In recent years we have seen tremendous progress in the development of robotic solutions for minimally invasive surgery (MIS). Indeed, a number of robot-assisted MIS systems have been developed to product level and are now well-established clinical tools; Intuitive Surgical’s very successful da Vinci Surgical System a prime example. The majority of these surgical systems are based on the traditional rigid-component robot design that was instrumental in the third industrial revolution—especially within the manufacturing sector. However, the use of this approach for surgical procedures on or around soft tissue has come under increasing criticism. The dangers of operating with a robot made from rigid components both near and within a patient are considerable. The EU project STIFF-FLOP, arguably the first large-scale research programme on soft robots for MIS, signalled the start of a concerted effort among researchers to investigate this area more comprehensively. While soft robots have many advantages over their rigid-component counterparts, among them high compliance and increased dexterity, they also bring their own specific challenges when interacting with the environment, such as the need to integrate sensors (which also need to be soft) that can determine the robot’s position and orientation (pose). In this study, the challenges of sensor integration are explored, while keeping the surgeon’s perspective at the forefront of our discussion. The paper critically explores a range of methods, predominantly those developed during the EU project STIFF-FLOP, that facilitate the embedding of soft sensors into articulate soft robot structures using flexible, optics-based light guides. We examine different optics-based approaches to pose perception in a minimally invasive surgery settings, and methods of integration are also discussed.

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Friction welding is one of the foremost welding processes for similar and dissimilar metals. Previously, the process has been modeled utilizing the rudimentary techniques of constant friction and
slip-stick friction. The motivation behind this article is to present a new characteristic for temperature profile estimation in modeling of the rotary friction welding process. For the first time, a unified model has been exhibited, with an implementation of the phase transformation of similar and dissimilar materials. The model was generated on COMSOL Multiphysics^® and thermal and structural modules were used to plot the temperature curve. The curve for the welding of dissimilar metals using the model was generated, compared and analyzed with that of practical curves already acquired through experimentation available in the literature, and then the effect of varying the parameters on the welding of similar metals was also studied.

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Sensing and localising pressure resulting from physical interaction between a robot and its environment is a key requirement in the deployment of soft robots in reallife scenarios. In order to adapt the robot’s behaviour in realtime, we argue that sensors must have a high sampling rate. In this paper, we present a novel tactile sensing strategy for soft sensors, based on an imaging technique known as optical tomography. Instead of transmitting light through the soft sensor in a sequential way (as commonly done in tomography systems), we demonstrate that concurrently illuminating the sensor with multiple light sources and reading out the sensor response has several advantages. Firstly, it drastically increases the sampling rate of the sensor when compared to standard tomography approaches, making it more suitable to sense sudden and short-lived contacts. Secondly, by concurrently switching on the light sources, we increase performance in terms of pressure localisation and pressure estimation achieved through Machine Learning techniques. We carry out experiments demonstrating that our approach allows for a robust pressure estimation and contact point localisation with an accuracy up to 91:1% (vs 70:3%) at a higher sampling rate.

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Thin and imperceptible soft skins that can detect internal deformations as well as external forces, can go a long way to address perception and control challenges in soft robots. However, decoupling proprioceptive and exteroceptive stimuli is a challenging task. In this paper, we present a silicone-based, capacitive E-skin for exteroception and proprioception (SCEEP). This soft and stretchable sensor can perceive stretch as along with touch at 100 different points via its 100 tactels. In this paper, we present a novel algorithm that decouples global strain from local indentations due to external forces. The soft skin is 10.1cm in length and 10cm in width and can be used to accurately measure the global strain of up to 25% with an error of under 3%; while at the same time, can determine the amplitude and position of local indentations. This is a step towards a fully soft electronic skin that can act as a proprioceptive sensor to measure internal states while measuring external forces.

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In this extended abstract, we present a soft stretchable multi-modal capacitive skin sensor that can be used for exteroception and proprioception in soft surgical manipulators. A soft skin prototype was made using Ecoflex, embedding three conductive carbon grease terminal layers. This soft skin is capable of measuring stretch and touch simultaneously. The soft skin measures uniaxial stretches from 1 to 1.2475 within an error range of 2.6% and can also quantify as well as localize local indentation. An algorithm is developed that decouples local change, i.e., due to indentation, from global strain, due to stretch. An experimental study was conducted; results are presented.

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Soft sensors are crucial to enable feedback in soft robots. Soft capacitive sensing is a reliable technology that can be embedded into soft pneumatic robots for obtaining proprioceptive and exteroceptive feedback. In this paper, we model a soft capacitive sensor that measures both the actuated state as well as applied external forces. We develop a Finite Element Model using a multiphysics software (COMSOL®). Using this model, we investigate the change in capacitance with the application of external force, for a range of different internal pressures and strains. We hope this study is helpful in understanding the coupling of internal inputs and external stimuli on the feedback obtained from the sensors and help us design better sensory systems for soft robots.

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