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Controlled microrobotic navigation in the vascular system can revolutionize minimally invasive medical applications, such as targeted drug and gene delivery. Magnetically controlled surface microrollers have emerged as a promising microrobotic platform for controlled navigation in the circulatory system. Locomotion of micrororollers in strong flow velocities is a highly challenging task, which requires magnetic materials having strong magnetic actuation properties while being biocompatible. The L10‐FePt magnetic coating can achieve such requirements. Therefore, such coating has been integrated into 8 µm‐diameter surface microrollers and investigated the medical potential of the system from magnetic locomotion performance, biocompatibility, and medical imaging perspectives. The FePt coating significantly advanced the magnetic performance and biocompatibility of the microrollers compared to a previously used magnetic material, nickel. The FePt coating also allowed multimodal imaging of microrollers in magnetic resonance and photoacoustic imaging in ex vivo settings without additional contrast agents. Finally, FePt‐coated microrollers showed upstream locomotion ability against 4.5 cm s−1 average flow velocity with real‐time photoacoustic imaging, demonstrating the navigation control potential of microrollers in the circulatory system for future in vivo applications. Overall, L10‐FePt is conceived as the key material for image‐guided propulsion in the vascular system to perform future targeted medical interventions.

Frequency diverse arrays (FDA) are recently introduced. Not only the phased array and frequency scanning arrays, but also FDA provides electronic beam scanning ability. Moreover, electronic beam scanning is provided without the use of phase shifters or vector modulators. Nevertheless, reported studies on this concept do not provide practically implementable results. In this paper, fundamental expressions of the FDA operation and its typical implementation schemes are reviewed, and an ultimately useful scheme for implementing and FDA is introduced based on linear frequency modulated continuous waveform (LFMCW). The mathematical foundations of LFMCW FDA are developed and used to design a basic proof of concept structure. This structure is also implemented and the measurements related to the implementation are presented with discussions on the results.Index Terms-Frequency diverse arrays (FDA), linear frequency modulated continuous waveform (LFMCW), phased array structures.

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time, which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

Systems with programmable and complex shape morphing are highly desired in many fields wherein sensing, actuation, and manipulation must be performed. Living organisms use nonuniform distributions of their body structural composition to achieve diverse shape morphing, motion, and functionality. However, for the microrobot fabrication, these designs often involve complicated robotic architectures requiring time-consuming and arduous fabrication processes. This paper proposes a single-step aniso-electrodeposition method for fabricating modular microrobots (MMRs) with distinct functions in each modular segment. By programming the electric field, the microscale stripe-shaped structure can be endowed with diverse shape-morphing capabilities, such as spiraling, twisting, bending, and coiling. The proposed fabrication method can develop MMRs with multiple independent modules onto which cells, drugs, and magnetic nanoparticles can be loaded to achieve multifunctionality. Thus, MMRs can perform multiple tasks, such as propulsion, grasping, and object delivery, simultaneously under magnetic control and ionic and pH stimuli.

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

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