k-meansクラスタリング強化SVMによる多様なロボット協調タスクの高精度分類
Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task
ロボットの位置と特徴をk-meansでクラスタリングしてからSVMで分類することで、飛行ロボットと移動ロボットを高速かつ高精度に識別する手法を提案した論文。
著者: Rui Liu, Xuanzhen Xu, Yuwei Shen, Armando Zhu, Chang Yu, Tianjian Chen, Ye Zhang
分類: cs.RO
原文アブストラクト
We introduce an advanced, swift pattern recognition strategy for various multiple robotics during curve negotiation. This method, leveraging a sophisticated k-means clustering-enhanced Support Vector Machine algorithm, distinctly categorizes robotics into flying or mobile robots. Initially, the paradigm considers robot locations and features as quintessential parameters indicative of divergent robot patterns. Subsequently, employing the k-means clustering technique facilitates the efficient segregation and consolidation of robotic data, significantly optimizing the support vector delineation process and expediting the recognition phase. Following this preparatory phase, the SVM methodology is adeptly applied to construct a discriminative hyperplane, enabling precise classification and prognostication of the robot category. To substantiate the efficacy and superiority of the k-means framework over traditional SVM approaches, a rigorous cross-validation experiment was orchestrated, evidencing the former's enhanced performance in robot group classification.
