Space Exploration - towards Bio-Inspired Climbing Robots
Carlo Menon, Michael Murphy, Metin Sitti, Lydia Nicholas
- 发表年份
- 2007
- 引用次数
- 12
- 访问权限
- 开放获取
摘要
Robotic systems continue to be an important tool in space exploration, and as robotic systems continue to gain capability, the particular challenges of the space environment and the tasks required in space will benefit from such systems finding new roles in space. This chapter focuses on the design of climbing robots suitable for use in space. These mimic the climbing ability of geckos, using micro-structured hairs to provide dry adhesion, and exploit gait to exhibit rapid and robust motion over vertical surfaces on earth. Mobility of robotic systems allows many different roles for systems in space, including exploration on extraterrestrial surfaces and navigating vehicle surfaces in orbit. Climbing robots in space may provide extra capability in the ability to negotiate a broader range of terrains. The range of surfaces that might be required to be negotiated in space can be matched by the range of different possible climbing strategies. In particular, dry adhesive techniques inspired by the gecko are highlighted, with different robot designs intended to take advantage of such dry adhesives described in detail. Examples of other strategies for climbing robots are presented and discussed, particularly in the context of usefulness for future implementation in space. With this section introducing the subject and composition of this chapter, section 2 gives a brief overview of the main challenges facing robotic systems in space. Section 3 is devoted to the introduction of different strategies for climbing robots, including examples designed for use in space. Examples of robotic systems employing different approaches are given. In section 4, the subject of biologically inspired synthetic dry adhesion is introduced. Section 5 forms the major part of this chapter, describing the design and breadboarding of three climbing robots, intended to form the basis for future robots using such dry adhesives. Future work is discussed in section 6 and conclusions are given in section 7.
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