The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans
The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans
复制标题
跌倒的解剖：对双足行走机器人和人类的原始力传感器数据进行自动实时分析
DOI：
10.1109/iros.2012.6385467
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
D. Caldwell
中科院分区：
文献类型：
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作者：
Petar Kormushev;B. Ugurlu;L. Colasanto;N. Tsagarakis;D. Caldwell
提出了一种能够自动分析两足行走机器人和人的地面反作用力数据的方法。自动分析的输入是来自安装在机器人脚上的力传感器的原始数据。输出是详细的信息，例如检测到的单支撑、双支撑和摆动阶段、它们的持续时间、事件的时间（如脚跟撞击）、相变和机器人本身的属性。该方法具有通用性、无参数性、无模型性、鲁棒性、计算效率高等特点，适用于行走过程中的实时应用。它可以检测到可能导致机器人坠落的早期不稳定迹象。提出了三个现实世界的实验：一个柔性双足机器人，一个僵硬的人形机器人和一个人类受试者。
An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.