Data-driven optimization for underactuated robotic hands
Ciocarlie
Matei
author
Columbia University. Computer Science
Allen
Peter K.
author
Columbia University. Computer Science
Columbia University. Computer Science
originator
text
Articles
2010
English
Passively adaptive and underactuated robotic hands have shown the potential to achieve reliable grasping in unstructured environments without expensive mechanisms or sensors. Instead of complex run-time algorithms, such hands use design-time analysis to improve performance for a wide range of tasks. Along these directions, we present an optimization framework for underactuated compliant hands. Our approach uses a pre-defined set of grasps in a quasistatic equilibrium formulation to compute the actuation mechanism design parameters that provide optimal performance. We apply our method to a class of tendon-actuated hands; for the simplified design of a two-fingered gripper, we show how a global optimum for the design optimization problem can be computed. We have implemented the results of this analysis in the construction of a gripper prototype, capable of a wide range of grasping tasks over a variety of objects.
Robotics
2010 IEEE International Conference on Robotics and Automation
Piscataway, N.J.
IEEE
2010
1292
1299
http://dx.doi.org/10.1109/ROBOT.2010.5509793
http://hdl.handle.net/10022/AC:P:15086
NNC
NNC
2012-10-25 15:28:09 -0400
2012-10-25 15:35:58 -0400
9084
eng