2023 Jan 4;9:904341. doi: 10.3389/frobt.2022.904341. eCollection 2022.
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.
在群体机器人技术中，通常会假设群体中的所有机器人行为相同，并且具有相似（即便不是相同）的误差模型。然而，在现实中并非如此，误差模型以及其他操作中缺乏这种一致性，可能会导致各种涌现行为。本文考虑了误差模型的影响，并将群体中使用相同误差模型（均匀误差）运行的机器人与群体中每个具有不同误差模型（从而引入误差多样性）的机器人进行了比较。实验是在觅食任务的背景下进行的。仿真和物理实验结果表明了误差模型和多样性在实现预期群体行为方面的重要性。
