Pneumatic-based methods for force sensing and contact detection in telemanipulation tasks

Teleoperation frameworks require a rich flow of information from the slave to the master side to be effective. While visual information is usually provided in any master-slave system, haptic perception is often missing. Despite the importance of the sense of touch while performing surgical procedures, minimally invasive robotic surgery still lacks the reproduction of haptic stimuli at the master side, mostly due to the difficulties in measuring forces at the contact site. However, there could be also procedures in open surgery in which a reduction of the natural haptic perception may occur, for example because vibrations generated by the surgical tool affect the surgeon’s perception. This thesis addresses the challenge of measuring forces between surgical instruments and patient’s tissues both in robotic and open surgery, presenting innovative pneumatic force sensors that rely on pressure variations inside one or more chambers. Performance comparisons with accurate commercial force sensors proved the feasibility and effectiveness of the proposed approaches. Besides, many advantages can be appreciated in terms of size, cost, biocompatibility, possibility of changing sensor features such as stiffness according to the application, and absence of electronic components into the patient’s body. The achieved results paved the way towards the exploitation of novel pneumatic-based devices for contact detection in more general robotic manipulation scenarios. In this context, a pneumatic device has been proposed to create soft inclusions in the environment that can be used by rigid grippers to achieve safer grasps. The use of soft sensing modules allows us to detect the contact between the gripper and the environment during grasp approach, and to estimate the approximate location and weight distribution of the object to be grasped. A system that combines the precision of rigid grippers with the adaptability of pneumatic-based devices was developed and successfully tested in grasping tasks with several different objects, taking advantage of the strengths of both rigid and soft robotics approaches.