The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated in vivo inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of 93 % and 88.1 % on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and corresponding classified tissues.

In this paper, with the goal of addressing the lack of tactile feedback in colorectal cancer (CRC) polyps diagnosis using a colonoscopy procedure, we propose the design and fabrication of a novel soft and inflatable strain-based tactile sensing balloon (SI-STSB). The proposed soft sensor features a unique stretchable sensing layer - that utilizes a liquid metal injected within spiral-shape microchannels of a stretchable substrate - and is integrated with a unique inflatable balloon mechanism. The proposed SI-STSB has been thoroughly characterized through different calibration experiments. Results demonstrate a phenomenal adjustable sensitivity with low hysteresis behavior under different experimental conditions for this sensor making it a great candidate for enhancing the existing diagnosis procedures.

Haptic organs are common in nature and help animals to navigate environments where vision is not possible. Insects often use slender, lightweight, and flexible links as sensing antennae. These antennae have a muscle-endowed base that changes their orientation and an organ that senses the applied force and moment, enabling active sensing. Sensing antennae detect obstacles through contact during motion and even recognize objects. They can also push obstacles. In all these tasks, force control of the antenna is crucial. The objective of our research is to develop a haptic robotic system based on a sensing antenna, consisting of a very lightweight and slender flexible rod. In this context, the work presented here focuses on the force control of this device. To achieve this, (a) we develop a dynamic model of the antenna that moves under gravity and maintains point contact with an object, based on lumped-mass discretization of the rod; (b) we prove the robust stability property of the closed-loop system using the Routh stability criterion; and (c) based on this property, we design a robust force control system that performs efficiently regardless of the contact point with the object. We built a mechanical device replicating this sensing organ. It is a flexible link connected at one end to a 3D force-torque sensor, which is attached to a mechanical structure with two DC motors, providing azimuthal and elevation movements to the antenna. Our experiments in contact situations demonstrate the effectiveness of our control method.

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end-effector. The fibrescope was implemented to ensure the device\'s compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas-Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required.

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform-1D, 2D, or 3D-would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.

Tactile sensors play an important role when robots perform contact tasks, such as physical information collection, force or displacement control to avoid collision. For these manipulations, excessive contact may cause damage while poor contact cause information loss between the robotic end-effector and the objects. Inspired by skin structure and signal transmission method, this paper proposes a tactile sensing system based on the self-sensing soft pneumatic actuator (S-SPA) capable of providing tactile sensing capability for robots. Based on the adjustable height and compliance characteristics of the S-SPA, the contact process is safe and more tactile information can be collected. And to demonstrate the feasibility and advantage of this system, a robotic hand with S-SPAs could recognize different textures and stiffness of the objects by touching and pinching behaviours to collect physical information of the various objects under the positive work states of the S-SPA. The result shows the recognition accuracy of the fifteen texture plates reaches 99.4%, and the recognition accuracy of the four stiffness cuboids reaches 100%by training a KNN model. This safe and simple tactile sensing system with high recognition accuracies based on S-SPA shows great potential in robotic manipulations and is beneficial to applications in domestic and industrial fields.

Creating bionic intelligent robotic systems that emulate human-like skin perception presents a considerable scientific challenge. This study introduces a multifunctional bionic electronic skin (e-skin) made from polyacrylic acid ionogel (PAIG), designed to detect human motion signals and transmit them to robotic systems for recognition and classification. The PAIG is synthesized using a suspension of liquid metal and graphene oxide nanosheets as initiators and cross-linkers. The resulting PAIGs demonstrate excellent mechanical properties, resistance to freezing and drying, and self-healing capabilities. Functionally, the PAIG effectively captures human motion signals through electromechanical sensing. Furthermore, a bionic intelligent sorting robot system is developed by integrating the PAIG-based e-skin with a robotic manipulator. This system leverages its ability to detect frictional electrical signals, enabling precise identification and sorting of materials. The innovations presented in this study hold significant potential for applications in artificial intelligence, rehabilitation training, and intelligent classification systems.

Liquid crystal elastomers (LCEs), as a classical two-way shape-memory material, are good candidates for developing artificial muscles that mimic the contraction, expansion, or rotational behavior of natural muscles. However, biomimicry is currently focused more on the actuation functions of natural muscles dominated by muscle fibers, whereas the tactile sensing functions that are dominated by neuronal receptors and synapses have not been well captured. Very few studies have reported the sensing concept for LCEs, but the signals were still donated by macroscopic actuation, that is, variations in angle or length. Herein, we develop a conductive porous LCE (CPLCE) using a solvent (dimethyl sulfoxide (DMSO))-templated photo-cross-linking strategy, followed by carbon nanotube (CNT) incorporation. The CPLCE has excellent reversible contraction/elongation behavior in a manner similar to the actuation functions of skeletal muscles. Moreover, the CPLCE shows excellent pressure-sensing performance by providing real-time electrical signals and is capable of microtouch sensing, which is very similar to natural tactile sensing. Furthermore, macroscopic actuation and tactile sensation can be integrated into a single system. Proof-of-concept studies reveal that the CPLCE-based artificial muscle is sensitive to external touch while maintaining its excellent actuation performance. The CPLCE with tactile sensation beyond reversible actuation is expected to benefit the development of versatile artificial muscles and intelligent robots.

Tactile sensing requires integrated detection platforms with distributed and highly sensitive haptic sensing capabilities along with biocompatibility, aiming to replicate the physiological functions of the human skin and empower industrial robotic and prosthetic wearers to detect tactile information. In this regard, short peptide-based self-assembled hydrogels show promising potential to act as bioinspired supramolecular substrates for developing tactile sensors showing biocompatibility and biodegradability. However, the intrinsic difficulty to modulate the mechanical properties severely restricts their extensive employment. Herein, by controlling the self-assembly of 9-fluorenylmethoxycarbonyl-modifid diphenylalanine (Fmoc-FF) through introduction of polyethylene glycol diacrylate (PEGDA), wider nanoribbons are achieved by untwisting from well-established thinner nanofibers, and the mechanical properties of the supramolecular hydrogels can be enhanced 10-fold, supplying bioinspired supramolecular encapsulating substrate for tactile sensing. Furthermore, by doping with poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and 9-fluorenylmethoxycarbonyl-modifid 3,4-dihydroxy-l-phenylalanine (Fmoc-DOPA), the Fmoc-FF self-assembled hydrogels can be engineered to be conductive and adhesive, providing bioinspired sensing units and adhesive layer for tactile sensing applications. Therefore, the integration of these modules results in peptide hydrogelation-based tactile sensors, showing high sensitivity and sustainable responses with intrinsic biocompatibility and biodegradability. The findings establish the feasibility of developing programmable peptide self-assembly with adjustable features for tactile sensing applications.

Replication of the human sense of touch would be highly advantageous for robots or prostheses as it would allow an agile and dexterous interaction with the environment. The article presents an approach for the integration of a micro-electromechanical system sensing skin with 144 tactile sensors on a soft, human-sized artificial fingertip. The sensing technology consists of thin, 1D sensing strips which are wrapped around the soft and curved fingertip. The sensing strips include 0.5 mm diameter capacitive sensors which measure touch, vibrations, and strain at a resolution of 1 sensor/mm2. The method allows to leverage the advantages of sensing skins over other tactile sensing technologies while showing a solution to integrate such skins on a soft three-dimensional body. The adaptable sensing characteristics are dominated by the thickness of a spray coated silicone layer, encapsulating the sensors in a sturdy material. We characterized the static and dynamic sensing capabilities of the encapsulated taxels up to skin thicknesses of 600 μm. Taxels with 600 μm skin layers have a sensitivity of 6 fF/mN, corresponding to an ∼5 times higher sensitivity than a human finger if combined with the developed electronics. They can detect vibrations in the full tested range of 0-600 Hz. The softness of a human finger was measured to build an artificial sensing finger of similar conformity. Miniaturized readout electronics allow the readout of the full finger with 220 Hz, which enables the observation of touch and slipping events on the artificial finger, as well as the estimation of the contact force. Slipping events can be observed as vibrations registered by single sensors, whereas the contact force can be extracted by averaging sensor array readouts. We verified the sturdiness of the sensing technology by testing single coated sensors on a chip, as well as the completely integrated sensing fingertip by applying 15 N for 10,000 times. Qualitative datasets show the response of the fingertip to the touch of various objects. The focus of this article is the development of the sensing hardware and the basic characterization of the sensing performance.