Robotics & Machine Learning Daily News2024,Issue(Feb.26) :47-48.DOI:10.1063/5.0189612

New Robotics Study Findings Have Been Reported from Southeast University (A comparative study of three modes for realizing transmedia standing-and-hovering behavior in robotic dolphins)

Robotics & Machine Learning Daily News2024,Issue(Feb.26) :47-48.DOI:10.1063/5.0189612

New Robotics Study Findings Have Been Reported from Southeast University (A comparative study of three modes for realizing transmedia standing-and-hovering behavior in robotic dolphins)

扫码查看

Abstract

A new study on robotics is now available. According to news reporting originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “Three different hovering modes, namely, the caudal fin, pectoral fins, and multi fins, were utilized to achieve the standingand- hovering behavior in robotic dolphins.” Financial supporters for this research include National Natural Science Foundation of China; State Key Laboratory of Robotics And System. The news reporters obtained a quote from the research from Southeast University: “A three-dimensional dolphin model, consisting of body, caudal fin, and symmetric pectoral fins, was used as the virtual swimmer to implement three hovering modes. A novel paddling motion was proposed, and a symmetric shape was designed of the pectoral fins. The hovering mechanisms of different modes were revealed, and the mapping relationships between different motion and performance parameters such as hovering height, efficiency, stability, and rapidity were established. The respective advantages of the three hovering modes were compared. The results showed that the caudal fin mode had the best hovering stability, while the pectoral fins mode had the best hovering rapidity. Moreover, it is worth noting that the multi fins mode had both the good hovering stability and rapidity.”

Key words

Southeast University/Nanjing/People’s Republic of China/Asia/Emerging Technologies/Machine Learning/Nano-robot/Robotics/Robots

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
参考文献量41
段落导航相关论文