植绒(纹理)
群体行为
计算机科学
粒子群优化
障碍物
避障
分布式计算
数学优化
人工智能
算法
数学
机器人
移动机器人
复合材料
材料科学
法学
政治学
作者
Ziqing Zhou,Chun Ouyang,Linqiang Hu,Yifang Xie,Yuning Chen,Zhongxue Gan
标识
DOI:10.1016/j.eswa.2023.122694
摘要
The bio-inspired flocking model has been widely utilized in self-organized swarms. However, existing potential field methods fail to guarantee safe and orderly swarm movement in dense environments featuring both static and dynamic obstacles. In this study, we propose an innovative, dynamically optimized flocking control framework that integrates an improved and tunable self-propelled flocking model with an enhanced dynamic particle swarm optimization (DPSO) algorithm. Our model incorporates pigeon-inspired obstacle gap selection based on a probability approach to optimize steering decisions. Moreover, we introduce the DPSO algorithm, which combines a collaboration-based particle swarm optimizer with fractional order velocity incorporated history-guided estimation. This hybrid algorithm dynamically determines the optimal set of control parameters to maintain the desired swarm state. Through numerical comparison experiments with state-of-the-art models, we demonstrate that our approach enhances the safety, synchronization, and efficiency of the swarm even within a limited visual field, without prior environmental information. These features bring the simulations closer to real-world experiments.
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