State Key Laboratory of Robotics and Systems

Swimming microrobots that are energized by external magnetic fields exhibit a variety of intriguing collective behaviors, ranging from dynamic self-organization to coherent motion; however, achieving multiple, desired collective modes within one colloidal system to emulate high environmental adaptability and enhanced tasking capabilities of natural swarms is challenging. Here, we present a strategy that uses alternating magnetic fields to program hematite colloidal particles into liquid, chain, vortex, and ribbon-like microrobotic swarms and enables fast and reversible transformations between them. The chain is characterized by passing through confined narrow channels, and the herring school-like ribbon procession is capable of large-area synchronized manipulation, whereas the colony-like vortex can aggregate at a high density toward coordinated handling of heavy loads. Using the developed discrete particle simulation methods, we investigated generation mechanisms of these four swarms, as well as the "tank-treading" motion of the chain and vortex merging. In addition, the swarms can be programmed to steer in any direction with excellent maneuverability, and the vortex's chirality can be rapidly switched with high pattern stability. This reconfigurable microrobot swarm can provide versatile collective modes to address environmental variations or multitasking requirements; it has potential to investigate fundamentals in living systems and to serve as a functional bio-microrobot system for biomedicine.

This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

In this article, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances. The relative-threshold-based event-triggered signal is introduced to reduce communication burden, and the dynamic surface control technique is applied to address the computational complexity problem. A disturbance observer is designed to estimate the compounded disturbances, which are composed of external disturbances and fuzzy approximation errors. The proposed control strategy can guarantee that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set by incorporating the prescribed performance bound in finite-time. Finally, simulation results are provided to verify the effectiveness of the proposed robust fuzzy control strategy.

The problem of fuzzy-based adaptive event-triggered tracking control is investigated for a class of non-strict-feedback nonlinear systems within fixed-time interval in this paper. Fuzzy logic system (FLS) is employed to approximate the packaged unknown nonlinearities. Event-triggered mechanism is employed to schedule the data transmission dependent upon errors exceeding certain threshold. And combining backstepping design algorithm with Lyapunov stability theorem, a fuzzy-based adaptive event-triggered controller is developed. The presented control scheme eliminates the singularity problem that may occur in the design process and guarantees the boundedness of all the closed-loop signals. And the tracking error converges into a small neighborhood of zero in fixed-time. Meanwhile, the Zeno behavior is also avoided. At last, the effectiveness of the proposed approach is demonstrated by simulation results.

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

Magnetically actuated miniature soft robots are capable of programmable deformations for multimodal locomotion and manipulation functions, potentially enabling direct access to currently unreachable or difficult-to-access regions inside the human body for minimally invasive medical operations. However, magnetic miniature soft robots are so far mostly based on elastomers, where their limited deformability prevents them from navigating inside clustered and very constrained environments, such as squeezing through narrow crevices much smaller than the robot size. Moreover, their functionalities are currently restricted by their predesigned shapes, which is challenging to be reconfigured in situ in enclosed spaces. Here, we report a method to actuate and control ferrofluid droplets as shape-programmable magnetic miniature soft robots, which can navigate in two dimensions through narrow channels much smaller than their sizes thanks to their liquid properties. By controlling the external magnetic fields spatiotemporally, these droplet robots can also be reconfigured to exhibit multiple functionalities, including on-demand splitting and merging for delivering liquid cargos and morphing into different shapes for efficient and versatile manipulation of delicate objects. In addition, a single-droplet robot can be controlled to split into multiple subdroplets and complete cooperative tasks, such as working as a programmable fluidic-mixing device for addressable and sequential mixing of different liquids. Due to their extreme deformability, in situ reconfigurability and cooperative behavior, the proposed ferrofluid droplet robots could open up a wide range of unprecedented functionalities for lab/organ-on-a-chip, fluidics, bioengineering, and medical device applications.

This article studies the issue of adaptive neural network (NN) control for strict-feedback multi-input and multioutput (MIMO) nonlinear systems with full-state constraints and actuator hysteresis. Radial basis function NNs (RBFNNs) are introduced to approximate unknown nonlinear functions. The command filter is adopted to solve the issue of "explosion of complexity." By applying a one-to-one nonlinear mapping, the strict-feedback system with full-state constraints is converted into a new pure-feedback system without state constraints, and a novel NN control method is proposed. The stability of the closed-loop system is proved via the Lyapunov stability theory, and the tracking errors converge to small residual sets. The simulation results are given to confirm the validity of the proposed method.

Event-triggered control has been an effective tool in dealing with problems with finite communication and computation resources. In this paper, we design an event-triggered control for nonlinear constrained-input continuous-time systems based on the optimal policy. Constraints on controls are handled using a bounded function. To learn the optimal solution with partially unknown dynamics, an online adaptive dynamic programming algorithm is proposed. The identifier network, the critic network, and the actor network are employed to approximate the unknown drift dynamics, the optimal value, and the optimal policy, respectively. The identifier is tuned based on online data, which further trains the critic and actor at triggering instants. A concurrent learning technique repeatedly uses past data to train the critic. Stability of the closed-loop system, and convergence of neural networks to the optimal solutions are proved by Lyapunov analysis. In the end, the algorithm is applied to the overhead crane system to observe the performance. The event-triggered optimal controller with constraints stabilizes the system and consumes much less sampling times.

Autonomous vehicles require constant environmental perception to obtain the distribution of obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional module as it can simultaneously predict surrounding objects' categories, locations, and sizes. Generally, autonomous vehicles are equipped with multiple sensors, including cameras and LiDARs. The fact that single-modal methods suffer from unsatisfactory detection performance motivates utilizing multiple modalities as inputs to compensate for single sensor faults. Although many multi-modal fusion detection algorithms exist, there is still a lack of comprehensive and in-depth analysis of these methods to clarify how to fuse multi-modal data effectively. Therefore, this paper surveys recent advancements in fusion detection methods. First, we present the broad background of multi-modal 3D object detection and identify the characteristics of widely used datasets along with their evaluation metrics. Second, instead of the traditional classification method of early, middle, and late fusion, we categorize and analyze all fusion methods from three aspects: feature representation, alignment, and fusion, which reveals how these fusion methods are implemented in an essential way. Third, we provide an in-depth comparison of their pros and cons and compare their performance in mainstream datasets. Finally, we further summarize current challenges and research trends for realizing the full potential of multi-modal 3D object detection.

This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.

Abstract Magnetic miniature soft‐bodied robots allow non‐invasive access to restricted spaces and provide ideal solutions for minimally invasive surgery, micromanipulation, and targeted drug delivery. However, the existing elastomer‐based (silicone) and fluid‐based (ferrofluid or liquid metal) magnetically actuated miniature soft robots have limitations. Owing to its limited deformability, the elastomer‐based small‐scale soft robot cannot navigate through a highly restricted environment. In contrast, although fluid‐based soft robots are more capable of deformation, they are also limited by the unstable shape of the fluid itself, and are therefore poorly adapted to the environment. In this study, non‐Newtonian fluid‐based magnetically actuated slime robots with both the adaptability of elastomer‐based robots and reconfigurable significant deformation capabilities of fluid‐based robots are demonstrated. The robots can negotiate through narrow channels with a diameter of 1.5 mm and maneuver on multiple substrates in complex environments. The proposed slime robot implements various functions, including grasping solid objects, swallowing and transporting harmful things, human motion monitoring, and circuit switching and repair. This study proposes the design of novel soft‐bodied robots and enhances their future applications in biomedical, electronic, and other fields.

A self-healable polyvinyl alcohol-based hydrogel electrolyte is synthesized for smart electrochemical capacitors with self-healing and tailorable characteristics.

Computational meta-optics brings a twist on the accelerating hardware with the benefits of ultrafast speed, ultra-low power consumption, and parallel information processing in versatile applications. Recent advent of metasurfaces have enabled the full manipulation of electromagnetic waves within subwavelength scales, promising the multifunctional, high-throughput, compact and flat optical processors. In this trend, metasurfaces with nonlocality or multi-layer structures are proposed to perform analog optical computations based on Green's function or Fourier transform, intrinsically constrained by limited operations or large footprints/volume. Here, we showcase a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing upon our single-layer Huygens' metasurface. Basic mathematical operations, including differentiation and cross-correlation, are performed by directly modulating complex wavefronts in spatial Fourier domain, facilitating edge detection and pattern recognition of various image processing. Our work substantiates an ultracompact and powerful kernel processor, which could find important applications for optical analog computing and image processing.

Recent progress of untethered mobile micromotors has shown immense potential for targeted drug delivery in vivo. However, designing a wireless micromotor with high maneuverability and biocompatibility and achieving controlled drug release with high efficiency at a specific position remains a great challenge. Herein, we present a pine pollen-based micromotor (PPBM) and demonstrate its potential application as a cargo carrier for targeted drug delivery. These multifunctional biohybrid micromotors were massively and inexpensively fabricated by the encapsulation of magnetic particles (Fe3O4) and medicine into the two hollow air sacs of pine pollen, via vacuum loading. PPBMs successfully inherit the intrinsic functionalities of pine pollen: structural uniformity, morphological stability, biocompatibility, autofluorescence (AF) and physicochemical robustness. Under an external magnetic field, the loaded Fe3O4 enables individual and swarm PPBMs to propel precisely in complex biological fluids. Capitalizing on the magnetic nanoparticle aggregation phenomenon under a powerful magnetic field, controlled release of the therapeutic cargo is achieved using a fluid field generated by the rotating magnetic agglomerate. The biohybrid micromotors reported here turn natural pine pollen into active and controllable cargo carriers for biomedical applications.

Highly stretchable and conductive core–sheath nanofibers are significant for flexible and wearable microelectronics. Core–sheath fibers were massively fabricated from ultralong chemical vapor deposition (CVD)-grown graphene bundles. They exhibited superior conductivity and excellent mechanical properties that exceeded those of the reduced graphene oxide fibers. The intrinsic dynamic fracture procedure and mechanism of the core–sheath nanofibers were investigated. Furthermore, safe strain sensors based on as-prepared core–sheath CVD graphene fibers have been demonstrated as a proof-of-concept application. The performance of strain sensors has been greatly improved by using CVD graphene fibers.

Abstract Developing microrobots with multiple deformabilities has become extremely challenging due to the lack of materials that are soft enough at the microscale level and the inability to be reconfigured after fabrication. In this study, it is aimed to prove that liquid microrobots composed of ferrofluid droplets are inherently deformable and they can be controlled, individually or in aggregate, with multiple programmable deformabilities. For example, the liquid‐microrobot monomer (LRM) can pass through narrow channels via elongation and achieve scaling via splitting and coalescence. LRMs can also reassemble into various kinds of functional liquid‐robot aggregates, such as microsticks, micropies, microtrains, microkayaks, and microrollingpins. Thus, they can respond to multi‐terrain surfaces or perform various complex tasks. Moreover, the authors' physics‐based theoretical model demonstrates dynamic self‐assembly and group behavior of a multiple LRM system, which is conducive to investigating the mechanisms behind it. These ferrofluid droplet robots provide novel solutions for some potential applications, such as untethered micromanipulation and targeted cargo delivery.

A piezoelectric actuator, which is constructed by bonding six pieces of lead zirconate titanate (PZT) ceramic plates on a step aluminum alloy beam, was proposed and tested for the design of a small-size rotary driving appratus. Two pieces of PZT ceramic plates bonded in the middle part is used to generate the first bending mode, whereas the other four on the two sides are set for the excitation of the second bending mode; their superimposition can produce elliptical movements on the two ends of the beam, which can rotate a disk-shaped rotor. Compared with the traditional ring-shaped traveling-wave piezoelectric actuator, the proposed actuator has a simpler structure and operating principle; it also gives a new mode for rotary driving. The resonance frequencies of the first and second bending modes were designed to be close at about 21.1 kHz. The maximum no-load speed and torque were tested to be 158 r/min and 0.053 N · m, respectively. The prototype achieved a power density of 19.0 W/kg under a weight of 15.8 g. The proposed combination plan of the first and second bending modes is very suitable for constructing a small-size piezoelectric actuator, which exhibits merits for application in small systems.

The lack of high-performance tactile sensors, especially for pressure/force, is a huge obstacle for the widespread application of intelligent robots. Current pressure sensors are often operated in the high range of pressure and normal direction, showing a little ability in the low range of pressure and three-axis direction simultaneously. Herein, a highly sensitive flexible tactile sensor with three-axis force sensing capacity is presented by combining microstructured polydimethylsiloxane (PDMS) arrays and a reduced graphene oxide (rGO) film. The deformation of microstructured rGO/PDMS results in a change in the contact area between the rGO film and electrode, leading to a high sensitivity of -1.71 kPa-1 in the low range pressure of 0-225 Pa with a fast response time of 6 ms at a large feature size of 100 μm. To realize three-axis sensing, a sensing unit was built up, which was composed of the adjacent four parts of patterns and electrodes underneath a bump. A mechanical model of the exerted spatial force was established to calculate each axis force component via the deformation of the rGO/PDMS pattern. The experimental results show that the current difference between the adjacent two parts has a strong relationship with the applied force. As a proof of concept, we have demonstrated a 3 × 3 array sensor for arbitrary force sensing. Our tactile sensor would be used in transmitting information from a gentle spatial force and would exhibit broad applications as e-skin in integrated robots.

This paper investigates the problem of delay-dependent H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> memory filtering for continuous-time semi-Markovian jump linear systems (MJLSs) with time-varying delay in an input-output framework. Differing from the constant transition rates (TRs) in the conventional MJLSs, the TRs of the semi-MJLSs depend on the random sojourn-time and are thus with time-varying characteristics. By utilizing a two-term approximation for the terms with time-varying delay, it is first shown that the filtering error system (FES) can be reformulated into a feedback interconnection form and the stability and performance analysis problem of the FES can be recast as the scaled small gain (SSG) problem of an interconnected system. Then, based on a semi-Markovian Lyapunov-Krasovskii formulation of SSG condition combined with projection lemma, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter synthesis for the underlying semi-MJLSs is formulated in terms of linear matrix inequalities. Finally, simulation studies are provided to evaluate the effectiveness and superiority of the proposed design method.