Paper
1 March 1991 Intelligent grasp planning strategy for robotic hands
Ian David Walker, John B. Cheatham, Jr., Yu-Che Chen
Author Affiliations +
Abstract
Providing robot hands with the intelligence required for effective multifinger grasping is a difficult problem. In particular, a good choice of grasp points on an object to be manipulated is critical in order to achieve dexterous manipulation. A methodology and algorithmic implementation is proposed for the choice of the feasible grasp points on irregular objects. The main thrust of this analysis is the embedding of grasp mechanics - namely intuitive human-like force distribution and computational efficiency - into a practical methodology for intelligent grasp planning. The strategy of grasp reasoning is to plan the grasp so that the fingers will be guaranteed to firmly grasp the object during handling and generate fine motion to perform tasks using the same planned grasp points. We also require the ability to change some of the grasp positions while holding the object firmly. The key to our approach is that the reasoning is based on a solid mathematical model of grasp mechanics - information from the mechanics of differing grasp candidates is automatically included at the grasp strategy stage. Therefore, our grasp has the advantage of being intelligence-based without sacrificing the physics of the grasp. Additionally, our underlying method for grasp mechanics is designed to be simple, computationally efficient, and intuitively natural, and can be easily employed in real time. Thus the grasp chosen is one that feels right, and is also based on solid physical principles, combining the intelligence of the expert with the mechanics needed for precise control.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ian David Walker, John B. Cheatham, Jr., and Yu-Che Chen "Intelligent grasp planning strategy for robotic hands", Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); https://doi.org/10.1117/12.45535
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Cited by 1 scholarly publication.
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KEYWORDS
Mechanics

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Quality measurement

Artificial intelligence

Image segmentation

Solids

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