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.

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.

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.