Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics

A retrofit sensing strategy for soft fluidic robots

Soft robots are intrinsically capable of adapting to different environments by changing their shape in response to interaction forces. However, sensory feedback is still required for higher level decisions. Most sensing technologies integrate separate sensing elements in soft actuators, which presents a considerable challenge for both the fabrication and robustness of soft robots. Here we present a versatile sensing strategy that can be retrofitted to existing soft fluidic devices without the need for design changes. We achieve this by measuring the fluidic input that is required to activate a soft actuator during interaction with the environment, and relating this input to its deformed state. We demonstrate the versatility of our strategy by tactile sensing of the size, shape, surface roughness and stiffness of objects. We furthermore retrofit sensing to a range of existing pneumatic soft actuators and grippers. Finally, we show the robustness of our fluidic sensing strategy in closed-loop control of a soft gripper for sorting, fruit picking and ripeness detection. We conclude that as long as the interaction of the actuator with the environment results in a shape change of the interval volume, soft fluidic actuators require no embedded sensors and design modifications to implement useful sensing.

The Role of 3D Printing Technologies in Soft Grippers

Soft grippers are essential for precise and gentle handling of delicate, fragile, and easy-to-break objects, such as glassware, electronic components, food items, and biological samples, without causing any damage or deformation. This is especially important in industries such as healthcare, manufacturing, agriculture, food handling, and biomedical, where accuracy, safety, and preservation of the objects being handled are critical. This article reviews the use of 3D printing technologies in soft grippers, including those made of functional materials, nonfunctional materials, and those with sensors. 3D printing processes that can be used to fabricate each class of soft grippers are discussed. Available 3D printing technologies that are often used in soft grippers are primarily extrusion-based printing (fused deposition modeling and direct ink writing), jet-based printing (polymer jet), and immersion printing (stereolithography and digital light processing). The materials selected for fabricating soft grippers include thermoplastic polymers, UV-curable polymers, polymer gels, soft conductive composites, and hydrogels. It is conclude that 3D printing technologies revolutionize the way soft grippers are being fabricated, expanding their application domains and reducing the difficulties in customization, fabrication, and production.

A review on model-based and model-free approaches to control soft actuators and their potentials in colonoscopy

Colorectal cancer (CRC) is the third most common cancer worldwide and responsible for approximately 1 million deaths annually. Early screening is essential to increase the chances of survival, and it can also reduce the cost of treatments for healthcare centres. Colonoscopy is the gold standard for CRC screening and treatment, but it has several drawbacks, including difficulty in manoeuvring the device, patient discomfort, and high cost. Soft endorobots, small and compliant devices thatcan reduce the force exerted on the colonic wall, offer a potential solution to these issues. However, controlling these soft robots is challenging due to their deformable materials and the limitations of mathematical models. In this Review, we discuss model-free and model-based approaches for controlling soft robots that can potentially be applied to endorobots for colonoscopy. We highlight the importance of selecting appropriate control methods based on various parameters, such as sensor and actuator solutions. This review aims to contribute to the development of smart control strategies for soft endorobots that can enhance the effectiveness and safety of robotics in colonoscopy. These strategies can be defined based on the available information about the robot and surrounding environment, control demands, mechanical design impact and characterization data based on calibration.

Sensing in Soft Robotics

Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.

Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits

Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.

Hybrid Device Fabrication Using Roll-to-Roll Printing for Personal Environmental Monitoring

Roll-to-roll (R2R) printing methods are well known as additive, cost-effective, and ecologically friendly mass-production methods for processing functional materials and fabricating devices. However, implementing R2R printing to fabricate sophisticated devices is challenging because of the efficiency of material processing, the alignment, and the vulnerability of the polymeric substrate during printing. Therefore, this study proposes the fabrication process of a hybrid device to solve the problems. The device was created so that four layers, composed of polymer insulating layers and conductive circuit layers, are entirely screen-printed layer by layer onto a roll of polyethylene terephthalate (PET) film to produce the circuit. Registration control methods were presented to deal with the PET substrate during printing, and then solid-state components and sensors were assembled and soldered to the printed circuits of the completed devices. In this way, the quality of the devices could be ensured, and the devices could be massively used for specific purposes. Specifically, a hybrid device for personal environmental monitoring was fabricated in this study. The importance of environmental challenges to human welfare and sustainable development is growing. As a result, environmental monitoring is essential to protect public health and serve as a basis for policymaking. In addition to the fabrication of the monitoring devices, a whole monitoring system was also developed to collect and process the data. Here, the monitored data from the fabricated device were personally collected via a mobile phone and uploaded to a cloud server for additional processing. The information could then be utilized for local or global monitoring purposes, moving one step toward creating tools for big data analysis and forecasting. The successful deployment of this system could be a foundation for creating and developing systems for other prospective uses.

Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors

Sensing the shape of continuum soft robots without obstructing their movements and modifying their natural softness requires innovative solutions. This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception. Magnetic sensors are compact, sensitive, and easy to integrate into a soft robot. We also propose a neural architecture to make sense of the highly nonlinear relationship between the perceived intensity of the magnetic field and the shape of the robot. By injecting a priori knowledge from the kinematic model, we obtain an effective yet data-efficient learning strategy. We first demonstrate in simulation the value of this kinematic prior by investigating the proprioception behavior when varying the sensor configuration, which does not require us to re-train the neural network. We validate our approach in experiments involving one soft segment containing a cylindrical magnet and three magnetoresistive sensors. During the experiments, we achieve mean relative errors of 4.5%.

A Learning-Based Approach to Sensorize Soft Robots

Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.

Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.

Design, Implementation, and Kinematics of a Twisting Robot Continuum Arm Inspired by Human Forearm Movements

In this article, a soft robot arm that has the ability to twist in two directions is designed. This continuum arm is inspired by the twisting movements of the human upper limb. In this novel continuum arm, two contractor pneumatic muscle actuators (PMA) are used in parallel, and a self-bending contraction actuator (SBCA) is laid between them to establish the twisting movement. The proposed soft robot arm has additional features, such as the ability to contract and bend in multiple directions. The kinematics for the proposed arm is presented to describe the position of the distal end centre according to the dimensions and positions of the actuators and the bending angle of the SBCA in different pressurized conditions. Then, the rotation behaviour is controlled by a high precision controller system.

A binder jet 3D printed MXene composite for strain sensing and energy storage application

Polymer composite materials have been proven to have numerous electrical related applications ranging from energy storage to sensing, and 3D printing is a promising technique to fabricate such materials with a high degree of freedom and low lead up time. Compared to the existing 3D printing technique for polymer materials, binder jet (BJ) printing offers unique advantages such as a fast production rate, room temperature printing of large volume objects, and the ability to print complex geometries without additional support materials. However, there is a serious lack of research in BJ printing of polymer materials. In this work we introduce a strategy to print poly(vinyl alcohol) composites with MXene-surfactant ink. By ejecting highly conductive MXene particles onto a PVOH matrix, the resulting sample achieved conductive behaviour in the order of mS m^-1 with demonstrated potential for strain sensing and energy storage. This work demonstrates that BJ printing has the potential to directly fabricate polymer composite materials with different end applications.

3D Printing Polymeric Materials for Robots with Embedded Systems

The fabrication of robots and their embedded systems is challenging due to the complexity of the interacting components. The integration of additive manufacturing (AM) to robotics has made advancements in robotics manufacturing through sophisticated and state-of-the-art AM technologies and materials. With the emergence of 3D printing, 3D printing materials are also being considered and engineered for specific applications. This study reviews different 3D printing materials for 3D printing embedded robotics. Materials such as polyethylene glycol diacrylate (PEGDA), acrylonitrile butadiene styrene (ABS), flexible photopolymers, silicone, and elastomer-based materials were found to be the most used 3D printing materials due to their suitability for robotic applications. This review paper revealed that the key areas requiring more research are material formulations for improved mechanical properties, cost, and the inclusion of materials for specific applications. Future perspectives are also provided.

Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing

With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the in situ printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated in situ strain sensing elements, showed that material selection affects the sensor signal significantly.

Force Control of a 3D Printed Soft Gripper with Built-In Pneumatic Touch Sensing Chambers

This work reports on a soft gripper with three-dimensional (3D) printed soft monolithic fingers that seamlessly incorporate pneumatic touch sensing chambers (pTSCs) for real-time pressure/force control to grasp objects with varying stiffness (i.e., soft, compliant, and rigid objects). The fingers of the soft gripper were 3D printed simultaneously along with the pTSC, without requiring support materials, using an inexpensive fused deposition modeling 3D printer. The pTSCs embedded in the fingers have numerous advantages, including fast response, repeatability, reliability, negligible hysteresis, stability over time, durability, and very low power consumption. Finite element modeling is used to predict the behavior of the pTSCs under different body contacts and to design their topology. Real-time pressure/force control was performed experimentally based on the feedback data provided by the pTSCs to grasp various objects with different weights, shapes, sizes, textures, and stiffnesses using an experimentally tuned proportional-integral-derivative (PID) controller with the same gains for all the objects grasped. In other words, the gripper can self-adapt to different environments with different stiffnesses and provide stable contact and grasping. These results are validated theoretically by modeling the soft gripper in contact with the objects with varying stiffness to show that the stability of the contact motion is not affected by the stiffness of the environment (i.e., the grasped object) when constant PID control gains are used.

Soft Elastomeric Capacitor for Strain and Stress Monitoring on Sutured Skin Tissues

Sutures are ubiquitous medical devices for wound closures in human and veterinary medicine, and suture techniques are frequently evaluated by comparing tensile strengths in ex vivo studies. Direct and nondestructive measurement of tensile force present in sutured biological skin tissue is a key challenge in biomechanical fields because of the unique and complex properties of each sutured skin specimen and the lack of compliant sensors capable of monitoring large levels of strain. The authors have recently proposed a soft elastomeric capacitor (SEC) sensor that consists of a highly compliant and scalable strain gauge capable of transducing geometric variations into a measurable change in capacitance. In this study, corrugated SECs are used to experimentally characterize the inherent biomechanical properties of canine skin specimens. In particular, an SEC corrugated with a re-entrant hexagonal honeycomb pattern is studied to monitor strain and stresses for three specific suture patterns: simple interrupted, cruciate, and intradermal patterns. Stress is estimated using constitutive models based on the Fractional Zener and the Kelvin-Voigt models, parametrized using a particle swarm algorithm from experimental data and results from a validated finite element model. Results are benchmarked against findings from the literature and show that SECs are valuable for clinical evaluation of tensile force in biological skins. It was found that both the ranking of suture pattern performance and the sutured skin's Young's modulus using the proposed approach agreed with data reported in the literature and that the estimated stress at the suture level closely matched that of an approximate finite element model.

Conductive Polymer Nanocomposites for Stretchable Electronics: Material Selection, Design, and Applications

Stretchable electronics that can elongate elastically as well as flex are crucial to a wide range of emerging technologies, such as wearable medical devices, electronic skin, and soft robotics. Critical to stretchable electronics is their ability to withstand large mechanical strain without failure while retaining their electrical conduction properties, a feat significantly beyond traditional metals and silicon-based semiconductors. Herein, we present a review of the recent advances in stretchable conductive polymer nanocomposites with exceptional stretchability and electrical properties, which have the potential to transform a wide range of applications, including wearable sensors for biophysical signals, stretchable conductors and electrodes, and deformable energy-harvesting and -storage devices. Critical to achieving these stretching properties are the judicious selection and hybridization of nanomaterials, novel microstructure designs, and facile fabrication processes, which are the focus of this Review. To highlight the potentials of conductive nanocomposites, a summary of some recent important applications is presented, including COVID-19 remote monitoring, connected health, electronic skin for augmented intelligence, and soft robotics. Finally, perspectives on future challenges and new research opportunities are also presented and discussed.

A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data

Soft robotic grippers are increasingly desired in applications that involve grasping of complex and deformable objects. However, their flexible nature and non-linear dynamics makes the modelling and control difficult. Numerical techniques such as Finite Element Analysis (FEA) present an accurate way of modelling complex deformations. However, FEA approaches are computationally expensive and consequently challenging to employ for real-time control tasks. Existing analytical techniques simplify the modelling by approximating the deformed gripper geometry. Although this approach is less computationally demanding, it is limited in design scope and can lead to larger estimation errors. In this paper, we present a learning based framework that is able to predict contact forces as well as stress distribution from soft Fin Ray Effect (FRE) finger images in real-time. These images are used to learn internal representations for deformations using a deep neural encoder, which are further decoded to contact forces and stress maps using separate branches. The entire network is jointly learned in an end-to-end fashion. In order to address the challenge of having sufficient labelled data for training, we employ FEA to generate simulated images to supervise our framework. This leads to an accurate prediction, faster inference and availability of large and diverse data for better generalisability. Furthermore, our approach is able to predict a detailed stress distribution that can guide grasp planning, which would be particularly useful for delicate objects. Our proposed approach is validated by comparing the predicted contact forces to the computed ground-truth forces from FEA as well as real force sensor. We rigorously evaluate the performance of our approach under variations in contact point, object material, object shape, viewing angle, and level of occlusion.

New Frontiers in 3D Structural Sensing Robots

Advanced robotics is the result of various contributions from complex fields of science and engineering and has tremendous value in human society. Sensing robots are highly desirable in practical settings such as healthcare and manufacturing sectors through sensing activities from human-robot interaction. However, there are still ongoing research and technical challenges in the development of ideal sensing robot systems. The sensing robot should synergically merge sensors and robotics. Geometrical difficulty in the sensor positioning caused by the structural complexity of sensing robots and their corresponding processing have been the main challenges in the production of sensing robots. 3D electronics integrated into 3D objects prepared by the 3D printing process can be the potential solution for designing realistic sensing robot systems. 3D printing provides the advantage to manufacture complex 3D structures in electronics in a single setup, allowing the ease of design flexibility, and customized functions. Therefore, the platform of 3D sensing systems is investigated and their expansion into sensing robots is studied further. The progress toward sensing robots from 3D electronics integrated into 3D objects and the advanced material strategies, used to overcome the challenges, are discussed.

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Electronic skins and machine learning for intelligent soft robots

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

Soft Robotics: A Review of Recent Developments of Pneumatic Soft Actuators

This paper focuses on the recent development of soft pneumatic actuators for soft robotics over the past few years, concentrating on the following four categories: control systems, material and construction, modeling, and sensors. This review work seeks to provide an accelerated entrance to new researchers in the field to encourage research and innovation. Advances in methods to accurately model soft robotic actuators have been researched, optimizing and making numerous soft robotic designs applicable to medical, manufacturing, and electronics applications. Multi-material 3D printed and fiber optic soft pneumatic actuators have been developed, which will allow for more accurate positioning and tactile feedback for soft robotic systems. Also, a variety of research teams have made improvements to soft robot control systems to utilize soft pneumatic actuators to allow for operations to move more effectively. This review work provides an accessible repository of recent information and comparisons between similar works. Future issues facing soft robotic actuators include portable and flexible power supplies, circuit boards, and drive components.