Abstract
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.
Abstract (translated)
我们提出了一个在曲线协商中用于各种机器人的高级、快速的模式识别策略。该方法利用了一个先进的k-means聚类增强支持向量机算法，将机器人明显地分为飞行或移动机器人。最初，范式将机器人的位置和特征视为表明分散机器人模式的基本参数。随后，利用k-means聚类技术有助于有效地将机器数据进行分离和合并，显著优化支持向量轮廓绘制过程，并加速识别阶段。在预备阶段之后，将SVM方法应用于构建区分性的超平面，使得精确分类和预测机器类别。为了证实k-means框架相对于传统SVM方法的优越性和有效性，进行了一项严谨的交叉验证实验，结果表明前者在机器人群体分类方面表现更好。
URL
https://arxiv.org/abs/2405.03026
