The Development of a Flexible Sensor for Continuum Soft-Bodied Robots

In this thesis, we investigate, develop, and verify an approach to sense over soft and flexible materials based on the use of a tomographic technique known as Electrical Impedance Tomography

Touch and deformation perception of soft manipulators with capacitive e-skins and deep learning

Tactile sensing in soft robots remains particularly challenging because of the coupling between contact and deformation information which the sensor is subject to during actuation and interaction with the environment. This often results in severe interference and makes disentangling tactile sensing and geometric deformation difficult. To address this problem, this paper proposes a soft capacitive e-skin with a sparse electrode distribution and deep learning for information decoupling. Our approach successfully separates tactile sensing from geometric deformation, enabling touch recognition on a soft pneumatic actuator subject to both internal (actuation) and external (manual handling) forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\% accuracy in touch recognition across a range of deformations. When complemented with prior knowledge, a transformer-based architecture effectively tracks the deformation of the soft actuator. The average distance error in positional reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under operative conditions with different inflation states and physical contacts which lead to additional signal variations and consequently interfere with deformation tracking. These findings represent a tangible way forward in the development of e-skins that can endow soft robots with proprioception and exteroception

TACTILE SENSING WITH COMPLIANT STRUCTURES FOR HUMAN-ROBOT INTERACTION

This dissertation presents the research on tactile sensing with compliant structures towards human-robot interaction. It would be beneficial for robots working collaboratively with humans to be soft or padded and have compliant tactile sensing skins over the padding. To allow the robots to interact with humans via touch effectively and safely and to detect tactile stimuli in an unstructured environment, new tactile sensing concepts are needed that can detect a wide range of potential interactions and sense over an area. However, most highly sensitive tactile sensors are unable to cover the forces involved in human contacts, which ranges from 1 newton to thousand newtons; to implement area sensing capabilities, there have been challenges in creating traditional sensing arrays, where the associated supporting electronics become more complex with an increasing number of sensing elements. This dissertation develops a novel multi-layer cutaneous tactile sensing architecture for enhanced sensitivity and range, and employs an imaging technique based on boundary measurements called electrical impedance tomography (EIT) to achieve area tactile sensing capabilities. The multi-layer cutaneous tactile sensing architecture, which consists of stretchable piezoresistive strain-sensing layers over foam padding layers of different stiffness, allows for both sufficient sensitivity and an extended force range for human contacts. The role that the padding layer plays when placed under a stretchable sensing layer was investigated, and it was discovered that the padding layer magnifies the sensor signal under indentation compared to that obtained without padding layers. The roles of the multi-layer foams were investigated by changing stiffness and thickness, which allows tailoring the response of multi-layer architectures for different applications. To achieve both extended force range and distributed sensing, EIT technique was employed with the multi-layer sensing architecture. Machine and human touch were conducted on the developed multi-layer sensing system, revealing that the second sensing skin is required to detect the large variability in human touch. Although widely applied in the medical field for functional imaging, EIT applied in tactile sensing faces different challenges, such as unknown number and region of tactile stimuli. Current EIT tactile sensors have focused on qualitative demonstration. This dissertation aims at achieving quantitative information from piezoresistive EIT tactile sensors, by investigating spatial performance and the effect of sensor’s conductivity. A spatial correction method was developed for obtaining consistent spatial information, which was validated by both simulation and experiments from our stretchable piezoresistive EIT sensor with an underlying padding layer

Development of a practical electrical tomography system for flexible contact sensing applications

Tactile sensing is seeing an increase in potential applications, such as in humanoid and industrial robots; health care systems and medical instrumentation; prosthetic devices; and in the context of human-machine interaction. However, these applications require the integration of tactile sensors over various objects with different surface shapes. This emphasises the need of developing sensors which are flexible in contrast with the common rigid type. Moreover, flexible sensing research is considered to be in its infancy. Many technological and system issues are still open, mainly: conformability; scalability; system integration; high system cost; sensor size; and power consumption. In light of the above, this thesis is concerned with the development of a flexible fabric-based contact sensor system. This is done through an interdisciplinary approach whereby electronics, system engineering, electrical tomography, and machine learning have been considered. This results in a practical flexible sensor that is capable of accurately detecting contact locations with high temporal resolution; and requires low power consumption.The sensor is based on the principle of electrical tomography. This is essential since this technique allows us to eliminate electrodes and wiring from within the sensing area, confining them to the periphery of the sensor. This improves flexibility all while eliminating electrode fatigue and deterioration due to repeated loading.We start by developing an electrical tomography sensor system. This comprises of a piezoresistive flexible fabric material, a data acquisition card, and a custom printed circuit board for managing both current injection and data collection. We show that current injection and voltage measurement protocols respond differently to different positions of the input contact region of interest, consequently affecting the overall performance of the tomography sensor system. Then, an approach for classifying contact location over the sensor is presented. This is done using supervised machine learning, namely discriminant analysis. Accurate touch location identification is achieved, along with an increase in the detection speed and sensor versatility. Finally, the sensor is placed over different surfaces in order to show and validate its efficiency. The main finding of this work is that electrical tomography flexible sensor systems present a very promising technology, and can be practically and effectively used for developing inexpensive and durable flexible sensors for tactile applications. The main advantage of this approach is the complete absence of wires in the internal area of the sensor. This allows the sensor to be placed over surfaces with different shapes without losing its functionality. The sensor's applicability can be further improved by using machine learning strategies due to their ability of empirical learning and extracting meaningful tactile information. The research work in this thesis was motivated by the problems faced by industrial partners which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e)