SUMMARY

Robotic grasping is a significant area of study, and reduction in its mechanical and control complexity is of great interest for use in wearable devices, teleoperated devices, as well as fully autonomous devices. This work aims to demonstrate efficient and reliable methods for mechanical complexity reduction using fully characterized soft robotics and advanced modeling techniques, and to combine that reduction with a methodology for reducing the dimensionality of the control problem using techniques based on synergy control and a novel method for generating an optimal synergy matrix for an arbitrary gripper. The major goals of this work are to: 1. Study muscle-like soft actuators and characterize properties to provide a foundation for using compliant muscle-like actuators. 2. Develop a soft secondary mechanism that would enable synergy control in underactuated gripper by changing the joint interaction properties, then fully characterizing and modeling this system to simulate it as a series of rigid bodies. 3. Estimate and optimize synergy matrix without mapping, and determine costs and benefits to grasp planning and performance.