Title :
Grasp recognition strategies from empirical models
Author :
Kang, Dukhyun ; Goldberg, Kenneth Y.
Author_Institution :
Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
A method for recognizing a part from a set of known parts using a parallel-jaw gripper and a simple sensor that measures the distance between the jaws is described. The authors consider how empirical measurements of part behavior can be used to generate efficient recognition strategies. These strategies are compared to random strategies using physical experiments. It is found that the former cut error rates and recognition times by approximately 50%
Keywords :
assembling; computer vision; industrial manipulators; path planning; efficient recognition strategies; empirical models; error rates; grasp recognition strategies; parallel-jaw gripper; part recognition; recognition times; simple sensor; Grippers; Intelligent robots; Intelligent sensors; Manufacturing automation; Noise measurement; Sensor systems; Shape; Solid modeling; Strategic planning; Virtual manufacturing;
Conference_Titel :
Robotics and Automation, 1993. Proceedings., 1993 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-8186-3450-2
DOI :
10.1109/ROBOT.1993.292214
