june W

Magnetic particle imaging (MPI) is a nascent modality that can provide imaging guidance for magnetic particle hyperthermia (MPH) to treat cancer. For MPI, magnetic particles are both imaging tracers and therapeutic heaters. A particle ranking system is validated, which condenses particle imaging and heating performance into a single metric to aid particle selection.

Abstract

Magnetic particle imaging (MPI) is an emerging modality that can address longstanding technological challenges encountered with magnetic particle hyperthermia (MPH) cancer therapy. MPI is a tracer technology compatible with MPH for which magnetic nanoparticles (MNPs) provide signal for MPI and heat for MPH. Identifying whether a specific MNP formulation is suitable for both modalities is essential for clinical implementation. Current models predict that functional requirements of each modality impose conflicting demands on nanoparticle magnetic properties. This objective here is to develop a measurement and ranking scheme based on end-use performance to streamline evaluation of candidate MNP formulations. The measured MPI point-spread function (PSF) and specific loss power (SLP) is combined to generate a single numerical value for comparison on a relative ranking scale, or figure of merit (FoM). 12 aqueous iron-containing formulations are evaluated, including FDA-approved (parenteral) iron-containing colloids. MNPs with high (Synomag-D70: 123.4), medium (Synomag-D50: 63.2), and low (NanoXact: 0.147) FoM values are selected for in vivo validation of the selection scheme in subcutaneous 4T1 tumors. Results demonstrate that the proposed ranking accurately assessed the relative performance of MNPs for MPI and MPH. Data demonstrated that image quality and tumor temperature rise increased with FoM ranking, validating predictions. It isshown that the MPI signal correlated with MNP concentration in tissue. Computational heat transfer models anchored on tumor MPI data harmonized with experimental results to within an average of 2 °C when MNP content estimated from MPI data is included. Computational studies emphasized the importance of post-injection MNP quantitation and MPI spatial resolution.

A new concept of hierarchically reconfigurable strategy and elastically-guided multimodal actuation design principle for modular soft robots is reported in this work. With the delicate experiments and analysis, the performances of the modules are revealed. Subsequently, multiple types of robots are demonstrated, including a tube-detecting robot, a planar omnidirectional crawling robot, and several soft manipulators.

Abstract

Reconfigurable soft robots exhibit superior flexibility and adaptability when coping with complex environments and variable tasks. However, conventional modular design strategy based on soft actuator modules with specific structure and actuation mode as the basic constructing element for reconfigurable soft robots always has limited reprogrammability or scalability. Here, a hierarchical reconfigurable strategy is reported that not only offers conventional module reconfiguration as the first level to build different robot prototypes, but also provides the elastically-guided multimodal actuation (contraction, bend, and twist) as the second level which can be reprogrammed to construct different actuator modules. This strategy deepens the modularity to a higher dimensionality and provides more choices for robots to adjust various conformations and functions as needed, for example, a peristalsis robot, an omnidirectional crawling robot, and a soft manipulator can be easily constructed using the module-level reconfiguration. Moreover, soft manipulators with various preprogrammed deformation trajectories based on the mode reconfiguration are proven to dramatically reduce the operation difficulty and cost in potential applications such as environment detection and human-robot interaction. This work provides a hierarchical framework for reconfigurable soft robots and may open up a new way for modular design.

Magnetic Actuators

In article number 2400369, Shengzhi Sun, Yi Zhu, and co-workers propose a fabrication method of a sensor-equipped magnetic actuator (SEMA). The SEMA consists of a magnetic actuator and a temperature-sensitive unit, exhibiting excellent position controlling and temperature monitoring performance. This method of equipping sensors with micro actuators can bring many potential applications in microenvironment sensing, micro-mechanical manipulating, and micro-cargo transporting.

This research presents an adapted thermal drawing platform for cost-effective and rapid prototyping of catheters for MR-guided endovascular interventions. The benefits of the proposed platform are demonstrated in the development of advanced catheter systems, exhibiting excellent mechanical properties and MR visibility. Successful in vitro and in vivo testing underscores their potential for advanced cardiovascular procedures under MR guidance.

Abstract

Cardiovascular diseases (CVDs), including congenital heart diseases (CHD), present significant global health challenges, emphasizing the need for safe and effective treatment modalities. Fluoroscopy-guided endovascular interventions are widely utilized but raise concerns about ionizing radiation, especially in pediatric cases. Magnetic resonance imaging (MRI) offers a radiation-free alternative with superior soft tissue visualization and functional insights. However, the lack of compatible instruments remains a major obstacle. An adapted thermal drawing platform that enables low-cost and rapid prototyping of instruments for MR-guided endovascular interventions is introduced. This platform is demonstrated through the development of two exemplary catheter systems: a tendon-driven steerable catheter with helical lumina and an active tracking Tiger-shaped catheter with an embedded coaxial wire. These catheters exhibit mechanical properties comparable to commercial counterparts and show promising outcomes in both in vitro and in vivo feasibility testing. This scalable thermal drawing platform addresses the limitations of existing manufacturing approaches and facilitates the exploration of diverse designs, potentially accelerating advancements in catheter technologies for MR-guided cardiovascular interventions.

Here, an injectable magnetic hydrogel nanosystem for the dual-purpose magnetothermal and anti-inflammatory treatment of endometriosis is well designed. Upon magnetic activation, the hydrogel containing Fe₃O₄ nanoparticles can induce a localized hyperthermic response, effectively destroying endometriotic cells and facilitating the release of anti-inflammatory peptide. Consequently, this work offers a promising non-invasive treatment modality for synergistical magnetothermal and anti-inflammatory treatment of endometriosis.

Abstract

Endometriosis is a prevalent gynecological condition characterized by chronic pelvic pain, dysmenorrhea, and infertility, affecting ≈176 million women of reproductive age worldwide. Current treatments, including pharmacological and surgical interventions, are often associated with significant side effects and high recurrence rates. Consequently, there is an urgent need for innovative and safer therapeutic approaches. In this study, an injectable magnetic hydrogel nanosystem is developed designed for the dual-purpose magnetothermal and anti-inflammatory treatment of endometriosis. This hydrogel incorporates Fe₃O₄ nanoparticles alongside an anti-inflammatory peptide. Upon magnetic activation, the Fe₃O₄ nanoparticles induce a localized hyperthermic response, raising the temperature of endometriotic lesions to 63.3 °C, effectively destroying endometriotic cells. Concurrently, the thermally responsive hydrogel facilitates the controlled release of the anti-inflammatory peptide, thus modulating the inflammatory milieu. The biocompatibility and complete in vivo degradability of the hydrogel further enhance its therapeutic potential. The in vivo studies demonstrated that this injectable magnetic hydrogel system achieved a 90% reduction in the volume of endometriotic lesions and significantly decreased inflammatory markers, offering a promising non-invasive treatment modality for endometriosis. By integrating precise lesion ablation with the modulation of the inflammatory microenvironment, this system represents a novel approach to the clinical management of endometriosis.

An integrated technique to fabricate fillable microrobotic systems is developed based on microfluidic loading with dip sealing (MLDS). The fillable chamber design isolates the loaded cargo from its surroundings, maintains cargo stability, and facilitates high loading capacity. This technique expands opportunities in microrobotics for targeted on-demand cargo release and biosensing with an all-in-one fabrication method.

Abstract

Microrobots can provide spatiotemporally well-controlled cargo delivery that can improve therapeutic efficiency compared to conventional drug delivery strategies. Robust microfabrication methods to expand the variety of materials or cargoes that can be incorporated into microrobots can greatly broaden the scope of their functions. However, current surface coating or direct blending techniques used for cargo loading result in inefficient loading and poor cargo protection during transportation, which leads to cargo waste, degradation and non-specific release. Herein, a versatile platform to fabricate fillable microrobots using microfluidic loading and dip sealing (MLDS) is presented. MLDS enables the encapsulation of different types of cargoes within hollow microrobots and protection of cargo integrity. The technique is supported by high-resolution 3D printing with an integrated microfluidic loading system, which realizes a highly precise loading process and improves cargo loading capacity. A corresponding dip sealing strategy is developed to encase and protect the loaded cargo whilst maintaining the geometric and structural integrity of the loaded microrobots. This dip sealing technique is suitable for different materials, including thermal and light-responsive materials. The MLDS platform provides new opportunities for microrobotic systems in targeted drug delivery, environmental sensing, and chemically powered micromotor applications.

Nature, Published online: 27 September 2024; doi:10.1038/d41586-024-03171-1

Biocompatible Liquid Embolic

This cover demonstrates the various embolic options to microvascular bleeding. Microbeads randomly distribute occluding vessels based on their diameter. Coils and Obsidio gel embolic only occlude the larger upstream blood vessels. In beads, coils, and gels, distal microvascular bleeding continues to occur consistent with the clinical experience. P-LE, however, is biocompatible, does not require a polymerization step or toxic DMSO for delivery, and can reach distal microvessels achieving instant hemostasis. More details can be found in article number 2403615 by Rahmi Oklu and co-workers.

This study presents a new animal model of magnetically controlled scalable nerve injury (mSNI), which exerts quantitative and off-body controllable compression injury to the rat sciatic nerve via a 3D-printed, implantable, and magnetically controlled clamp. This model can accurately control the injury properties, thus facilitating precise investigations into peripheral nerve injury (PNI), regeneration, and neuropathic pain.

Abstract

Animal models of peripheral nerve injury (PNI) serve as the fundamental basis for the investigations of nerve injury, regeneration, and neuropathic pain. The injury properties of such models, including the intensity and duration, significantly influence the subsequent pathological changes, pain development, and therapeutic efficacy. However, precise control over the intensity and duration of nerve injury remains challenging within existing animal models, thereby impeding accurate and comparative assessments of relevant cases. Here, a new model that provides quantitative and off-body controllable injury properties via a magnetically controlled clamp, is presented. The clamp can be implanted onto the rat sciatic nerve and exert varying degrees of compression under the control of an external magnetic field. It is demonstrated that this model can accurately simulate various degrees of pathology of human patients by adjusting the magnetic control and reveal specific pathological changes resulting from intensity heterogeneity that are challenging to detect previously. The controllability and quantifiability of this model may significantly reduce the uncertainty of central response and inter-experimenter variability, facilitating precise investigations into nerve injury, regeneration, and pain mechanisms.

A magnetic barbell-shaped soft microrobot (MBS²M) is proposed inspired by the natural locomotion of crucian carp with the advantages of environmental adaptation, flexible movement, precise positioning, and multifunctionality. The proposed microrobot provides a “all-in-one” solution for sophisticated tasks in complex environments, which has excellent application potential and is expected to play an important role in biomedical applications such as drug delivery, detection, and diagnosis in vivo.

Abstract

The development of environmentally adaptive solutions for magnetically actuated microrobots to enable targeted delivery in complex and confined fluid environments presents a significant challenge. Inspired by the natural locomotion of crucian carp, a barbell-shaped soft microrobot (MBS²M) is proposed. A mechano-electromagnetic hybrid actuation system is developed to generate oscillating magnetic fields to manipulate the microrobot. The MBS²M can seamlessly transition between three fundamental locomotion modes: fast navigation (FN), high-precision navigation (HPN), and fixed-point rotation (FPR). Moreover, the MBS²M can move in reverse without turning. The multimodal locomotion endows the MBS²M's adaptability in diverse environments. It can smoothly pass through confined channels, climb over obstacles, overcome gravity for vertical motion, track complex pathways, traverse viscous environments, overcome low fluid resistance, and navigate complex spaces mimicking in vivo environments. Additionally, the MBS²M is capable of drug loading and release in response to ultrasound excitation. In an ex vivo porcine liver vein, the microrobot demonstrated targeted navigation under ultrasound guidance, showcasing its potential for specialized in vivo tasks.

Magnetic Millirobots

In article number 2308382, Tian Qiu and co-workers report the first electrocauterization performed by a team of millirobots. They form a convoy to collaboratively transport an electric wire or a catheter through the bile duct. It opens up the possibility to use a team of miniature robots in future endoscopic surgery.

The Dual-Strategy Materials Intelligent Design Framework (DSMID) tackles the challenges of small data in materials intelligent design. By enhancing property prediction and efficiently screening experimental candidates, DSMID successfully identifies a new eutectic High Entropy Alloy with significantly improved plasticity and strength, providing a powerful solution for material intelligent design in data-constrained scenarios.

Abstract

Small data in materials present significant challenges to constructing highly accurate machine learning models, severely hindering the widespread implementation of data-driven materials intelligent design. In this study, the Dual-Strategy Materials Intelligent Design Framework (DSMID) is introduced, which integrates two innovative methods. The Adversarial domain Adaptive Embedding Generative network (AAEG) transfers data between related property datasets, even with only 90 data points, enhancing material composition characterization and improving property prediction. Additionally, to address the challenge of screening and evaluating numerous alloy designs, the Automated Material Screening and Evaluation Pipeline (AMSEP) is implemented. This pipeline utilizes large language models with extensive domain knowledge to efficiently identify promising experimental candidates through self-retrieval and self-summarization. Experimental findings demonstrate that this approach effectively identifies and prepares new eutectic High Entropy Alloy (EHEA), notably Al₁₄(CoCrFe)₁₉Ni₂₈, achieving an ultimate tensile strength of 1085 MPa and 24% elongation without heat treatment or extra processing. This demonstrates significantly greater plasticity and equivalent strength compared to the typical as-cast eutectic HEA AlCoCrFeNi_2.1. The DSMID framework, combining AAEG and AMSEP, addresses the challenges of small data modeling and extensive candidate screening, contributing to cost reduction and enhanced efficiency of material design. This framework offers a promising avenue for intelligent material design, particularly in scenarios constrained by limited data availability.

Nature Materials, Published online: 11 September 2024; doi:10.1038/s41563-024-02007-7

Nature Materials, Published online: 02 October 2024; doi:10.1038/s41563-024-02010-y

This article presents the design and characterization of a dual-functional element, serving as both a mechanochromic sensor and soft end-effector. Utilizing spiropyran-functionalized polydimethylsiloxane and a compact load-transfer system, it enables precise stress detection during mechanical interactions. Looking ahead, integrating this technology into robotic manipulators has the potential to enhance exteroceptive sensing for improved performance in complex tasks.

Advancements in smart soft materials are enhancing the capabilities of robotic manipulators in object interactions and complex tasks. Mechanochromic materials, acting as lightweight sensors, offer easily interpretable visual feedback for localized stress detection, structural health monitoring, and energy-efficient robotic skins. Herein, an innovative mechanochromic soft end-effector capable of discerning local contact stresses during mechanical interactions with objects is presented and their relative position is ascertained. This system utilizes a reversible force-induced color switch in a thin layer of spiropyran-functionalized polydimethylsiloxane, which coats a silicone-made suction cup. The mechanochromic suction cup is integrated with a 3D-printed compact load-transferring system and electronic color-changing detection elements. The assembly may serve as a synthetic receptor for robotic actuators, discerning localized interaction forces down to 3 N. The system's resilience to varying environmental factors, including illumination, tilting, and interaction with objects of various shapes is verified. The results indicate potential for exteroceptive solutions in reconfigurable manipulation tasks without compromising the overall softness of the manipulator.

The study presents a control strategy for navigating optical microrobotic swarms through complex environments. By confining microrobots within a manipulation domain, the strategy ensures swarm integrity and efficient obstacle avoidance. It addresses challenges like swarm collapse and anomalous interactions, enhancing robustness. This approach advances optical microrobots’ capabilities in precise medicine, cell transport, drug delivery, and nanosurgery.

The local force field generated by light endows optical microrobots with remarkable flexibility and adaptivity, promising significant advancements in precise medicine and cell transport. Nevertheless, the automated navigation of multiple optical microrobots in intricate, dynamic environments over extended distances remains a challenge. Herein, a versatile control strategy aimed at navigating optical microrobotic swarms to distant targets under obstacles of varying sizes, shapes, and velocities is introduced. By confining all microrobots within a manipulation domain, swarm integrity is ensured while mitigating the effects of Brownian motion. Obstacle's elliptical approximation is developed to facilitate efficient obstacle avoidance for microrobotic swarms. Additionally, several supplementary functions are integrated to enhance swarm robustness and intelligence, addressing uncertainties such as swarm collapse, particle immobilization, and anomalous laser–obstacle interactions in real microscopic environments. We further demonstrate the efficacy and versatility of our proposed strategy by achieving autonomous long-distance navigation to a series of targets. This strategy is compatible with both optical trapping- and nudging-based microrobotic swarms, representing a significant advancement in enabling optical microrobots to undertake complex tasks such as drug delivery and nanosurgery and understanding collective motions.

Space-wall-climbing robots struggle with spacecraft surfaces, especially rough ones in microgravity. We developed a gecko-inspired adhesive material and variable-stiffness paw, achieving 180N adhesion on smooth surfaces. Integrated into a robot, this paw excels in microgravity. Satellite climbing and capture tests confirm its effectiveness, indicating potential for in-orbit services and spacecraft recovery.

Space-wall-climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion-based wall-climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a microgravity environment. A novel adhesive material is developed inspired by the adhesion mechanism and locomotion of the Gekko gecko. This material exhibits exceptional adhesion across various materials and surface roughness. A variable-stiffness gecko-inspired paw is engineered, generating substantial adhesion forces while minimizing detachment forces. Impressively, this paw generates up to 180 N of adhesion force on smooth surfaces and achieves detachment without external forces. By integrating such variable-stiffness paws with a wall-climbing robot, a gecko-inspired robot effectively operating in a microgravity environment is created. The robotic satellite surface climbing experiments and robotic satellite capture experiments are conducted using a simulated microgravity environment and a satellite model. The results unequivocally demonstrate the gecko-inspired robot's proficiency in executing various functions, including stable motion and capture on both smooth and rough spacecraft surfaces within a microgravity environment. These experiments underscore the potential of adhesion-based gecko-inspired robots for in-orbit services and spacecraft capture and recovery.

Soft Underwater Robots

Soft robots hold great significance for underwater operations due to their exceptional compliance and adaptability. In article number 2300688, Qin Fang, Zhefeng Gong, Haojian Lu, and co-workers introduce a transparent reconfigurable soft underwater robot with variable stiffness capability. The cover image shows a soft gripper for delicate underwater grasping and a soft manipulator for exploration in confined underwater environments, highlighting the robot’s potential for future underwater applications.

This article introduces an extended Cartesian space robot control framework using virtual force-tracking impedance control to improve end-effector trajectory tracking. An assumed virtual surface near the desired trajectory generates a contact force, which is controlled by an impedance model to maintain a constant desired force. Optimal impedance parameters and super twisting sliding mode control enhance robustness and accuracy. Experiments validate the approach, showing superior trajectory tracking compared to position control alone.

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end-effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end-effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque-controlled robot end-effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end-effector deviates from the desired trajectory, the constant force-tracking impedance control generates a compliance trajectory that regulates the end-effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed-loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force-tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force-tracking impedance control improves the end-effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

This study explores strategies for embedding intelligence in soft robots to adapt to complex real-world environments. It classifies robots that embed intelligence by adaptive shape, functionality, and mechanics and by computational approach: centralized, decentralized, or embedded. The study concludes that a tailored design and a new perspective on embodied intelligence, called “mechanical intelligence,” is essential for optimizing soft robots.

Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real-world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so-called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real-world environment.

Explainable artificial intelligence (XAI) provides transparent deep learning explanations. This article introduces self-supervised automatic semantic interpretable XAI (AS-XAI), a framework using orthogonal embedding spaces and principal component analysis (PCA) for global semantic interpretation, and offers effective interpretability for convolutional neural networks (CNNs), including out-of-distribution (OOD) category interpretation and species classification, with minimal computational cost.

Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for “black-box” deep learning models. However, it remains difficult for existing methods to achieve the trade-off of the three key criteria in interpretability, namely, reliability, understandability, and usability, which hinder their practical applications. In this article, we propose a self-supervised automatic semantic interpretable explainable artificial intelligence (AS-XAI) framework, which utilizes transparent orthogonal embedding semantic extraction spaces and row-centered principal component analysis (PCA) for global semantic interpretation of model decisions in the absence of human interference, without additional computational costs. In addition, the invariance of filter feature high-rank decomposition is used to evaluate model sensitivity to different semantic concepts. Extensive experiments demonstrate that robust and orthogonal semantic spaces can be automatically extracted by AS-XAI, providing more effective global interpretability for convolutional neural networks (CNNs) and generating human-comprehensible explanations. The proposed approach offers broad fine-grained extensible practical applications, including shared semantic interpretation under out-of-distribution (OOD) categories, auxiliary explanations for species that are challenging to distinguish, and classification explanations from various perspectives. In a systematic evaluation by users with varying levels of AI knowledge, AS-XAI demonstrated superior “glass box” characteristics.

This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.

Bioinspired soft robotics is an emerging field that aims to develop flexible and adaptive robots inspired by the movement and capabilities of biological organisms. This review article examines recent advances in materials, actuation mechanisms, sensors, and control strategies and discusses the challenges and future prospects of bioinspired soft robotics. Key innovations highlighted include pneumatic, elastomer actuators, variable-length shape memory alloy tendons, closed-loop control with soft sensors, and the incorporation of soft materials including shape memory polymers and conductive composites. Challenges in soft robotics such as achieving complex motion control, incorporating feedback systems, modeling soft material dynamics, and replicating biological muscle efficiency with artificial muscles are also discussed. Promising future directions are explored including the integration of biodegradable materials, machine learning-based control algorithms, and leveraging data-driven techniques for modeling and control. Building on progress in multi-functional materials, manufacturing techniques, and bioinspired design principles, soft robots hold considerable promise for expanding robot capabilities, enhancing versatility and adaptability, enabling applications from wearable assistive devices to search and rescue operations. This review provides a holistic perspective encompassing key drivers propelling innovations in the vibrant field of bioinspired soft robotics.

Helical Antennas

In article number 2400503, Xiaochen Chen, Wen Huang, and co-workers present helical antennas that can be manufactured on wafers, which can form arrays to transmit signals in the terahertz band with high gain.

This work introduces a deep learning-assisted triboelectric whisker sensor array (TWSA) for 3D motion estimation and near-field perception in unmanned underwater vehicles. The TWSA detects flow velocity and direction. It achieves 81.2% accuracy in wake vortex detection and enables precise 3D trajectory estimation with a root mean square error of 0.02, significantly enhancing underwater vehicle navigation capabilities.

Abstract

Aquatic animals can perceive their surrounding flow fields through highly evolved sensory systems. For instance, a seal whisker array understands the hydrodynamic field that allows seals to forage and navigate in dark environments. In this work, a deep learning-assisted underwater triboelectric whisker sensor array (TWSA) is designed for the 3D motion estimation and near-field perception of unmanned underwater vehicles. Each sensor comprises a high aspect ratio elliptical whisker shaft, four sensing units at the root of the elliptical whisker shaft, and a flexible corrugated joint simulating the skin on the cheek surface of aquatic animals. The TWSA effectively identifies flow velocity and direction in the 3D underwater environments and exhibits a rapid response time of 19 ms, a high sensitivity of 0.2V/ms ⁻¹, and a signal-to-noise ratio of 58 dB. The device also locks onto the frequency of the upstream wake vortex, achieving a minimal detection accuracy of 81.2%. Moreover, when integrated with an unmanned underwater vehicle, the TWSA can estimate 3D trajectories assisted by a trained deep learning model, with a root mean square error of ≈0.02. Thus, the TWSA-based assisted perception holds immense potential for enhancing unmanned underwater vehicle near-field perception and navigation capabilities across a wide range of applications.