  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.
