论文标题
机器人群的有效定量评估：覆盖范围和针对莱维策略
Efficient quantitative assessment of robot swarms: coverage and targeting Lévy strategies
论文作者
论文摘要
长期以来，以生物学启发的策略适应了群体机器人系统，包括偏见的随机步行，对趋化线索的反应和远程协调。在本文中，我们将开发的分析工具应用于建模生物系统（例如连续描述）的有效定量表征。作为例证，讨论了具有特征性远程运动的布朗和莱维策略。结果，我们获得了用于优化机器人运动定律的计算快速方法，以实现规定的集体行为。我们展示了如何计算覆盖范围和打击时间等性能指标，并说明了我们在区域覆盖和搜索问题方面的准确性和效率。连续模型与机器人模拟之间的比较证实了我们方法的定量一致性和速度。结果证实并量化了莱维策略比布朗运动对群体机器人技术中的搜索和区域覆盖问题的优势。
Biologically inspired strategies have long been adapted to swarm robotic systems, including biased random walks, reaction to chemotactic cues and long-range coordination. In this paper we apply analysis tools developed 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 of our approach. Results confirm and quantify the advantage of Lévy strategies over Brownian motion for search and area coverage problems in swarm robotics.
