This invention is a novel approach to tactile perception in robotic assisted minimally invasive surgery (RAMIS), focusing on deformation and texture detection. The invention consists of algorithms that aim to accurately classify objects with varying softness and roughness into corresponding deformation or texture labels. By designing and implementing a sensor-based system which comprises feature extraction and a recognition module, the surgeon can detect texture (the roughness of a biological organ on its surface) and deformation (tissue hardness / softness). It integrates two microelectromechanical systems (MEMS) sensors and a force-sensitive resistor (FSR) sensor attached to a thoracic grasper instrument. This innovation is compatible with the da Vinci MIS surgical system. Background: Tactile feedback is crucial in RAMIS, especially for surgeons palpating subsurface tumors and other organ structures. This research introduces a novel approach to tactile perception in RAMIS, focusing on deformation and texture detection. The robotic technology employed in RAMIS allows for greater precision, flexibility, and control compared to traditional methods. However, even with its significant advancements in surgical practice, the lack of haptic feedback is a huge setback. Unlike traditional surgery, where surgeons can feel the tissues that they are manipulating, RAMIS currently lacks haptic feedback. This can make it more challenging for surgeons to assess tissue characteristics and apply the appropriate amount of force. By providing detailed tactile feedback, this sensor system allows surgeons to differentiate between various tissue types and textures, improving the accuracy of surgical manipulations. The sensor's compatibility with the widely used da Vinci Surgical System ensures that it can be readily adopted without significant modifications to existing surgical tools. Applications:
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