Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929336090624000 |
|---|---|
| author | Liu, Rui Xu, Xuanzhen Shen, Yuwei Zhu, Armando Yu, Chang Chen, Tianjian Zhang, Ye |
| author_facet | Liu, Rui Xu, Xuanzhen Shen, Yuwei Zhu, Armando Yu, Chang Chen, Tianjian Zhang, Ye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task Liu, Rui Xu, Xuanzhen Shen, Yuwei Zhu, Armando Yu, Chang Chen, Tianjian Zhang, Ye Robotics 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. |
| title | Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.03026 |