Online Learning for Foot Contact Detection of Legged Robot Based on Data Stream Clustering.
Online Learning for Foot Contact Detection of Legged Robot Based on Data Stream Clustering.
复制标题
基于数据流聚类的足式机器人足部接触检测在线学习
DOI：
10.3389/fbioe.2021.771415
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发表时间：
2021
影响因子：
5.7
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Liu Q;Yuan B;Wang Y
足部接触检测是基于状态机的腿式机器人运行控制的关键，在状态机中，控制器在腿的飞行阶段和着陆阶段使用不同的控制模块。本文提出了一种在线学习框架，以提高有腿机器人奔跑中足部接触检测的快速性。在该框架中，采用三个子分量的高斯混合模型学习平地跑步、楼上跑步和楼下跑步对应的接触数据向量。采用在线数据流学习算法对模型进行更新。针对在线获取着陆时刻接触数据困难的问题，设计了“回溯”模块，对存储堆栈中的接触数据进行回溯，直到数据满足概率接触准则。为了测试脚是否与地面接触，提出了一种投影法。将腿段飞行阶段的采集数据向量投影到独立的随机向量空间中，如果投影的随机变量均落在相应高斯分布的1.5σ范围内，则触发接触事件。在一个有腿机器人上的实验表明，与仅使用腿力预测相比，该算法可以提前16 ms预测足部接触，从而简化了控制器的设计，提高了有腿机器人控制的稳定性。
Foot contact detection is critical for legged robot running control using state machine, in which the controller uses different control modules in the leg flight phase and landing phase. This paper presents an online learning framework to improve the rapidity of foot contact detection in legged robot running. In this framework, the Gaussian mixture model with three sub-components is adopted to learn the contact data vectors corresponding to running on flat ground, running upstairs, and running downstairs. An online data stream learning algorithm is used to update the model. To deal with the difficulty in obtaining contact data at landing moment online, a “trace back” module is designed to trace back the contact data in the memory stack until the data meet with the probability contact criterion. To test if the foot is in contact with the ground, a projection method is proposed. The acquiring data vector during the leg flight phase is projected onto an independent random vector space, and the contact event is triggered if all projected random variables fall within 1.5σ of the corresponding Gaussian distribution. Experiments on a legged robot show that the presented algorithm can predict the foot contact 16 ms in advance compared with the prediction using only leg force, which will ease the controller design and enhance the stability of legged robot control.