Fuzzy-based self-organizing aggregation method was suggested in the present study for swarm robots. In the suggested method, Swarm robots evaluate their limited sensor input via rules of fuzzy logic and display aggregation behavior with the suggested aggregation method. In the meantime, swarm robots also have the ability to escape the obstacles in a bounded arena with this method. Swarm robots perceive the neighboring robots with this method, make individual decisions and display aggregation behaviors. Different from the traditional self-organizing aggregation methods, the suggested approach utilizes fuzzy logic controllers to evaluate limited sensor data. Systematic experiments were applied on different number of swarm robots with different detection areas in arenas of different sizes. Moreover, noise was applied on the sensor inputs for examining the performance of the fuzzy logic based self-organizing aggregation method. The scalability and flexibility of the self-organizing aggregation behaviors of swarm robots were evaluated by way of systematic experiments. The swarm robots displayed aggregation behavior during the systematic experiments applied despite the changes in the number of robots and detection distances.

在研究中提出了基于模糊的自组织聚合方法。在建议的方法中，Swarm机器人通过模糊逻辑规则评估其有限的传感器输入，并使用建议的聚合方法显示聚合行为。同时，群机器人也可以使用此方法在有界竞技场中躲避障碍物。群机器人使用这种方法感知相邻的机器人，做出单独的决定并显示聚集行为。与传统的自组织聚合方法不同，建议的方法利用模糊逻辑控制器来评估有限的传感器数据。在不同规模的竞技场上，对不同数量的具有不同检测区域的群体机器人进行了系统的实验。此外，将噪声应用于传感器输入，以检查基于模糊逻辑的自组织聚合方法的性能。通过系统实验评估了群体机器人自组织聚集行为的可扩展性和灵活性。尽管机器人数量和检测距离发生了变化，但在进行系统的实验过程中，群体机器人仍显示出聚集行为。