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A swarm intelligence labour division approach to solving complex area coverage problems of swarm robots

机译:一种群体识别群体覆盖群体机器人覆盖问题的群体情报司探

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摘要

The complex area coverage problem is classical and widespread in the research field of swarm robots. In order to solve the complex area coverage problem with complex nonlinear boundary and special task area (forbidden area or threat area), firstly, the task area is adjusted and grid discretisation. Then, inspired by the labour division phenomenon of typical biological groups such as bee colony and ant colony, the paper analyses the performance characteristics of typical ant colony labour division model (response threshold model) and bee colony labour division model (activation-inhibition model) from the perspectives of individual and environment, individual and individual, and a new swarm intelligence labour division approach (activation-inhibition response threshold algorithm) to solve the complex area coverage problem of swarm robot. Three experiments are carried out to illustrate that the algorithm are endowed with great ability of area coverage and dynamic environment. It can respond to the sudden threat in time and make an efficient response, which has a good practical application prospects.
机译:复杂的区域覆盖问题是群体机器人的研究领域的古典和普遍存在。为了解决复杂的非线性边界和特殊任务区域(禁区或威胁区域)的复杂区域覆盖问题,首先,调整任务区域和网格分散。然后,受到诸如蜜蜂菌落和蚁群等典型生物群体的劳动部现象的启发,分析了典型蚁群劳动师模型(响应阈值模型)和蜜蜂植物划分模型的性能特征(激活抑制模型)从个人和环境,个人和个人的角度来看,以及一种新的群体智能劳动划分方法(激活抑制响应阈值算法)来解决群体机器人的复杂区域覆盖问题。进行三个实验以说明该算法具有较强的面积覆盖和动态环境的能力。它可以及时响应突发的威胁,并进行有效的反应,这具有良好的实际应用前景。

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