Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task

TL;DR

The paper tackles rapid and accurate classification of heterogeneous robot groups (aerial vs mobile) during collaborative tasks, where class boundaries are non-linear. It proposes a k-means clustering–enhanced SVM ($\text{k-SVM}$) pipeline that first reduces data complexity with clustering and then learns a discriminative hyperplane using a Gaussian kernel; optimal parameters are found via grid search and cross-validation. Empirical results across multiple distribution scenarios show that $\text{k-SVM}$ achieves faster training times and higher accuracy than standard SVM, especially when class overlap is significant. The approach offers scalable, real-time pattern recognition to improve multi-robot coordination and decision-making.

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.

Figures (5)

• Figure 1: Two different robot groups in a collaboration task; one flying robot installed a camera module as a "commander" to detect and recognize the whole scenario of both groups to coordinate the collaboration.
• Figure 2: The classifying results using k-SVM method for both kinds of robot. Yellow pentagon: mobile robot; black pentagon: flying robot.
• Figure 3: The classifying results using k-SVM method for both kinds of robots. Yellow pentagon: mobile robot; black pentagon: flying robot.
• Figure 4: The testing evaluation for two types robot using the K-SVM method based on training data in Fig. \ref{['figure5']}.
• Figure 5: Classification results for two types robot using k-SVM method.