信息与控制2025，Vol.54Issue(6)：880-892,905,14.DOI:10.13976/j.cnki.xk.2024.4142
基于监督学习的双足机器人触地事件检测方法
Supervised Learning Based Detection Method for Ground Contact Events of Bipedal Robots
摘要
Abstract
Traditional ground contact detection methods are often affected by sensor noise and dynamic complexity in unstructured terrains.Thus,they rely heavily on foot force sensors,and this over-reliance restricts their applicability to certain robot scenarios.To address these limitations,we propose a supervised learning based ground contact event detection algorithm for bipedal robots.Further,we design an algorithm to assist the invariant extended Kalman filter in obtaining accu-rate,inexpensive ground contact signals,enabling precise state estimation.First,we establish a state machine model for ground contact events and analyze the contact event types and their transi-tions.Second,we collect sensor data from the robot,including the inertial measurement unit,en-coders,current sensors,foot force sensors,and kinematic-derived foot height and vertical velocity(z-axis).We use mutual information for feature selection,thereby retaining 16-dimensional fea-tures.Finally,we sample historical features and use them to construct graph embeddings via a clustering method.These graph embeddings are subsequently fed into a one-dimensional convolu-tional neural network to extract temporal information.This enables the regression analysis of foot contact forces and the classification of ground contact events.The proposed method is experimen-tally compared with current-based detection and neural network-based classification approaches.Results reveal that the proposed method performs excellently during flat-ground walking and dem-onstrates strong generalization and robustness,significantly outperforming traditional methods in complex terrains.Overall,this method overcomes the reliance of conventional ground contact de-tection approaches on foot force sensors and provides an efficient and inexpensive solution for robot ground contact detection.关键词
监督学习/聚类分类/图编码/互信息法/触地检测/双足机器人/不变扩展卡尔曼滤波Key words
supervised learning/clustering classification/graph encoding/mutual information method/ground contact detection/bipedal robot/invariant extended Kalman filter分类
信息技术与安全科学引用本文复制引用
陈弘毅,侯运锋,李家龙,李清都..基于监督学习的双足机器人触地事件检测方法[J].信息与控制,2025,54(6):880-892,905,14.基金项目
国家自然科学基金项目(92048205,62403323) （92048205,62403323）
东方学者计划项目(TP2019064) （TP2019064）
上海市自然科学基金项目(24ZR1453100) （24ZR1453100）
