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Collective robotic search using hybrid techniques: Fuzzy logic and swarm intelligence inspired by nature

机译:使用混合技术的集体机器人搜索:受自然启发的模糊逻辑和群智能

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This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm.
机译:本文提出了两种针对目标/任务集中应用的机器人群导航的新策略,包括地雷检测和消防。第一种方法提出了粒子群优化(PSO)算法机器人中的嵌入式模糊逻辑方法,第二种方法提出了一组模糊逻辑控制器,每个机器人上都有一个。两种策略的框架都受到自然群体的启发,例如鱼群或鸟群。除了使用上述方法进行目标搜索外,还提出了用于协调机器人群的层次结构。评估这两种策略的鲁棒性,以评估群体成员的失败或损失。结果既有策略,也有性能比较,是针对贪婪的搜索算法进行的。

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