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首页> 外文期刊>IEEE Transactions on Robotics >Singularity-Free Guiding Vector Field for Robot Navigation
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Singularity-Free Guiding Vector Field for Robot Navigation

机译:机器人导航的奇点导向矢量领域

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In robot navigation tasks, such as unmanned aerial vehicle (UAV) highway traffic monitoring, it is important for a mobile robot to follow a specified desired path. However, most of the existing path-following navigation algorithms cannot guarantee global convergence to desired paths or enable following self-intersected desired paths due to the existence of singular points where navigation algorithms return unreliable or even no solutions. One typical example arises in vector-field guided path-following (VF-PF) navigation algorithms. These algorithms are based on a vector field, and the singular points are exactly where the vector field diminishes. Conventional VF-PF algorithms generate a vector field of the same dimensions as those of the space where the desired path lives. In this article, we show that it is mathematically impossible for conventional VF-PF algorithms to achieve global convergence to desired paths that are self-intersected or even just simple closed (precisely, homeomorphic to the unit circle). Motivated by this new impossibility result, we propose a novel method to transform self-intersected or simple closed desired paths to nonself-intersected and unbounded (precisely, homeomorphic to the real line) counterparts in a higher dimensional space. Corresponding to this new desired path, we construct a singularity-free guiding vector field on a higher dimensional space. The integral curves of this new guiding vector field is thus exploited to enable global convergence to the higher dimensional desired path, and therefore the projection of the integral curves on a lower dimensional subspace converge to the physical (lower dimensional) desired path. Rigorous theoretical analysis is carried out for the theoretical results using dynamical systems theory. In addition, we show both by theoretical analysis and numerical simulations that our proposed method is an extension combining conventional VF-PF algorithms and trajectory tracking algorithms. Finally, to show the practical value of our proposed approach for complex engineering systems, we conduct outdoor experiments with a fixed-wing airplane in windy environment to follow both 2-D and 3-D desired paths.
机译:在机器人导航任务中,如无人驾驶航空公司(UAV)公路交通监控,移动机器人遵循指定的所需路径很重要。然而,大多数现有路径导航算法不能保证对所需路径的全局会聚,或者由于存在奇异点而在自相交的期望路径之后,其中导航算法返回不可靠甚至没有解决方案。在向量场引导路径之后(VF-PF)导航算法中,一个典型的示例出现。这些算法基于矢量字段,并且奇点正是矢量字段减小的位置。传统的VF-PF算法生成与所需路径寿命的空间相同尺寸的矢量字段。在本文中,我们表明,传统的VF-PF算法是数学上不可能实现对自相交的所需路径的全局融合,甚至只是简单关闭(精确,同内圈)。通过这种新的不可能性,我们提出了一种新的方法来将自相交或简单的封闭所需路径转换为较高尺寸空间的非彼此与非束缚和无限的(精确,同内常规)对应物。对应于这种新的所需路径,我们在更高的尺寸空间上构建一个奇点导向的矢量场。因此,利用该新引导载体场的积分曲线以使全局会聚到更高的尺寸期望的路径,因此在较低尺寸子空间上的积分曲线的投影会聚到物理(下维数)期望路径。使用动力系统理论对理论结果进行严格的理论分析。此外,我们通过理论分析和数值模拟显示我们所提出的方法是结合传统的VF-PF算法和轨迹跟踪算法的扩展。最后,为了表明我们提出的复杂工程系统方法的实用价值,我们在有风环境中使用固定翼飞机进行户外实验,以遵循2-D和3-D所需路径。

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