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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A GENETIC-BASED OPTIMIZED FUZZY-TABU CONTROLLER FOR MOBILE ROBOT RANDOMIZED NAVIGATION IN UNKNOWN ENVIRONMENTO
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A GENETIC-BASED OPTIMIZED FUZZY-TABU CONTROLLER FOR MOBILE ROBOT RANDOMIZED NAVIGATION IN UNKNOWN ENVIRONMENTO

机译：未知环境中基于遗传算法的移动机器人随机导航优化模糊TABU控制器

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摘要

Although sampling-based path planning algorithms have been shown robust performances in many cases, they still suffer from many drawbacks. Inefficiency in online/sensor-based planning purposes, generating far-from-optimum paths, high running time, failure in difficult environments and generating unstable variant results in different runs are some of the existing problems with sampling-based methods. We propose an online sampling-based path planning algorithm which employs a fuzzy-tabu controller to evaluate the generated samples and select better ones in order to improve the performance of the planner. Furthermore, a genetic algorithm-based optimization framework is developed to optimize the parameters of the controller. The genetic optimizer takes place before the planner starts any planning task and attempts to optimize the fuzzy-tabu controller parameters and also the parameters of the planner. We simulate the proposed algorithm to analyze its performance and to compare it with existing sampling-based algorithms. The proposed algorithm generates relatively short paths in less than few seconds while results variations between different runs are considerably low.
机译：尽管基于采样的路径规划算法已在许多情况下显示出强大的性能，但它们仍存在许多缺点。基于采样的方法存在的一些现有问题是，基于在线/传感器的计划目的效率低下，生成距离最佳路径远，运行时间长，在困难环境中发生故障以及在不同运行中生成不稳定的变量结果。我们提出了一种基于在线采样的路径规划算法，该算法采用模糊Tabu控制器来评估生成的样本并选择更好的样本，以提高计划者的性能。此外，开发了基于遗传算法的优化框架来优化控制器的参数。遗传优化器发生在计划者开始任何计划任务并尝试优化Fuzzy-tabu控制器参数以及计划者的参数之前。我们对提出的算法进行仿真，以分析其性能并将其与现有的基于采样的算法进行比较。所提出的算法在不到几秒钟的时间内生成了相对较短的路径，而不同运行之间的结果差异却很小。

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