Driving decision-making in mixed traffic, characterized by high-dynamic interactions and stochastic behaviors of human-driven vehicles, poses significant challenges for autonomous driving systems. To address these issues, we propose a novel Transformer-based Spatial Temporal Fusion (TSTF) module integrated with an auxiliary contrastive learning task within a multi-agent reinforcement learning (MARL) framework. The TSTF module captures interaction-aware behaviors and long-term temporal dependencies that tackle mixed cooperative driving scenarios, while the auxiliary contrastive learning task refines feature representations to enhance exploration efficiency and decision stability. Experimental evaluations on the MetaDrive platform demonstrate that the proposed approach outperforms baseline algorithms in safety, adaptability and robustness to dynamic traffic scenarios. The results highlight the effectiveness of the TSTF module in enabling robust and context-aware collaborative driving behaviors, offering a scalable solution for real-world mixed traffic. This work advances MARL by addressing key challenges in interaction modeling and driving decision-making under uncertainty, with significant implications for the development of intelligent transportation systems.

Collective microrobots enable controlled batch delivery, showing promising application in the biomedical field. However, significant challenges remain in achieving long-distance delivery of collective microrobots in dynamic environments. This study proposes a magnetic actuation strategy for delivering collective cell microrobots in flowing conditions. A magnetic actuation method is developed, and a mobile actuation system with multiple coils coordination is designed to generate spatially isotropic magnetic fields. Experiments of delivering collective microrobots are conducted in flowing conditions, including downstream and upstream with an average flow velocity up to 8.84 mm/s. Results demonstrate that the proposed actuation strategy enhances driving performance in dynamic environments, achieving long-distance delivery of collective microrobots (over 548 mm). The final access rate of microrobots reaches 90.63% and 94.79% in upstream and downstream conditions, respectively. Our strategy provides an efficient control method for delivering collective microrobots, showing potential for targeted delivery in biomedical applications.

Online path planning for magnetic microrobots actuated by electromagnetic system in dynamic flow field presents significant challenges due to time-varying fluid dynamics, energy constraints, and collision risks. Traditional path planning approaches, which often rely on static flow assumptions or simplified geometric models, struggle to balance energy efficiency, path continuity, and adaptability in real-world scenarios. This paper introduces an end-to-end path planner for energy-efficient and collision-free navigation of magnetic helical microrobots, integrating flow field feature extraction and reinforcement learning (RL) framework. Our method employs a transformer encoder to capture contextual correlations of flow field and uses a Soft Actor-Critic (SAC) framework to optimize energy consumption while ensuring dynamic obstacle avoidance. Simulations and experiments in dynamic flow environments validate our approach, demonstrating 14.7% lower energy consumption and robust collision avoidance in several different test scenarios.

Magnetically actuated robots have recently shown great capabilities for remote applications in medical procedures. However, the efficient actuation of magnetic robots with dexterous field and gradient generation in large workspaces remains challenging. To overcome the critical challenges, we report a reconfigurable electromagnetic actuation system (REMA) for regulating magnetic fields (maximum: 17 mT) and gradients (maximum: 120 mT/m) in large workspaces. Reconfigurable coil configurations are achieved by employing three mobile electromagnetic coils mounted on three independent 6-DOF robotic arms. Furthermore, the field characteristics generated by a single coil and three coils were modeled via Finite-element method (FEM) and measurements from experiments, respectively. Since there are non-linearities between desired field generation and coil configuration, we propose a multi-objective optimization (MOO) method for generating the Pareto-optimized coil configuration to achieve field and force control in large workspaces. Finally, extensive experiments were conducted to demonstrate the capability and dexterity of our system for autonomous magnetic manipulation in large workspaces, thus showing its potential for clinical applications. Note to Practitioners—This paper aims to address the dexterous generation of magnetic fields and gradients in large workspaces, aiming to realize accurate, efficient, and automated control of different magnetic robots. This paper introduces a reconfigurable electromagnetic actuation system based on three independent robotic arms with three electromagnetic coils. Subsequently, we propose a multi-objective optimization (MOO) method to regulate the coil configuration for generating different fields and gradients. This approach facilitates the application of magnetically driven helical robots, catheters, and capsule robots in various medical scenarios. The results demonstrate that our proposed platform and optimization strategy can effectively implement magnetic manipulations across diverse application scenarios. Looking ahead, we anticipate integrating our work with medical imaging devices to furnish doctors with enhanced tools for medical applications.

Magnetic continuum robots (MCRs) with active steering capability hold great promise for improving interventional surgery due to their flexibility and controllability. However, achieving real-time tracking and automatic navigation of MCRs in tissue-mimicking multi-bifurcated vessels remains a significant challenge. This work proposes an ultrasound-guided real-time automatic navigation framework for magnetic guidewire robots to improve interventional surgery, including modeling, simulation, tracking and control. An ultrasound-guided magnetically controlled guidewire robot system (UMCGRS) is designed and validated in 3D vascular phantom. An equilibrium guidewire model is established to describe the quasi-static behavior of MCRs in a permanent magnetic field and to derive the control Jacobian for guidewire tip control, which is validated by magnetic navigation simulation. A network-based real-time ultrasound tracking method is developed for accurate guidewire detection (average detection error of 0.81 mm across various vessels), and a model-based path tracking control strategy is proposed for guidewire navigation. Experiments in a femoral artery gelatin phantom with tissue-mimicking environments demonstrate the effectiveness of the tracking and control (average tracking error of 1.50 ± 0.30 mm). The proposed UMCGRS and automatic navigation framework are expected to enhance the autonomy of MCRs, and will provide a reliable solution for improving interventional surgery.

This paper proposes a specific and effective real-time sequence planning method using robot manipulators to complete complex assembly tasks. Many previous studies developed different traversal methods to obtain the optimal assembly sequence. Besides, a number of algorithms were proposed to enhance flexibility when the conditions or rules were changed in various sequence optimization problems. However, these state-of-the-art (STOA) methods necessarily require modifications when task details are changed. Consequently, to further improve the generalization ability and improve the performance of the sequence optimization, a neural combinatorial optimization algorithm combined with a self-learning strategy is proposed for assembly sequence planning. In addition, obstacle avoidance and the non-collision constraints between workpieces in the assembly process are considered. According to the experiment results, the new method is superior to the STOA methods in terms of optimization efficiency. More importantly, the proposed method has satisfactory generalization ability for different assembly tasks.Note to Practitioners—This paper studies assembly sequence planning problems for different real-world applications in industrial and home service fields. Many assembly sequence planning solutions have been widely utilized before. However, the generalization ability of the previous methods is not satisfactory since the re-adjust process is required when the workpiece number or collision condition changes in different tasks.Motivated by the above reasons, this paper develops a learning-based assembly sequence planning solution to resolve complex assembly problems without parameter re-adjustment processes. Users can directly apply the developed workpiece identification and localization method to obtain the sensing information. Then, the newly designed collision-free cost function should be programmed as the core of the assembly sequence optimization. Next, the proposed neural combinatorial optimization (NCO) with the sensing information and target configuration as inputs can provide the optimal assembly sequence by self-learning. The learned NCO-based method can be directly applied to diverse planning tasks, even with different workpiece numbers. Users can also refer to the experimental examples in this paper for the extension of the proposed method to their own applications.

Untethered magnetically actuated soft microrobots are promising in micromanipulation applications. Autonomous navigation and micromanipulation in dynamic environments are challenging because uncertain dynamic obstacles lead to increased planning time and reduced real-time performance. Here, we developed a novel H-shape soft magnetic microrobot with finger behaviors, including gripping and releasing. Without additional stimuli (e.g., light, temperature, etc.), the finger behaviors and movements of the microrobot can be controlled simultaneously by adjusting the strength and direction of the magnetic fields. We proposed a sliding-window-based local dynamic path planning method in dynamic environments to address the drawbacks of inefficiency and the high computational cost of global search. An enhanced optimized rapidly-exploring random tree algorithm is developed for planning avoidance paths at a safe distance from the boundary of obstacles. In order to automatically manipulate multiple objects, the manipulation process is quantitatively divided into several subtasks, and a finite state machine (FSM) based task planner is proposed to control the switching between subtasks. Experiments demonstrate the autonomous obstacle avoidance and navigation of the microrobot in dynamic environments with obstacles moving 2.5 times faster than the robot. Eight spherical objects of 2 mm diameter were automatically transported to the corresponding target position with an average error of 0.29 mm and an average transport time of 17.25 seconds. In the future, this work promises to enable automated cell sorting by microrobots. Note to Practitioners—This article was motivated by the recent interest in utilizing the small-scale microrobots to perform micromanipulation tasks. For automated manipulation of objects, effective Untethered actuation, manipulation behavior control, and navigation are required. Besides, obstacle avoidance and dynamic obstacles in the environment need to be considered. The strategy proposed here is developed for automated multi-object manipulation via a novel soft magnetic microrobot. The developed task planner quantitatively divides the operation process into several subtasks and controls the switching between them. The dynamic path planning algorithm is used to efficiently search for collision avoidance paths in dynamic obstacle environments. The motion-behavior coordination controller is used to control the robot to perform manipulation subtasks. Experimental results have validated the proposed strategy in actuation, navigation, and multiple cargoes manipulation in dynamic obstacle environments.

The operation accuracy of humanoid robot hands driven by the tendon sheath will be reduced due to the influence of friction torque during rotation, which is not conducive to the dexterous operations of humanoid robot hands. In order to improve the control accuracy of humanoid robot hands, this paper proposes a control strategy based on the disturbance observer compensation, which eliminates the external disturbance torque by compensating the friction torque. Firstly, this article proposes the mechanical structure of humanoid robot hands driven by the tendon sheath with 19 degrees of freedom (DOF). This humanoid robot hands can grasp most irregular objects. Next, the dynamic model of humanoid robot hands’ drive systems is established based on the tendon sheath transmission theory. The driving system’s dynamic model reveals the influence of friction torque on the motion accuracy of the humanoid robot hand. Then, the disturbance observer (DOB) is designed based on the robust stability theorem. The DOB is used to improve the control accuracy of the driving system, thereby improving the operational accuracy of humanoid robot hands. Finally, this article conducts simulation rotation tracking control and prototype grasping control experiments on humanoid robot hands. The experimental results show that the proposed control strategy based on disturbance compensation can effectively improve the operational accuracy of humanoid robot hands. Note to Practitioners—This paper proposes a humanoid hand with 19 degrees of freedom based on the tendon-driven theory and applies the cable theory for its dynamic modeling. To address the issue of decreased precision caused by friction in practical operations, a friction compensation control strategy based on disturbance observer is proposed in this study. This control strategy improves the motion accuracy and stability of the mechanical hand. Finally, the effectiveness of the proposed control strategy is demonstrated through numerical simulation and experimental validation.

Tiny crack detection plays a vital role in ensuring the safety of critical industrial components and infrastructure, providing early intervention to prevent crack propagation and mitigate potential damage. Although machine vision-based defect detection has been greatly utilized in safety inspection and maintenance, tiny crack detection remains a big issue due to the low visibility and weak features with large background noise. To address this challenge, this paper presents an end-to-end trainable hierarchical constrained neural network for tiny crack detection. Firstly, we present a hierarchical loss constraint Filter (HLCF) module based on a region-level attention mechanism to capture the holistic vein structure of cracks and enhance the faint feature extraction of tiny cracks. In order to balance local high contrast and holistic crack features, a feature fusion loss constraint is designed to reduce noise interference and refine the boundary details of cracks by learning different receptive fields in each layer. Besides that, we created a dataset consisting of tiny crack samples collected through image processing and manual labeling from industrial production, named TinyCrack. The proposed HLCF model is evaluated on TinyCrack and seven public databases. The experimental indices show that the model achieves Precision over 0.62%, Recall over 0.12%, F-score over 0.53%, and IOU over 1.12% in TinyCrack, demonstrating the best accuracy compared with other public crack detection models. The results indicate that the HLCF detection model has better performance in identifying low-visibility tiny cracks.

Target localization technology is widely applied in various applications, such as rescue missions, robot navigation, and the Internet of Things. However, in some scenarios, the positions of sensors are unknown due to the load limitation of the sensor carriers and environmental interferences, resulting in the instability of the global navigation satellite system (GNSS). This paper focuses on optimal angle-of-arrival (AOA) sensor placement using multiple position-unknown sensors for target localization accuracy improvement. To guarantee the uniqueness of the target coordinate, at least two anchors are needed. The anchors are some static benchmark objects in the environment with priori known positions. Firstly, a new optimization problem for AOA target localization accuracy improvement incorporating position-unknown sensors and anchors is formulated. Secondly, the optimal theoretical localization accuracies of the unknown sensors and target are derived by minimizing the trace of the Cramér-Rao lower bounds (CRLBs). Thirdly, a mixture optimization method, including a geometrical initialization and the new proposed simultaneous perturbation stochastic approximation and adaptive momentum estimation (SPSA-Adam) algebraic algorithm, is developed. Then, the correctness of the new theoretical findings and the effectiveness of the proposed sensor placement optimization method are verified by simulation examples.

Magnetic microrobots have garnered significant attention and hold great potential for biomedical research applications. However, achieving precise manipulation in vivo poses significant challenges, particularly in medical image-based real-time feedback control, because it is difficult for a visual camera to track the motion of magnetic microrobots inside the body in biomedical applications. To realize the precise control of magnetic microrobots, it is also necessary to design and implement a simple and powerful control method. This approach allows for avoiding resource-intensive and complex control strategies. In this article, we present a learning-based real-time control method utilizing ultrasound images. Inspired by the ADboost concept, we use a reinforcement learning approach to integrate two simple control methods: a proportional-integral-derivative controller and a guiding vector field controller. We develop a novel$Q$-learning method called average$Q$-learning that incorporates average operation and$n$-step bootstraps. Its primary objective is to dynamically adjust the outputs of the different simple controllers. While each controller individually offers a straightforward solution, their integration contributes to a powerful control approach. To demonstrate its scalability, a nonsmooth path is utilized to investigate the integration performance of three simple controllers. In addition, we enhance a classic segmentation module, U-net, by incorporating an atrous spatial pyramid pooling module. To validate the effectiveness of the proposed control method, we conduct simulations and experiments using various planar paths. The quantitative analysis of the results demonstrates the efficacy of our approach in achieving precise manipulation, leveraging real-time control based on medical images for magnetic microrobots. Overall, this study provides a preliminary investigation into the field of medical image-based precise manipulation of magnetic microrobots in vivo applications.

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3% of the body length which is less than$0.5mm$. Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. Note to Practitioners—This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

Remote-actuated magnetic robots, relying solely on the magnetic torque stemming from rotating magnetic fields, hold immense promise in biomedical applications. However, to precisely actuate magnetic robots in large workspaces, the efficient generation of isotropic rotating fields using electromagnetic actuation (EMA) systems presents an enduring challenge. This is because the choice of configuration of the EMA system is a major concern, particularly when considering collision avoidance between coils and the human body while ensuring isotropic actuation. In this study, we presented an analysis of the characteristics of various three-coil configurations by quantitatively evaluating field isotropy. Furthermore, we introduced a performance-guided optimization method to adjust coil configurations by optimizing designed evaluation metrics, aiming to generate rotating fields with isotropic characteristics in a target local region. Finally, we implemented a reconfigurable EMA and conducted extensive experiments to demonstrate the capability of our method and platform. The experimental results showcase the potential of our approach for advanced clinical applications.

Untethered microrobots possess a promising perspective for micromanipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, to achieve which the mechanism of picking, transporting, and releasing behaviors needs further investigation. In this article, we propose to achieve automated microrobotic manipulation using magnetic microswarms with multimodal morphology. The microswarm is composed of around 11–21 million$\text{Fe}_{3}\mathrm{O}_{4}$nanoparticles (1.0$\text{--}1.8\,\mu$L particle suspension). When exposed to different dynamic magnetic fields, the swarm could exhibit corresponding forms. We realize precise and controllable cargo picking and releasing by exploiting the fluid fields of different swarm forms. In order to quantitatively describe these behaviors, we design a finite-state machine. A super-twisting sliding-mode controller has been formulated for the motion control of swarms. The disturbances are compensated via a disturbance observer. To enable automated micromanipulation in obstructed scenarios, a path planner inspired by rapidly exploring random tree algorithm is designed for path planning when obstacles exist. We also propose an enhanced-genetic algorithm to optimally transport multiple objects to the target position. Experiments demonstrate that our method could effectively transport micro-objects with different sizes and shapes. The precise selectivity of the method is validated when multiple objects exist in the working environment. Finally, the long-distance delivery ability and adaptivity to various friction situations of our strategy are demonstrated. This work explores a concise, untethered, and automated micromanipulation strategy, provides a new automatic tool for micromanipulation tasks, and extends the application potential of swarm microrobotics.

Hardness is one of the most critical tactile properties for robots to recognize objects. Machine learning methods have shown superior performance in object hardness classification. However, existing machine learning methods for tactile hardness classification cannot use the ordinal information between hardness classes because the one-hot encoding only cares about the correct class and ignores the inter-class relationship. To solve this problem, we propose to generalize the one-hot encoding using unimodal distributions including the Poisson and binomial distributions for tactile ordinal classification problems, resulting in two tactile ordinal networks (TacONet): TacONet-p and TacONet-b. Furthermore, we collect a tactile hardness dataset on the silicone samples with three different shapes (Shapes A, B, C), and each shape samples have thirteen hardness classes ranging from 0A (Shore A scale) to 60A at 5A intervals. We validate the resulting method for tactile hardness classification using a real robot. Experimental results demonstrate that compared with state-of-the-art methods, the proposed method achieves better classification performance in terms of accuracy and quadratic weighted kappa (QWK) on the tactile hardness dataset, reaching a classification accuracy up to 99.5% and a QWK up to 99.9% on Shape C. Note to Practitioners—In the field of robotics tactile recognition, hardness classification is one of the most important and common tasks for robots to accurately recognize objects, particularly when the environment is dark or visual sensors are not working. In this paper, we propose a novel tactile ordinal network for tactile hardness classification tasks. The existing machine learning models for tactile hardness classification are trained by minimizing the cross-entropy loss between predicted vectors and one-hot encoding vectors of true classes, which makes the models only care about the correct classes and ignores the inter-class relationship of hardness classes. In other words, these models have the same probability to misclassify a hardness class with any other hardness class. To tackle this problem, we propose to generalize the one-hot encoding method using a unimodal distribution method to encode the true classes. The unimodal distribution encoding vectors can make the model learn the ordinal information between classes. It is proved that the proposed method is able to effectively improve the classification accuracy and QWK in a tactile hardness classification task.

Soft magnetic miniature robots (SMMRs) have potential biomedical applications due to their flexible size and mobility to access confined environments. However, navigating the robot to a goal site with precise control performance and high repeatability in unstructured environments, especially in flow rate conditions, still remains a challenge. In this study, drawing inspiration from the control requirements of drug delivery and release to the goal lesion site in the presence of dynamic biofluids, we propose a flow rate rejection control strategy based on a deep reinforcement learning (DRL) framework to actuate an SMMR to achieve goal-reaching and hovering in fluidic tubes. To this end, an SMMR is first fabricated, which can be operated by an external magnetic field to realize its desired functionalities. Subsequently, a simulator is constructed based on neural networks to map the relationship between the applied magnetic field and robot locomotion states. With minimal prior knowledge about the environment and dynamics, a gated recurrent unit (GRU)-based DRL algorithm is formulated by considering the designed history state-action and estimated flow rates. In addition, the randomization technique is applied during training to distill the general control policy for the physical SMMR. The results of numerical simulations and experiments are illustrated to demonstrate the robustness and efficacy of the presented control framework. Finally, in-depth analyses and discussions indicate the potentiality of DRL for soft magnetic robots in biomedical applications.

The widespread application of learning-based methods in robotics has allowed significant simplifications to controller design and parameter adjustment. In this article, robot motion is controlled with learning-based methods. A control policy using a broad learning system (BLS) for robot point-reaching motion is developed. A sample application based on a magnetic small-scale robotic system is designed without detailed mathematical modeling of the dynamic systems. The parameter constraints of the nodes in the BLS-based controller are derived based on Lyapunov theory. The design and control training processes for a small-scale magnetic fish motion are presented. Finally, the effectiveness of the proposed method is demonstrated by convergence of the artificial magnetic fish motion to the targeted area with the BLS trajectory, successfully avoiding obstacles.

Microswimmer and miniswimmer toward precision-targeted medicine have attracted extensive attention recently. We have developed an autonomous manipulation approach for magnetic-driven helical miniswimmer at low Reynolds number in the horizontal plane ($H$-plane). Different from our previous work which just makes the barycenter of miniswimmer on the reference path as well as takes the swimming direction not into consideration in planar path following, our control policy in this article can make the miniswimmer to follow the reference path and, at the same time, its swimming direction is also along the reference path. A robust tracking method is employed to locate the helical miniswimmer in real time. Due to different external disturbances, an angle compensating model in the global coordinate frame is developed by radial basis function (RBF) networks trained by backpropagation algorithms, which is used to express the swimming model of the helical miniswimmer facing the gravity and lateral disturbances. A discrete-time optimal controller is formulated based on the linear-quadratic feedback control. Simulations and experiments are conducted to quantitatively validate the autonomous manipulation, and the results show the control performance with submillimeter accuracy in the$H$-plane.Note to Practitioners:This article is motivated by the potential application of precision-targeted medicine using magnetic-driven microswimmer/miniswimmer. The formulated controller employs the error model in the horizontal plane to design the control law. Simulations and experiments validate the effectiveness of the proposed discrete-time optimal control scheme using magnetic-driven miniswimmers.

Touch modality identification has attracted increasing attention due to its importance in human–robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods.Note to Practitioners—In a wealth of tactile recognition tasks, we are in the face of various challenges. For instance, tactile measurements are commonly tensorial and high-dimensional. The misalignments among tactile measurements prevail, such as different durations of tactile measurements and the misaligned starting time point of tactile measurements. This article presents a kernel-based method using an ideal regularized composite kernel to deal with all challenges in a unified framework. The kernel-based method consists of two key components including the SVD-based kernel and the global alignment kernel. The proposed method may shed new insights on new advances in tactile signal processing particularly in human–robot interactions.

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos' data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters' constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system.

Magnetically actuated small-scale robots have great potential for numerous applications in remote, confined, or enclosed environments. Multiple small-scale robots enable cooperation and increase the operating efficiency. However, independent control of multiple magnetic small-scale robots is a great challenge, because the robots receive identical control inputs from the same external magnetic field. In this article, we propose a novel strategy of completely decoupled independent control of magnetically actuated flexible swimming millirobots. A flexible millirobot shows a crawling motion on a flat plane within an oscillating magnetic field. Millirobots with different magnetization directions have the same velocity response curve to the oscillating magnetic field but with a difference of phase. We designed and fabricated a group of up to four heterogeneous millirobots with identical geometries and different magnetization directions. According to their velocity response curves, an optimal direction of oscillating magnetic field is calculated to induce a desired velocity vector for the millirobot group, one of which is nonzero and the others are approximately zero. The strategy is verified by experiments of independent position control of up to four millirobots and independent path following control of up to three millirobots with small errors. We further expect that with this independent control strategy, the millirobots will be able to cooperate to finish complicated tasks.

Reconfigurable microrobotic swarms and controllable active matter systems have drawn extensive attention recently. Developing effective actuation strategies and control schemes that enable embodied intelligence of microscopic swarms are both major challenges. In this work, we realize the generation of an elliptical paramagnetic nanoparticle swarm (EPNS) with enhanced dexterity for adaptive locomotion, and subsequently a fuzzy control strategy is developed for automatically tuning pattern deformation, orientation, and position of the swarm. By adjusting the input field, the aspect ratio of the EPNS will change accordingly, and we demonstrate its adaptive navigation through curved and narrowed channel by performing pattern reconfigurations. Moreover, using the proposed control strategy, precise matches can be reached between the controlled swarms and the desired patterns. Finally, to show the high compatibility of the control strategy, we employ ribbon-like colloidal swarms driven by oscillating magnetic field, and the results also validate the effectiveness of the strategy.

Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor drift problem, a gas sensor drift compensation approach is proposed based on local discriminant subspace projection (LDSP). The proposed approach aims to find a subspace to reduce the distribution difference between two domains, i.e., the source and target domain. Similar to domain regularized component analysis (DRCA) which is a recently proposed sensor drift correction method, the mean distribution discrepancy is minimized in the common subspace in our approach. LDSP extends DRCA in two aspects, i.e., it not only takes the label information of the source data into consideration to reduce the possibility of the case that samples in the subspace with different class labels stay close to each other, but also borrows the idea of locality-preserving projection to deal with multimodal data. Specifically, inspired by local Fisher discriminant analysis (LFDA), the label information is utilized to maximize the local between-class variance of source data in the latent common subspace and simultaneously minimize the local within-class variance. The formulation of LDSP is a generalized eigenvalue problem that can be readily solved. The experimental results have shown the proposed method outperforms other gas sensor drift compensation methods in terms of classification accuracy on two public gas sensor drift datasets.

This article presents a novel discriminative subspace-learning-based unsupervised domain adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning approaches assume that the gas sensor data follow a certain distribution such as Gaussian, which often does not exist in real-world applications. In this article, we address this issue by proposing a novel discriminative subspace learning method for DA with neighborhood preserving (DANP). We introduce two novel terms, including the intraclass graph term and the interclass graph term, to embed the graphs into DA. Besides, most existing methods ignore the influence of the subspace learning on the classifier design. To tackle this issue, we present a novel classifier design method (DANP+) that incorporates the DA ability of the subspace into the learning of the classifier. The weighting function is introduced to assign different weights to different dimensions of the subspace. We have verified the effectiveness of the proposed methods by conducting experiments on two public gas sensor datasets in comparison with the state-of-the-art DA methods.

Steering microswimmers toward 3-D autonomous manipulation tasks has received extensive attention. Our previous works have accomplished autonomously manipulating microswimmers in the 2-D space. This article aims to extend the 2-D autonomous manipulation to 3-D autonomous manipulation. Specifically, this article addresses the problem of an autonomous system that consists of 3-D path planning and 3-D path following for magnetically driven helical microswimmers. The path-planning algorithm called optimal Bidirectional RRT* is formulated to explore the shortest route in the confined 3-D space. A proxy-based sliding mode control (PSMC) approach is developed to design stable controllers based on the error model in the Serret–Frenet frame. We transport the swimming model trained by a kind of neural network to another new helical microswimmer according to an online updating scheme. The updating scheme can identify and refine compensating angles between the swimming direction of the microswimmer and the magnetic direction in the 3-D space facing the weight disturbances of the swimmer and lateral disturbances. The experiments are conducted to quantitatively validate the 3-D autonomous manipulation system. Experimental results show the effectiveness of path planning and path following with submillimeter accuracy in a 3-D space. Future works will focus on autonomous manipulations in dynamic environments.Note to Practitioners—This article is motivated by the issue of 3-D autonomous manipulation tasks for magnetically driven helical microswimmers. The formulated path planning is responsible for finding the shortest route in the 3-D confined space. The closed-loop controller is charge of steering the helical microswimmers on a reference path based on an online updating model trained by neural networks. It is demonstrated that the helical microswimmer can find the shortest path and follow it in a 3-D space with submillimeter accuracy.

Tactile sensing with spiking neural networks (SNNs) has attracted increasing attention in the past decades. In this article, a novel SNN framework is proposed for the tactile surface roughness categorization task. In contrast to supervised SNN methods such as ReSuMe and Tempotron that require prespecifying target spike trains, the presented method performs the classification through directly comparing the distance between multineuron spike trains. Unlike simple spike train fusion methods using average pairwise spike train distance or pooled spike train distance, the proposed method merges spike trains from different neurons with the multineuron spike train distance, which can capture the complex correlation of multiple spike trains. Specifically, the spike trains are generated via the Izhikevich neurons from tactile signals. The similarity of the multineuron spike trains is computed using the multineuron Victor–Purpura spike train distance, which can be efficiently implemented in an inductive manner. The classification can be performed by incorporating$k$-nearest neighbors and the multineuron spike train distance as a similarity metric. The proposed framework is quite general, i.e., other multineuron spike train distances and spike train kernel-based methods can be readily incorporated. The effectiveness of the proposed method has been demonstrated on a tactile data set by comparing it with various feature- and spike-based methods.Note to Practitioners—In the soft neuromorphic implementation of biomimetic tactile sensing and the development of the tactile sensing capability in neurobotic systems, the processing and analysis of spike-like tactile signals are quite common. Inspired by human tactile perception, this article proposes a novel supervised spiking neural network method for tactile sensing tasks. The traditional methods have to prespecify target spike trains, which is still an open question. In addition, the current ways to fuse spike trains from multiple neurons are far from mature. This article tackles these two problems using spike train similarity comparison with multineuron spike train distance. The direct spike train similarity comparison avoids the need to prespecify target spike trains. The multineuron spike train distance can inherently fuse spike trains from different neurons. It is demonstrated that the proposed method is able to effectively perform classification in a tactile roughness discrimination task.

Microrobots capable of performing minimally invasive surgery, targeted drug delivery, and manipulation of micro-objects have shown great potentials in multiple application areas. However, microrobots assembled by nanoparticles with stable configuration and good performance on closed-loop control are still to be further studied. In this paper, ferromagnetic nanoparticles are used as experimental materials, rather than paramagnetic nanoparticles that require complex synthesis processes, and the advantage is demonstrated, for example, high pattern stability. The locomotion velocity as a function of the magnetic field frequency is modeled, analyzed and verified by experiment. Moreover, a path following experiment based on the arbitrary planar path following algorithm is performed. The nanoparticle microrobots are of great significance for biomedical applications. In the future works, experiments in bio-fluids, multimodal locomotion, and targeted drug delivery tasks will be investigated.

High flexible and high precise manipulation is one of the most critical technique for complex microsystem’s measurement, manufacture, and assembly. Although recent advances in microrobotics have successfully realized the automatic manipulation and positioning of tiny objects, their flexible manipulation in 3-D free space remains a challenge, such as the wide-angle rotation manipulation of microsize sphere, due to the complicate surface forces. Herein, this article proposed a feedforward model and realized the precise rotation for microsized sphere by two cooperative flexible micropipettes. Firstly, a microrobotic manipulation system with six degrees-of-freedom (DOFs) was developed and integrated with the microscope. Then, a feedforward compensation control strategy involving dual rotation was proposed for the precise manipulation of microsized sphere (${\sim }90~\mu \text {m}$) based on the analysis of contact forces. As a result, the rotation of the microsized sphere in two different planes was realized and the microsized sphere release procedure was also accomplished after rotation. Compared with existing techniques only allowing limited amplitudes rotation, this article realizes wide-angle rotation manipulation of microsized sphere in 3-D free space. This research opens new prospects for the microsized object accurate manipulation, which is expected to give a long-term impact for complex microsystem’s manufacture and assembly.Note to Practitioners—This article is motivated by the problem of the flexible manipulation of tiny object in 3-D space. The proposed nanorobotic manipulation system, two micropipettes and feedforward compensation model control strategy could realize the precise translational and rotational manipulation of microbeads in 3-D space, which offers obvious advantages of existing techniques. The proposed system and method could be a general solution for precise and flexible micromanipulation. Thus, it could find wide applications ranging from fundamental research to industrial applications, such as biological cell positioning, characterization of a particular micro/nanoregion, microassembly, and manufacturing.

Controlling magnetic microswimmers toward 3-D manipulation tasks has received considerable attention. Although related studies on manipulating helical microswimmers have been developed, stable closed-loop controls and accuracy swimming models should be still investigated. This article addresses the problem of 3-D path following for magnetically driven helical microswimmers with an adaptive-compensation scheme. The orientation-compensation model in the global coordinate frame is learned by radial basis function (RBF) networks trained with backpropagation algorithms, which is used to express the motion of the helical microswimmer in the presence of the weight of the swimmer and lateral disturbances from the boundary effects. A proxy-based sliding-mode control (PSMC) approach is developed to design stable controllers based on the kinematic error model. The effects of variable parameters and boundary effects are also considered. Experimental results including different paths in 3-D space validated the path following with submillimeter accuracy using the helical microswimmer. Note to Practitioners-This article is motivated by the issue of the following predefined paths for magnetically driven helical microswimmers in 3-D space. The proposed closed-loop controller employs the error model in 3-D space to formulate the control law according to an orientation-compensation model learned by neural networks. It is demonstrated that the helical microswimmer is able to follow different paths in 3-D space with submillimeter accuracy using the proposed control scheme.

Magnetically actuated microswimmers have attracted researchers to investigate their swimming characteristics and controlled actuation. Although plenty of studies on actuating helical microswimmers have been carried out, robust closed-loop controls should be still explored for practical applications. In this paper, we proposed a data-driven model-free method using Image-Based Visual Servoing (IBVS), which uses features directly extracted in the image space as feedbacks. The IBVS method can eliminate camera calibration errors. We have demonstrated with experiments that the proposed IBVS method can enable velocity-independent path following of an arbitrarily given path on the plane, which permits a better experience of user interaction. The proposed control method is successfully applied to obstacle avoidance tasks and has the potential for the application in complex circumstances. This approach is promising for biomedical applications.

In this paper, controlling miniature swimmers via electromagnetic actuation has received considerable attention due to their minor invasive trait in various biomedical applications and ease of passing through the complex environments. Studying the navigation and control system is an essential step towards such applications. Currently, navigation and control for magnetically driven miniature swimmers are still challenging research issues. This paper aims to formulate a navigation and control system of magnetically driven helical miniature swimmers. First, a global planning algorithm named informed optimal random exploring tree (Informed RRT*) is applied to compute the feasible path in cluttered environments. Second, a closed-loop control algorithm is presented to follow various of reference paths using visual feedback on a planar substrate. In particular, a single-hidden layer feedforward neural networks is employed to approximate the mapping relationship between the magnetic self-rotation direction and the actual moving direction of helical miniature swimmers. The neural network is first implemented to control the magnetically driven miniature swimmers in this paper. Experiments are conducted to verify the ability of navigation and visual feedback control tasks.

Untethered and wirelessly-controlled microrobots have many applications in the field of biomedicine. Therefore, many laboratories and scientists have invested more scientific research into magnetic microrobots which can make more contributions to medical care. Many magnetic field devices and microrobots are manufactured. In the development of micro-robots, helical microrobots have been well developed. Rigid-body robots account for the majority of these, but they may cause damage to human organs during treatment. However, soft and deformable robots can relieve more medical restrictions. In general, helical microrobots are driven by uniform fields which have their own limitations while the gradient magnetic field can relieve more restrictions and have more functions. This paper presents a flexible deformable helical swimmer controlled in a rotating gradient magnetic field. Helical swimmers are covered with magnetic nano-particles and the helical structure is derived from the inner fiber structure of the lotus root. The soft helical swimmers are controlled to swim several special trajectories in the rotating gradient magnetic field and we analyze the frequency and other factors for velocity or other effects.

Magnetically actuated microswimmers have shown great potentials in multiple application scenarios, attracting researchers to investigate their swimming characteristics and controlling methods. However, among those studies, only a small number of closed-loop control schemes have been applied, which is crucial for the accuracy and repeatability in applications. In this paper, we proposed an Image-Based Visual Servoing(IBVS) method for arbitrary planar path following using features directly presented in image space as feedbacks. During experiments we found that IBVS guarantees convergence while not requiring camera calibrations. Furthermore, the proposed path following method is intuitive and flexible, and provides great potentials in various applications.

Microrobots show great potential to realize a wide range of medical applications especially in a hard-to-reach region within human body. Among a diversity of medical applications, the microrobots are expected to efficiently perform tasks within the human body, but the existing electromagnetic coil systems such as Helmholtz coil, Maxwell coil, etc. do not compromise on the insertion of such a large volume of human parts. We aim to magnetically manipulate diverse microrobots in medical applications with respect to surgical or biopsy tasks within insiders of the human head such as to perform brachytherapy with the helical microswimmer, etc. For this reason, we proposed a novel, a magnetically actuated system called the HyBrid system with the conceptual design in capability of generating both uniform and gradient magnetic fields. The system consists of three different coil setups orthogonally arranged over a large workspace which is available to let the human head inserted into. The simulation results of the magnetic field generation expressed that its performance is suitable and feasible towards magnetically manipulation within the human head, and more efficient than 3D-Helmholtz coil system under the same conditions.

Paramagnetic nanoparticles are considered as attractive building blocks, particularly for robotic delivery of drugs. Although paramagnetic nanoparticles can be effectively gathered and transported using external magnetic fields, the disassembly process is yet to be fully investigated to avoid the formation of aggregations. In this paper, we report a novel method of controllable disassembly of paramagnetic nanoparticle chains using a predefined dynamic magnetic field. The dynamic field is capable of performing spreading and fragmentation of the particle chains simultaneously. Using the magnetic dipole-dipole repulsive forces, the final area covered by the particle chains swells up to 545% of the initial area. The final length distribution presents a strong relationship with the frequency of the dynamic field in deionized (DI) water and two kinds of biofluids. An analytical model of phase lag is proposed, which shows good agreement with the experimental results. Furthermore, we also present an assembly process using a rotating magnetic field, indicating that the assembly disassembly process is reversible. In addition, batch-cargo delivery of polystyrene microbeads using the nanoparticle chains as swarm-like nanorobots is demonstrated.

We present a general solution for steering microrobotic swarm by dynamic actuating fields. In our approach, the motion of micro-robots is controlled by changing the actuating direction of a field applied to them. The time-series sequence of actuating field's directions can be computed automatically. Given a target position in the domain of swarm, a governing field is first constructed to provide optimal moving directions at every points. Following these directions, a robot can be driven to the target efficiently. However, when working with a crowd of micro-robots, the optimal moving directions on different agents can contradict with each other. To overcome this difficulty, we develop a novel steering algorithm to compute a statistically optimal actuating direction at each time frame. Following a sequence of these actuating directions, a crowd of micro-robots can be transported to the target region effectively. Our steering strategy of swarm has been verified on a platform that generates magnetic fields with unique actuating directions. Experimental tests taken on aggregated magnetic micro-particles are quite encouraging.

Helical microswimmers capable of propulsion at low Reynolds numbers have been proposed for numerous applications. Several different kinds of helical swimmers inspired by E. coli bacteria have been proposed by researchers, and most of them have rigid helical tails. However, high softness could make swimmers more adaptive in confined environments for biomedical applications. This paper aims to study the morphologies and the swimming characteristics of magnetically actuated swimmers with belt-like soft tails initially straight at low Reynolds numbers. We found that a swimmer with a soft tail during rotations shows different morphologies: a twisted shape until the input frequency increases to a threshold frequency, and a helical shape until a step-out frequency. Beyond the stepout frequency, the shape of the soft tail becomes irregular. The soft tail swimmers with different lengths show similar swimming velocities at same rotational frequencies. However, their maximal swimming velocities are different because of the varied step-out frequencies. The interactions between the soft tails reduce the swimming velocity, and this influence increases with the rotational frequency. Thus, the swimming performance is not improved by doubling the number of soft tails in our experiments.