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An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot

机译：一种有效的数据驱动模糊方法解决移动机器人的运动计划问题

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摘要

A data-driven fuzzy approach is developed for solving the motion planning problem of a mobile robot in the presence of moving obstacles. The approach consists of devising a general method for the derivation of input-output data to construct a fuzzy logic controller (FLC) off-line. The FLC is constructed based on the use of a recently developed data-driven and efficient fuzzy controller modeling algorithm, and it can then be used on-line by the robot to navigate among moving obstacles. The novelty in the presented approach, as compared to the most recent fuzzy ones, stems from its generality. That is, the devised data-derivation method enables the construction of a single FLC to accommodate a wide range of scenarios. Also, care has been taken to find optimal or near optimal FLC solution in the sense of leading to a sufficiently small robot travel time and collision-free path between the start and target points. Furthermore, since the algorithm has been shown efficient in the representation of non-linear control functions, in terms of combating noise and possessing a good generalization capability, these aspects are also tested in this practical control problem. Comparison of the results with those obtained by fuzzy-genetic and another hybrid and data-driven design approach is also done.
机译：开发了一种数据驱动的模糊方法，用于解决在存在移动障碍物的情况下移动机器人的运动计划问题。该方法包括设计一种用于推导输入输出数据的通用方法，以离线构建模糊逻辑控制器（FLC）。 FLC是基于最近开发的数据驱动的高效模糊控制器建模算法构建的，然后可以由机器人在线使用，以在移动的障碍物之间导航。与最新的模糊方法相比，本方法的新颖性源于其通用性。即，所设计的数据推导方法使得能够构造单个FLC以适应广泛的场景。同样，从导致足够小的机器人行进时间以及起点和目标点之间的无碰撞路径的意义上，已经着手寻找最佳或接近最佳的FLC解决方案。此外，由于已经证明该算法在非线性控制功能的表示中是有效的，因此在抗噪和具有良好的泛化能力方面，这些方面也在该实际控制问题中进行了测试。还将结果与通过模糊遗传和另一种混合数据驱动设计方法获得的结果进行比较。

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