For legged robots to operate in complex terrains, they must be robust to the disturbances and uncertainties they encounter. This paper contributes to enhancing robustness through the design of fall detection/prediction algorithms that will provide sufficient lead time for corrective motions to be taken. Falls can be caused by abrupt (fast-acting), incipient (slow-acting), or intermittent (non-continuous) faults. Early fall detection is a challenging task due to the masking effects of controllers (through their disturbance attenuation actions), the inverse relationship between lead time and false positive rates, and the temporal behavior of the faults/underlying factors. In this paper, we propose a fall detection algorithm that is capable of detecting both incipient and abrupt faults while maximizing lead time and meeting desired thresholds on the false positive and negative rates.

翻译：为使腿式机器人在复杂地形中运行，它们必须能够有效抵御所遇到的扰动和不确定性。本文通过设计跌倒检测/预测算法来增强机器人鲁棒性，为采取纠正动作提供充分的提前时间。跌倒可能由突发性（快速作用）、渐进性（慢速作用）或间歇性（非连续）故障引发。由于控制器的掩蔽效应（通过其扰动抑制行为）、提前时间与误报率之间的反比关系，以及故障/潜在因素的时间特性，早期跌倒检测是一项具有挑战性的任务。本文提出一种能够同时检测渐进性与突发性故障的跌倒检测算法，该算法可在满足误报率与漏报率期望阈值的条件下，最大化提前时间。