论文标题

在感知降低的外星环境中,行星流浪者的本体感受性检测

Proprioceptive Slip Detection for Planetary Rovers in Perceptually Degraded Extraterrestrial Environments

论文作者

Kilic, Cagri, Gu, Yu, Gross, Jason N.

论文摘要

滑动检测对于在外星人表面驾驶的流浪者的安全性和效率至关重要。当前的行星流动站滑移检测系统依赖于视觉感知,假设可以在环境中获得足够的视觉特征。但是,基于视觉的方法容易受到感知降解的行星环境,具有主要低地形特征,例如岩石,冰川地形,盐疏外物以及较差的照明条件,例如黑暗的洞穴和永久阴影区域。仅依靠视觉传感器进行滑动检测也需要额外的计算功率,并降低了流动性遍历速率。本文回答了如何检测行星漫游车的车轮滑移而不取决于视觉感知的问题。在这方面,我们提出了一个滑动检测系统,该系统从本体感受的本地化框架中获取信息,该框架能够提供数百米的可靠,连续和计算有效的状态估计。这是通过使用零速度更新,零角度更新和非独立限制作为惯性导航系统框架的伪测量更新来完成的。对所提出的方法进行了对实际硬件的评估,并在行星 - 分析环境中进行了现场测试。该方法仅使用IMU和车轮编码器就可以达到150 m左右的92%滑动检测精度。

Slip detection is of fundamental importance for the safety and efficiency of rovers driving on the surface of extraterrestrial bodies. Current planetary rover slip detection systems rely on visual perception on the assumption that sufficient visual features can be acquired in the environment. However, visual-based methods are prone to suffer in perceptually degraded planetary environments with dominant low terrain features such as regolith, glacial terrain, salt-evaporites, and poor lighting conditions such as dark caves and permanently shadowed regions. Relying only on visual sensors for slip detection also requires additional computational power and reduces the rover traversal rate. This paper answers the question of how to detect wheel slippage of a planetary rover without depending on visual perception. In this respect, we propose a slip detection system that obtains its information from a proprioceptive localization framework that is capable of providing reliable, continuous, and computationally efficient state estimation over hundreds of meters. This is accomplished by using zero velocity update, zero angular rate update, and non-holonomic constraints as pseudo-measurement updates on an inertial navigation system framework. The proposed method is evaluated on actual hardware and field-tested in a planetary-analog environment. The method achieves greater than 92% slip detection accuracy for distances around 150 m using only an IMU and wheel encoders.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源