群体行为
莱维航班
计算机科学
熵(时间箭头)
机器人
觅食
布朗运动
蚁群
人工智能
群体智能
数学优化
蚁群优化算法
数学
随机游动
粒子群优化
机器学习
生态学
物理
统计
生物
量子力学
作者
Aditya M. Deshpande,Manish Kumar,Suresh Ramakrishnan
标识
DOI:10.1115/dscc2017-5229
摘要
Design of robot swarms inspired by self-organization in social insect groups is currently an active research area with a diverse portfolio of potential applications. In this work, the authors propose a control law for efficient area coverage by a robot swarm in a 2D spatial domain, inspired by the unique dynamical characteristics of ant foraging. The novel idea pursued in the effort is that dynamic, adaptive switching between Brownian motion and Lévy flight in the stochastic component of the search increases the efficiency of the search. Influence of different pheromone (the virtual chemotactic agent that drives the foraging) threshold values for switching between Lévy flights and Brownian motion is studied using two performance metrics — area coverage and visit entropy. The results highlight the advantages of the switching strategy for the control framework, particularly in cases when the object of the search is scarce in quantity or getting depleted in real-time.
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