足端惯性信息辅助的四足机器人惯性导航算法  

Inertial navigation algorithm for quadruped robotassisted by foot-end inertial information

作　　者：路永乐[1] 苏胜 罗毅 徐晓东 车移 Lu Yongle;Su Sheng;Luo Yi;Xu Xiaodong;Che Yi(Chongqing Engineering Research Center of Intelligent Sensing Technology and Microsystems,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Advanced Manufacturing Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Southwest Technology and Engineering Research Institute,China South Industries Group Corporation,Chongqing 401329,China)

机构地区：[1]重庆邮电大学智能传感技术与微系统重庆市高校工程研究中心,重庆400065 [2]重庆邮电大学先进制造工程学院,重庆400065 [3]中国兵器装备集团西南技术工程研究所,重庆401329

出　　处：《仪器仪表学报》2024年第12期169-178,共10页Chinese Journal of Scientific Instrument

基　　金：重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0566)资助。

摘　　要：针对四足机器人在卫星信号缺失和环境感知退化时定位精度陡降的问题,提出一种足端惯性信息辅助的四足机器人惯性导航算法。首先,基于足端惯性信息和关节编码器数据构建腿部里程计观测模型,以补偿因接触静止假设导致的速度损失;然后,通过时域卷积网络(TCN)和双向门控循环单元(BiGRU)提取足端惯性信息和关节数据的长短时域特征,实现鲁棒的平稳区间接触事件估计。将所提出的里程计观测模型作为不变扩展卡尔曼滤波器(InEKF)的量测信息,在平稳区间内修正惯性导航误差。最后,在室外场景进行了长距离定位实验,数据显示,所提算法的平稳区间估计准确率超过96%,无闭环实验的终点误差仅为总里程的0.93%,混合地形闭环实验的东、北向平均误差分别为1.07和0.74 m,验证了所提算法在不依赖外部信息的条件下能长时间保持较高的定位精度。To address the problem of rapid decline in positioning accuracy for quadruped robots when satellite signals are unavailable and environmental perception degrades,this paper proposes an inertial navigation algorithm for quadruped robots assisted by foot-end inertial information.Firstly,a leg odometry observation model is constructed based on foot-end inertial data and joint encoder readings to compensate for velocity loss caused by the stationary contact assumption.Subsequently,a temporal convolutional network(TCN)and bidirectional gated recurrent unit(BiGRU)are employed to extract long-term and short-term features from the foot-end inertial and joint data,enabling robust estimation of stationary contact intervals.The proposed odometry observation model is employed as measurement input for an Invariant Extended Kalman Filter(InEKF)to correct inertial navigation errors during stationary intervals.Long-distance outdoor localization experiments demonstrated the effectiveness of the algorithm,achieving over 96%accuracy in stationary interval estimation.In open-loop tests,the endpoint error was only 0.93%of the total traveled distance.In mixed-terrain closed-loop experiments,the average eastward and northward errors were 1.07 m and 0.74 m,respectively,highlighting the proposed method's ability to maintain high positioning accuracy over extended periods without relying on external data.To address the problem of rapid decline in positioning accuracy for quadruped robots when satellite signals are unavailable and environmental perception degrades,this paper proposes an inertial navigation algorithm for quadruped robots assisted by foot-end inertial information.Firstly,a leg odometry observation model is constructed based on foot-end inertial data and joint encoder readings to compensate for velocity loss caused by the stationary contact assumption.Subsequently,a temporal convolutional network(TCN)and bidirectional gated recurrent unit(BiGRU)are employed to extract long-term and short-term features from the foot-end inerti

关 键 词：足端惯性信息 腿部里程计 不变扩展卡尔曼滤波器 四足机器人导航算法 

分 类 号：TH89[机械工程—仪器科学与技术]