Weifu Wang
University at Albany, SUNY
38 Papers
133 Citations
Weifu Wang is an academic researcher from University at Albany, SUNY. The author has contributed to research in topics: Computer science & Knot (unit). The author has an hindex of 9, co-authored 29 publications. Previous affiliations of Weifu Wang include Rutgers University & Daimler AG.
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Papers
An online method for tight-tolerance insertion tasks for string and rope
Weifu Wang,Dmitry Berenson,Devin Balkcom +2 more
- 26 May 2015
TL;DR: The results suggest that the method presented is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string and opening differ by only 1.4 mm.
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Knot-tying with four-piece fixtures
TL;DR: A class of fixtures that can be disassembled into four pieces to extract the loosely tied knot is presented, which show that simple knots can be tied extremely quickly and reliably using four-piece fixtures.
23
Smart Metasurface for Active and Passive Cooperative Manipulation of Electromagnetic Waves.
Lixin Jiang,Yongfeng Lu,Lin Zheng,Qi Yuan,Zhibiao Zhu,Weipeng Wan,He Wang,Yongqiang Pang,Jiafu Wang,Weifu Wang,Tie Jun Cui,Shaobo Qu +11 more
TL;DR: In this article , the authors proposed a paradigm that integrates active and passive manipulation of electromagnetic (EM) waves in a reconfigurable way, where either active or passive manipulation is determined by the sensed signals and operating state to reduce detectability.
22
Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives
Zakary Littlefield,David Surovik,Massimo Vespignani,Jonathan Bruce,Weifu Wang,Kostas E. Bekris +5 more
TL;DR: This work synthesizes new and existing approaches to produce dynamic long-term motion of tensegrity robots while balancing the computational demand, and demonstrates that modest but efficiently applied search effort can unlock the utility of dynamic tensiongrity motion to produce high-quality solutions.
22
A fast online spanner for roadmap construction
TL;DR: A fast weighted streaming spanner algorithm that trims edges from roadmaps generated by robot motion planning algorithms such as Probabilistic Roadmap (PRM) and variants as the edges are generated, but before collision detection; no route in the resulting graph is more than a constant factor larger than it would have been in the original roadmap.
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