Fuzzy and neural network control of object acquisition for power grasp

Mark D. Hanes; David E. Orin; Stanley C. Ahalt

doi:10.1117/12.235918

22 March 1996 Fuzzy and neural network control of object acquisition for power grasp

Mark D. Hanes, David E. Orin, Stanley C. Ahalt

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235918
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

There exist numerous examples of grasping tasks in which stable grasping in the presence of uncertainties is needed. Applications such as underwater rescue and salvage and space exploration call for grasping of poorly specific objects in unstructured environments. A fully enveloping grasp, also called a power grasp, appears well suited for stable grasping in such unstructured environments. This is because a power grasp is characterized by inherent stability, increased weight handling capability, and resilience to modeling errors. However, research into power grasps has lagged behind research of precision or fingertip grasps for a variety of reasons, as discussed briefly in this paper. The primary objective of a power grasp system is to provide a stable grasp capable of withstanding external perturbations without crushing or otherwise damaging the object grasped. The system should also satisfy additional objectives, including allowances for inaccurate object specifications and other system inaccuracies such as compliant effects in the joints due to large focus, actuator modeling errors, and force sensor noise and modeling errors. This paper reports on progress towards meeting these objectives using an intelligent control architecture consisting of both adaptive fuzzy control techniques and artificial neural networks, along with traditional kinematic and impedance control algorithms.

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Mark D. Hanes, David E. Orin, and Stanley C. Ahalt "Fuzzy and neural network control of object acquisition for power grasp", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235918

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