Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors

Ji Chang¹  Chen Cheng¹  Kun Li¹  Zerui Li¹  Wenjun Lv¹  Saifei Ma²  Chenhui Yuan²  Yuping Wu³ 

[1] Department of Automation, University of Science and Technology of China, Hefei 230027, China;Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China;Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China;

关键词: autonomous robot;    non-geometric hazards;    terrain classification;    dynamic environment;    vibration;   

DOI  :  10.3390/s20226550

来源: DOAJ

【 摘 要 】

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

【 授权许可】

Unknown