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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., Science Advances, 8 (50), ISSN:2375-2548

International journal of robotics research (2021). doi:10.1177/02783649211021869 special issue: "Special Issue: RSS2020", Published by Sage Science Press, Thousand Oaks, Calif.

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 L1(0)-FePt magnetic coating can achieve such requirements. Therefore, such coating has been integrated into 8 mu m-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, L1(0)-FePt is conceived as the key material for image-guided propulsion in the vascular system to perform future targeted medical interventions., Advanced Functional Materials, 32 (8), ISSN:1616-3028, ISSN:1616-301X

Robotic ground vehicles are commonly used in various road conditions for performing special tasks with different levels of autonomy. This requires the robots to possess a certain degree of perception about the road conditions up ahead and make plans accordingly. It is essential for the robots to be able to quickly adapt their perception for various road conditions they operate. In this paper, an adaptive method for road boundary extraction using 2D LIDAR sensor is presented. A three-stage detection algorithm is utilized for road determination, in which parameter sets are updated adaptively based on a discriminative learning approach. Details of both the learning approach and the detection algorithm are discussed in detail. Experiments were performed on constructed and unconstructed roads to evaluate the performance of the proposed method, and the outcomes were presented in the paper. The results showed that accuracy of road boundary detection increased significantly with the proposed adaptive method. Likewise, significant changes were observed in the required computational times.

Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al2O3 passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H2 plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications.

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.