Abstract
There is a need for a versatile system with integration between microtechnologies and sensing techniques to retrieve information feedback about the physical parameters of an object in the emerging areas of minimally invasive surgery. These methods have been applied to a pneumatic balloon actuated cantilever beam sensor in order to design amicro-scale gripping device and to further develop upon the existing need for sensory feedback. The pneumatic actuation produces displacement together with a large force by a deformation of the beam under a pressure supply. When actuated the gripper rig is able to grasp objects. The force exerted on the object is measured by the pressure of the air in the balloon
and used to control the driving system of the gripper. Neural networks that are able to predict contact of the object, object position, size and stiffness have been constructed, this could help surgeons to detect tissue properties in minimally invasive surgical procedures. The results from the
gripper proved extremely accurate in their predictions, they were able to
predict object position within 98.6% accuracy and object diameter within
90% accuracy, also the gripper is able to discriminate between four
different examples of object stiffness. The results from the large gripper and further analysis have been used to make recommendations for the
design of the gripper on the micro-scale.
Date of Award | Feb 2007 |
---|---|
Original language | English |
Awarding Institution |
|
Keywords
- Pneumatic balloon actuated cantilever beam
- Invasive surgery