Low-Cost Wireless Modular Soft Tensegrity Robots
Jonathan Kimber, Zongliang Ji, Aikaterini Petridou, Thomas Sipple, Kentaro Barhydt, James Boggs, Luke Dosiek, John Rieffel
- 发表年份
- 2019
- 引用次数
- 24
摘要
Completely soft robots are emerging as a compelling new platform for exploring and operating in unstructured, rugged, and dynamic environments. Unfortunately, the very properties which make soft robots so appealing also make them difficult to accurately model, scalably design, and robustly control. One of the outstanding obstacles to exploring these challenges is the relative lack of low-cost entry-level investigative model systems. In this paper we describe the design and implementation of a low-cost entry-level soft robotics platform based upon modular tensegrity structures. This modular platform can scale across a variety of shapes and sizes and is capable of untethered control. We then demonstrate how unsupervised learning algorithms can be used to produce vibration-based locomotion.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002