Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots

Integrated Actuation and Sensing: Toward Intelligent Soft Robots

Soft robotics has received substantial attention due to its remarkable deformability, making it well-suited for a wide range of applications in complex environments, such as medicine, rescue operations, and exploration. Within this domain, the interaction of actuation and sensing is of utmost importance for controlling the movements and functions of soft robots. Nonetheless, current research predominantly focuses on isolated actuation and sensing capabilities, often neglecting the critical integration of these 2 domains to achieve intelligent functionality. In this review, we present a comprehensive survey of fundamental actuation strategies and multimodal actuation while also delving into advancements in proprioceptive and haptic sensing and their fusion. We emphasize the importance of integrating actuation and sensing in soft robotics, presenting 3 integration methodologies, namely, sensor surface integration, sensor internal integration, and closed-loop system integration based on sensor feedback. Furthermore, we highlight the challenges in the field and suggest compelling directions for future research. Through this comprehensive synthesis, we aim to stimulate further curiosity among researchers and contribute to the development of genuinely intelligent soft robots.

Protein-Aided Synthesis of Copper-Integrated Polyaniline Nanocomposite Encapsulated with Reduced Graphene Oxide for Highly Sensitive Electrochemical Detection of Dimetridazole in Real Samples

Dimetridazole (DMZ) is a derivative of nitroimidazole and is a veterinary drug used as an antibiotic to treat bacterial or protozoal infections in poultry. The residues of DMZ cause harmful side effects in human beings. Thus, we have constructed a superior electrocatalyst for DMZ detection. A copper (Cu)-integrated poly(aniline) (PANI) electrocatalyst (PANI-Cu@BSA) was prepared by using a one-step method of biomimetic mineralization and polymerization using bovine serum albumin (BSA) as a stabilizer. Then, the synthesized PANI-Cu@BSA was encapsulated with reduced graphene oxide (rGO) using an ultrasonication method. The PANI-Cu@BSA/rGO nanocomposite had superior water dispersibility, high electrical conductivity, and nanoscale particles. Moreover, a PANI-Cu@BSA/rGO nanocomposite-modified, screen-printed carbon electrode was used for the sensitive electrochemical detection of DMZ. In phosphate buffer solution, the PANI-Cu@BSA/rGO/SPCE displayed a current intensity greater than PANI-Cu@BSA/SPCE, rGO/SPCE, and bare SPCE. This is because PANI-Cu@BSA combined with rGO increases fast electron transfer between the electrode and analyte, and this synergy results in analyte-electrode junctions with extraordinary conductivity and active surface areas. PANI-Cu@BSA/rGO/SPCE had a low detection limit, a high sensitivity, and a linear range of 1.78 nM, 5.96 μA μM-1 cm-2, and 0.79 to 2057 μM, respectively. The selective examination of DMZ was achieved with interfering molecules, and the PANI-Cu@BSA/rGO/SPCE showed excellent selectivity, stability, repeatability, and practicability.

Data-driven method for damage localization on soft robotic grippers based on motion dynamics

Damage detection is one of the critical challenges in operating soft robots in an industrial setting. In repetitive tasks, even a small cut or fatigue can propagate to large damage ceasing the complete operation process. Although research has shown that damage detection can be performed through an embedded sensor network, this approach leads to complicated sensorized systems with additional wiring and equipment, made using complex fabrication processes and often compromising the flexibility of the soft robotic body. Alternatively, in this paper, we proposed a non-invasive approach for damage detection and localization on soft grippers. The essential idea is to track changes in non-linear dynamics of a gripper due to possible damage, where minor changes in material and morphology lead to large differences in the force and torque feedback over time. To test this concept, we developed a classification model based on a bidirectional long short-time memory (biLSTM) network that discovers patterns of dynamics changes in force and torque signals measured at the mounting point. To evaluate this model, we employed a two-fingered Fin Ray gripper and collected data for 43 damage configurations. The experimental results show nearly perfect damage detection accuracy and 97% of its localization. We have also tested the effect of the gripper orientation and the length of time-series data. By shaking the gripper with an optimal roll angle, the localization accuracy can exceed 95% and increase further with additional gripper orientations. The results also show that two periods of the gripper oscillation, i.e., roughly 50 data points, are enough to achieve a reasonable level of damage localization.

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.

Recent Advances in Electronic Skins with Multiple-Stimuli-Responsive and Self-Healing Abilities

Wearable electronic skin (e-skin) has provided a revolutionized way to intelligently sense environmental stimuli, which shows prospective applications in health monitoring, artificial intelligence and prosthetics fields. Drawn inspiration from biological skins, developing e-skin with multiple stimuli perception and self-healing abilities not only enrich their bionic multifunctionality, but also greatly improve their sensory performance and functional stability. In this review, we highlight recent important developments in the material structure design strategy to imitate the fascinating functionalities of biological skins, including molecular synthesis, physical structure design, and special biomimicry engineering. Moreover, their specific structure-property relationships, multifunctional application, and existing challenges are also critically analyzed with representative examples. Furthermore, a summary and perspective on future directions and challenges of biomimetic electronic skins regarding function construction will be briefly discussed. We believe that this review will provide valuable guidance for readers to fabricate superior e-skin materials or devices with skin-like multifunctionalities and disparate characteristics.

Self-Healing Behavior of Polymer/Protein Hybrid Hydrogels

The paper presents the viscoelastic properties of new hybrid hydrogels containing poly(vinyl alcohol) (PVA), hydroxypropylcellulose (HPC), bovine serum albumin (BSA) and reduced glutathione (GSH). After heating the mixture at 55 °C, in the presence of GSH, a weak network is formed due to partial BSA unfolding. By applying three successive freezing/thawing cycles, a stable porous network structure with elastic properties is designed, as evidenced by SEM and rheology. The hydrogels exhibit self-healing properties when the samples are cut into two pieces; the intermolecular interactions are reestablished in time and therefore the fragments repair themselves. The effects of the BSA content, loaded deformation and temperature on the self-healing ability of hydrogels are presented and discussed through rheological data. Due to their versatile viscoelastic behavior, the properties of PVA/HPC/BSA hydrogels can be tuned during their preparation in order to achieve suitable biomaterials for targeted applications.