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Nature provides a wide range of inspiration for building mobile micromachines that can navigate through confined heterogenous environments and perform minimally invasive environmental and biomedical operations. For example, microstructures fabricated in the form of bacterial or eukaryotic flagella can act as artificial microswimmers. Due to limitations in their design and material properties, these simple micromachines lack multifunctionality, effective addressability and manoeuvrability in complex environments. Here we develop an origami-inspired rapid prototyping process for building self-folding, magnetically powered micromachines with complex body plans, reconfigurable shape and controllable motility. Selective reprogramming of the mechanical design and magnetic anisotropy of body parts dynamically modulates the swimming characteristics of the micromachines. We find that tail and body morphologies together determine swimming efficiency and, unlike for rigid swimmers, the choice of magnetic field can subtly change the motility of soft microswimmers. In nature many microorganisms are able to change their shape to adapt to the changes in the environment. Inspired by this phenomenon, here Huang et al . build artificial microswimmers with body and flagellum made of programmable hydrogel-based materials incorporated with magnetic nanoparticles.

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an R2 score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an R2 score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface.

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.

Shape memory materials have been widely used as programmable soft matter for developing multifunctional hybrid actuators. Several challenges of fabrication and effective modelling of these soft actuating systems can be addressed by implementing novel 3D printing techniques and simulations to aid the designer. In this study, the temperature-dependent recovery of an embedded U-shaped Shape Memory Alloy (SMA) and the shape fixity of a 3D-printed Shape Memory Polymer (SMP) matrix were exploited to create a bi-state Shape Memory Composite (SMC) soft actuator. Electrical heating allowed the SMA to achieve the bi-state condition, undergoing phase transformation to a U shape in the rubbery phase and a flat shape in the glassy phase of the SMP. A COMSOL Multiphysics model was developed to predict the deformation and recovery of the SMC by leveraging the in-built SMA constitutive relations and user-defined material subroutine for the SMP. The bi-state actuation model was validated by capturing the mid-point displacement of the 80 mm length × 10 mm width × 2 mm-thick 3D-printed SMC. The viability of the SMC as a periodic actuator in terms of shape recovery was addressed through modelling and simulation. Results indicated that the proposed COMSOL model was in good agreement with the experiment. In addition, the effect of varying the volume ratio of the SMA wire in the SMC on the maximum and recovered deflection was also obtained. Our model can be used to design SMC actuators with various performance profiles to facilitate future designs in soft robotics and wearable technology applications.

Rehabilitation requires repetitive and coordinated movements for effective treatment, which are contingent on patient compliance and motivation. However, the monotony, intensity, and expense of most therapy routines do not promote engagement. Gesture-controlled rehabilitation has the potential to quantify performance and provide engaging, cost-effective treatment, leading to better compliance and mobility. We present the design and testing of a gesture-controlled rehabilitation robot (GC-Rebot) to assess its potential for monitoring user performance and providing entertainment while conducting physical therapy. Healthy participants (n = 11) completed a maze with GC-Rebot for six trials. User performance was evaluated through quantitative metrics of movement quality and quantity, and participants rated the system usability with a validated survey. For participants with self-reported video-game experience (n = 10), wrist active range of motion across trials (mean ± standard deviation) was 41.6 ± 13° and 76.8 ± 16° for pitch and roll, respectively. In the course of conducting a single trial with a time duration of 68.3 ± 19 s, these participants performed 27 ± 8 full wrist motion repetitions (i.e., flexion/extension), with a dose-rate of 24.2 ± 5 reps/min. These participants also rated system usability as excellent (score: 86.3 ± 12). Gesture-controlled therapy using the GC-Rebot demonstrated the potential to be an evidence-based rehabilitation tool based on excellent user ratings and the ability to monitor at-home compliance and performance.

One of the grand challenges in interventional cardiology and neuroradiology is to minimize the operation time and risk of damage during catheterization. These two factors drastically increase if the target location resides in small and tortuous vessels. Flow‐driven microcatheters are capable of rapidly and safely navigating small arteries with complex anatomy. However, their navigation relies on proper perfusion, which is an important bottleneck in the treatment of pathologies that cause impaired flow conditions. This work introduces the first endovascular sensor‐integrated soft robotic device that navigates sub‐millimeter arteries by extracting propulsive power from external magnetic fields. To this end, a number of innovations are described in the design, actuation, and control of flexible magnetic structures. The device is capable of advancing inside vasculature in an automated fashion using an open‐loop control scheme. Onboard sensors enable the real‐time monitoring of flow conditions, and autonomous switching between different modes of locomotion. The potential of the presented technology for minimally invasive diagnosis and therapy is demonstrated by achieving navigation inside coronary arteries of an ex vivo porcine heart under fluoroscopic guidance. This work introduces an endovascular sensor‐integrated soft robotic device that navigates sub‐millimeter arteries by extracting propulsive power from external magnetic fields. Automated navigation is implemented using the 3D map of the vasculature. Onboard sensors continuously monitor the flow conditions to enable autonomous switching between different modes of locomotion. Navigation inside obstructed ex vivo porcine coronaries using fluoroscopy demonstrates clinical potential.

Straight-line needle insertion is a prevalent tool in surgical interventions in the brain, such as Deep Brain Stimulation and Convection-Enhanced Delivery, that treat a range of conditions from Alzheimer’s disease to brain cancer. Using a steerable needle to execute curved trajectories and correct positional deviation could enable more intervention possibilities, while reducing the risk of complication in these procedures. This paper experimentally identifies model parameters using an expectation-maximization (EM) algorithm for two different steerable needle models. The results compared a physically motivated model to the established bicycle needle model and found the former to be preferred for modeling soft brain tissue needle insertion. The results also supported the experimentally parameterized models’ use in future applications such as needle steering control.

In this work, we present methods allowing parallel, hybrid, and serial manipulators to be analyzed, calibrated, and controlled with the same analytical tools. We introduce a general approach to describe any robotic manipulator using established serial-link representations. We use this framework to generate analytical kinematic and calibration Jacobians for general manipulator constructions using null space constraints and extend the methods to hybrid manipulator types with complex geometry. We leverage the analytical Jacobians to develop detailed expressions for post-calibration pose uncertainties that are applied to describe the relationship between data set size and post-calibration uncertainty. We demonstrate the calibration of a hybrid manipulator assembled from high precision calibrated industrial components resulting in 91.1 μm RMS position error and 71.2 μrad RMS rotation error, representing a 46.7% reduction compared to the baseline calibration of assembly offsets.

This paper models an extensible catheter with an embedded magnet at its distal tip subject to an external magnetic field. We implement a control method coined observed control to perform model-based predictive control of the catheter using a Kalman smoother framework. Using this same smoother framework, we also solve for catheter shape and orientation given magnetic and insertion control using Cosserat rod theory and implement a disturbance observer for closed-loop control. We demonstrate observed control experimentally by traversing a 3D cube trajectory with the catheter tip. The catheter achieved positional accuracy of 3.3 mm average error in open-loop, while closed-loop control improved the accuracy to 0.33 mm.

For targeted transport in the body, biomedical microbots (μbots) must move effectively in three‐dimensional (3D) microenvironments. Swimming μbots translate via asymmetric or screw‐like motions while rolling ones use friction with available surfaces to generate propulsive forces. Previously the authors have shown that planar rotating magnetic fields assemble μm‐scale superparamagnetic beads into circular μbots that roll along surfaces. In this, gravity is required to pull μbots near the surface; however, this is not necessarily practical in complex geometries. Here, the authors show that rotating magnetic fields, in tandem with directional magnetic gradient forces, can be used to roll μbots on surfaces regardless of orientation. Simplifying implementation, a spinning permanent magnet is used to generate differing ratios of rotating and gradient fields, optimizing control for different environments. This use of a single magnetic actuator sidesteps the need for complex electromagnet or tandem field setups, removes requisite gravitational load forces, and enables μbot targeting in complex 3D biomimetic microenvironments. A spinning permanent magnet can be used to generate differing ratios of rotating and gradient fields to assemble, spin, and pull microbots toward a surface. This use of a single magnetic actuator sidesteps the need for complex electromagnet setups, removes requisite gravitational load forces, and enables microbot targeting in complex 3D biomimetic microenvironments.