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Abstract Endoluminal devices are indispensable in medical procedures in the natural lumina of the body, such as the circulatory system and gastrointestinal tract. In current clinical practice, there is a need for increased control and capabilities of endoluminal devices with less discomfort and risk to the patient. This paper describes the detailed modeling and experimental validation of a magneto-electroactive endoluminal soft (MEESo) robot concept that combines magnetic and electroactive polymer (EAP) actuation to improve the utility of the device. The proposed capsule-like device comprises two permanent magnets with alternating polarity connected by a soft, low-power ionic polymer-metal composite (IPMC) EAP body. A detailed model of the MEESo robot is developed to explore quantitatively the effects of dual magneto-electroactive actuation on the robot’s performance. It is shown that the robot’s gait is enhanced, during the magnetically-driven gait cycle, with IPMC body deformation. The concept is further validated by creating a physical prototype MEESo robot. Experimental results show that the robot’s performance increases up to 68% compared to no IPMC body actuation. These results strongly suggest that integrating EAP into the magnetically-driven system extends the efficacy for traversing tract environments. 

Abstract This paper focuses on the modeling and development of engineered ionic polymer-metal composite (eIPMC) sensors for applications such as postural and tactile measurement in mechatronics/robotics-assisted finger rehabilitation therapy. Specifically, to tailor the sensitivity of the device, eIPMCs, fabricated using a polymer-surface abrading technique, are utilized as the sensing element. An enhanced chemoelectromechanical model is developed that captures the effect of the abrading process on the multiphysics sensing behavior under different loading conditions. The fabricated sensors are characterized using scanning electron microscopy imaging and cyclic voltammetry and chronoamperometry. Results show significant improvement in the electrochemical properties, including charge storage, double layer capacitance, and surface conductance, compared to the control samples. Finally, prototype postural-tactile finger sensors composed of different eIPMC variants are created and their performance validated under postural and tactile experiments. The tailored eIPMC sensors show increased open-circuit voltage response compared to control IPMCs, with 7.7- and 4.7-times larger peak-to-peak bending response under postural changes, as well as a 3.2-times more sensitive response under compression during tactile loading, demonstrating the feasibility of eIPMC sensors. 

Abstract This paper develops a position estimation system for a robot moving over a two-dimensional plane with three degrees-of-freedom. The position estimation system is based on an external rotating platform containing a permanent magnet and a monocular camera. The robot is equipped with a two-axes magnetic sensor. The rotation of the external platform is controlled using the monocular camera so as to always point at the robot as it moves over the 2D plane. The radial distance to the robot can then be obtained using a one-degree-of-freedom nonlinear magnetic field model and a nonlinear observer. Extensive experimental results are presented on the performance of the developed system. Results show that the position of the robot can be estimated with sub-mm accuracy over a radial distance range of +/−60 cm from the magnet. 

Abstract Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced‐order physical model characterizing the field‐driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data‐driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult‐to‐derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data‐driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence. 

Abstract This paper introduces a magneto-electroactive endoluminal soft (MEESo) robot concept, which could enable new classes of catheters, tethered capsule endoscopes, and other mesoscale soft robots designed to navigate the natural lumens of the human body for a variety of medical applications. The MEESo locomotion mechanism combines magnetic propulsion with body deformation created by an ionic polymer-metal composite (IPMC) electroactive polymer. A detailed explanation of the MEESo concept is provided, including experimentally validated models and simulated magneto-electroactive actuation results demonstrating the locomotive benefits of incorporating an IPMC compared to magnetic actuation alone. 

Abstract The synergistic integration of nanomaterials with 3D printing technologies can enable the creation of architecture and devices with an unprecedented level of functional integration. In particular, a multiscale 3D printing approach can seamlessly interweave nanomaterials with diverse classes of materials to impart, program, or modulate a wide range of functional properties in an otherwise passive 3D printed object. However, achieving such multiscale integration is challenging as it requires the ability to pattern, organize, or assemble nanomaterials in a 3D printing process. This review highlights the latest advances in the integration of nanomaterials with 3D printing, achieved by leveraging mechanical, electrical, magnetic, optical, or thermal phenomena. Ultimately, it is envisioned that such approaches can enable the creation of multifunctional constructs and devices that cannot be fabricated with conventional manufacturing approaches. 

Abstract Biological microswimmers can coordinate their motions to exploit their fluid environment—and each other—to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications.