沙质地形中轮式移动机器人路径规划中与滑移率相关的可穿越性建模

Go Sakayori, G. Ishigami
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摘要

行星探索漫游车已被用于科学研究或作为即将到来的载人飞行任务的先驱。通过车轮滑移来预测漫游车的可穿越性,可确保漫游车在可变形的行星表面上安全高效地自主运行;通过考虑车轮与土壤的相互作用或地形数据来减少滑移的路径规划算法,可将漫游车无法移动的风险降至最低。了解瞬态下车轮与土壤的相互作用对于开发更精确的滑移率预测模型至关重要,而过去的路径规划假设路径上产生的滑移是一系列稳定状态下的滑移率。本文重点关注瞬态滑移,即滑移率的时间导数,将其明确纳入路径规划算法的成本函数中。我们建立了一个回归模型,将滑移率和牵引力作为输入,并输出滑移率。使用单轮试验台进行的实验表明,即使车轮牵引力相同,滑移率也会随着滑移率的不同而变化;我们证实,在牵引力相同的情况下,滑移率的绝对值越小,滑移率就越大。对回归模型的统计分析证实,该模型对滑移率的估计精度平均在 85% 以内。利用回归模型进行的路径规划仿真证实,漫游车在崎岖地形环境中行驶时的滑移率降低了 58%。动力学仿真结果表明,所提出的方法可以降低崎岖地形环境中的滑移率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling of slip rate-dependent traversability for path planning of wheeled mobile robot in sandy terrain
A planetary exploration rover has been employed for scientific endeavors or as a precursor for upcoming manned missions. Predicting rover traversability from its wheel slip ensures safe and efficient autonomous operations of rovers on deformable planetary surfaces; path planning algorithms that reduce slips by considering wheel-soil interaction or terrain data can minimize the risk of the rover becoming immobilized. Understanding wheel-soil interaction in transient states is vital for developing a more precise slip ratio prediction model, while path planning in the past assumes that slips generated at the path is a series of slip ratio in steady state. In this paper, we focus on the transient slip, or slip rate the time derivative of slip ratio, to explicitly address it into the cost function of path planning algorithm. We elaborated a regression model that takes slip rate and traction force as inputs and outputs slip ratio, which is employed in the cost function to minimize the rover slip in path planning phase. Experiments using a single wheel testbed revealed that even with the same wheel traction force, the slip ratio varies with different slip rates; we confirmed that the smaller the absolute value of the slip rate, the larger the slip ratio for the same traction force. The statistical analysis of the regression model confirms that the model can estimate the slip ratio within an accuracy of 85% in average. The path planning simulation with the regression model confirmed a reduction of 58% slip experienced by the rover when driving through rough terrain environments. The dynamics simulation results insisted that the proposed method can reduce the slip rate in rough terrain environments.
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