Carbon nanotube/glycerol embedded low cost flexible sensor for large deflection sensing of continuum manipulators

Author(s):  
SAPTAK BHATTACHERJEE ◽  
Sananda Chatterjee ◽  
Subhasis Bhaumik

Abstract Large deflection sensing is highly crucial for proper positioning and control of continuum robots during robot assisted minimally invasive surgery. Existing techniques suffer from eletromagnetic noise susceptibility, harmful radiation exposure, limited range, bio-incompatibility and necessity of expensive instruments. In the present study, we propose a Multi-Walled Carbon Nano-Tube (MWCNT)/polyglycerol based low cost, flexible and biocompatible sensor which could allow safer, faster and accurate angular deflection measurement of continuum robots for biomedical applications. Experimental results demonstrate that the sensor is stretchable upto 100% , provides a gauge factor upto 11.65, have response time around 8 ms, durability of -0.14% for cyclic loading and unloading and show very small creep upto ±0.0008 ( ±2.88%). Furthermore, the sensor can measure continuum robot deflection upto ±150 o with a sensitivity of 666.67 ohms/degree, with a maximum error of 1.67% and maximum hysteresis of 1.41%. Thus, wide range, low cost, fast response, and biocompatibility justify the potential of the proposed sensor for large deflection sensing of continuum robots during robot assisted minimally invasive surgery.

Author(s):  
Hang Su ◽  
Andrea Mariani ◽  
Salih Ertug Ovur ◽  
Arianna Menciassi ◽  
Giancarlo Ferrigno ◽  
...  

Author(s):  
Wen Qi ◽  
Hang Su ◽  
Ke Fan ◽  
Ziyang Chen ◽  
Jiehao Li ◽  
...  

The generous application of robot-assisted minimally invasive surgery (RAMIS) promotes human-machine interaction (HMI). Identifying various behaviors of doctors can enhance the RAMIS procedure for the redundant robot. It bridges intelligent robot control and activity recognition strategies in the operating room, including hand gestures and human activities. In this paper, to enhance identification in a dynamic situation, we propose a multimodal data fusion framework to provide multiple information for accuracy enhancement. Firstly, a multi-sensors based hardware structure is designed to capture varied data from various devices, including depth camera and smartphone. Furthermore, in different surgical tasks, the robot control mechanism can shift automatically. The experimental results evaluate the efficiency of developing the multimodal framework for RAMIS by comparing it with a single sensor system. Implementing the KUKA LWR4+ in a surgical robot environment indicates that the surgical robot systems can work with medical staff in the future.


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