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Integrating cloud model in evolutionary algorithm for path planning of mobile robots

机译:在移动机器人路径规划的进化算法中集成云模型

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Evolutionary algorithms (EA) have been used to solve path planning of mobile robots successfully. However, accuracy and convergence are always topics. The cloud model which transforms qualitative concept into quantitative description, and vice versa, is adept in uncertainty modeling. It can be used in EA for evolving in a uniform and natural way. An EA embedded with cloud model (EACM) for path planning of mobile robots was proposed in this paper. The algorithm adopted floating-point coding and realized fast converging. Simulation results verified the efficiency of our algorithm.
机译:进化算法(EA)已用于成功解决移动机器人的路径规划。但是,准确性和收敛性始终是主题。将定性概念转换为定量描述(反之亦然)的云模型善于不确定性建模。它可以在EA中用于以统一自然的方式发展。本文提出了一种嵌入云模型的EA(EACM),用于移动机器人的路径规划。该算法采用浮点编码,实现了快速收敛。仿真结果验证了该算法的有效性。

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