A drift-diffusion model for robotic obstacle avoidance
A drift-diffusion model for robotic obstacle avoidance
复制标题
机器人避障的漂移扩散模型
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Koditschek
中科院分区：
文献类型：
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作者：
Paul B. Reverdy;B. Ilhan;D. Koditschek
我们开发了一个随机框架，机器人导航的障碍物的存在下建模和分析。我们表明，与适当的假设，机器人避免一个给定的障碍物的概率可以减少到一个单一的无量纲参数，捕捉所有相关的数量的问题的函数。这个参数类似于文献中关于对流扩散流体流动中的质量输运的佩克莱数。使用该框架，我们还计算在信息丰富的情况下，逃离障碍物所需的时间的统计数据。计算结果表明，在导航策略中加入噪声可以提高导航性能。最后，我们提出了实验结果，说明这些性能的改善机器人平台上。
We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Péclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform.