Duncan S, Estrada-Rodriguez G, Stocek J, Dragone M, Vargas P A, Gimperlein H
Robotics Lab, Edinburgh Centre for Robotics, School of Mathematical and Computer Sciences School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
Department of Mathematics, Universitat Politecnica de Catalunya, Jordi Girona, 1-3, 08034, Barcelona, Spain.
Bioinspir Biomim. 2022 Mar 30;17(3). doi: 10.1088/1748-3190/ac57f0.
Biologically inspiredhave long been adapted to swarm robotic systems, including biased random walks, reaction to chemotactic cues and long-range coordination. In this paper we applydeveloped for modeling biological systems, such as continuum descriptions, to the efficient quantitative characterization of robot swarms. As an illustration, both Brownian and Lévy strategies with a characteristic long-range movement are discussed. As a result we obtain computationally fast methods for the optimization of robot movement laws to achieve a prescribed collective behavior. We show how to compute performance metrics like coverage and hitting times, and illustrate the accuracy and efficiency of our approach for area coverage and search problems. Comparisons between the continuum model and robotic simulations confirm the quantitative agreement and speed up by a factor of over 100 of our approach. Results confirm and quantify the advantage of Lévy strategies over Brownian motion for search and area coverage problems in swarm robotics.
长期以来，受生物启发的方法已被应用于群体机器人系统，包括有偏随机游走、对趋化线索的反应和远程协调。在本文中，我们将为生物系统建模而开发的方法，如连续统描述，应用于机器人群体的高效定量表征。作为一个例证，我们讨论了具有特征性远程运动的布朗策略和 Lévy 策略。结果，我们获得了计算速度快的方法，用于优化机器人运动定律以实现规定的集体行为。我们展示了如何计算诸如覆盖率和击中时间等性能指标，并说明了我们的方法在区域覆盖和搜索问题上的准确性和效率。连续统模型与机器人模拟之间的比较证实了定量一致性，并且我们的方法速度提高了 100 多倍。结果证实并量化了 Lévy 策略在群体机器人的搜索和区域覆盖问题上相对于布朗运动的优势。
