Online motion planning for tethered robots in extreme terrain

M. Tanner, J. Burdick, I. Nesnas
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引用次数: 13

Abstract

Several potentially important science targets have been observed in extreme terrains (steep or vertical slopes, possibly covered in loose soil or granular media) on other planets. Robots which can access these extreme terrains will likely use tethers to provide climbing and stabilizing force. To prevent tether entanglement during descent and subsequent ascent through such terrain, a motion planning procedure is needed. Abad-Manterola, Nesnas, and Burdick [1] previously presented such a motion planner for the case in which the geometry of the terrain is known a priori with high precision. Their algorithm finds ascent/descent paths of fixed homotopy, which minimizes the likelihood of tether entanglement. This paper presents an extension of the algorithm to the case where the terrain is poorly known prior to the start of the descent. In particular, we develop new results for how the discovery of previously unknown obstacles modifies the homotopy classes underlying the motion planning problem. We also present a planning algorithm which takes the modified homotopy into account. An example illustrates the methodology.
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极端地形系留机器人在线运动规划
在其他行星的极端地形(陡峭或垂直的斜坡,可能被松散的土壤或颗粒状介质覆盖)中观察到几个潜在的重要科学目标。能够进入这些极端地形的机器人可能会使用绳索来提供攀爬和稳定的力量。为了防止在下降和随后的上升过程中绳索缠结,需要一个运动规划程序。Abad-Manterola, Nesnas和Burdick[1]先前提出了这样一个运动规划器,在这种情况下,地形的几何形状是高精度先验已知的。他们的算法找到固定同伦的上升/下降路径,这将最小化系绳纠缠的可能性。本文提出了该算法的扩展,以在开始下降之前对地形知之甚少的情况下。特别是,我们开发了新的结果,如何发现以前未知的障碍修改运动规划问题的同伦类。我们还提出了一种考虑修正同伦的规划算法。一个示例说明了该方法。
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