Search Results (455)

When operating in complex and obstacle-dense environments, micro UAV swarms often face severe cooperative positioning failures due to transient non-line-of-sight (NLOS) interference and cascaded inertial sensor drift. To address this, this work proposes a fault-tolerant positioning framework integrating multi-agent deep reinforcement learning with cooperative extended Kalman filtering (MADRL-CEKF). The system incorporates a link-level dynamic soft isolation mechanism that dynamically adjusts observation covariance to effectively sever paths of cooperative error contagion. An adaptive Markov smoothing constraint is mathematically embedded to mitigate high-frequency control jitter typical of AI-driven policies. Crucially, the framework implements a resource-aware multi-objective reward architecture tailored for micro UAVs. Evaluated through high-fidelity simulations and offline physical datasets, the proposed framework achieves a 96.01% reduction in average tracking error (RMSE) under extreme multi-node cascaded failures, completely preventing system divergence. Furthermore, through autonomous multi-objective trade-offs, the system reduces processing delay by 44% (to 25.1 ms) and computational energy consumption by 41% with only a marginal accuracy compromise of 0.16 m, strictly keeping the execution time within the 50 ms real-time threshold. The MADRL-CEKF framework effectively bridges the gap between sophisticated AI decision-making and strict engineering constraints, providing a highly robust and resource-efficient navigation paradigm for swarm robotics. Full article

Achieving precise docking, reliable locking and damage-free emergency unlocking under complex ocean current conditions remains a key challenge for deep-water multi-way quick connectors (MQCs). This study proposes a novel MQC prototype characterised by a tiered tolerance guidance mechanism, an innovative L-shaped spatial helical cam locking system, and a real-time visual attitude indicator. Using Ansys 2023 R2 and its tools, the safe operating limits were determined through explicit non-linear finite element collision analysis. The results demonstrate that, under a controlled docking speed of 10 mm/s, the hierarchical guidance mechanism successfully accommodated extreme initial misalignments (25 mm lateral offset, 5° horizontal rotation and 15° axial rotation), whilst keeping the peak collision stress within the elastic limit. Furthermore, the L-shaped locking guide was analysed using a fifth-order polynomial motion law and a macro-micro elastoplastic Hertzian contact mechanics model, effectively eliminating rigid-flexible impact forces. Under extreme separation loads of 10,000 psi, the maximum equivalent plastic strain at the base of the locking shaft was strictly controlled at 0.00926. This is well below the failure threshold of 0.0865 specified by ASME, providing a substantial safety margin and completely preventing local yielding. Crucially, the emergency release strategy based on precision locating pins was validated through full-scale prototype testing. Destructive tests conducted under simulated severe jamming conditions demonstrated clean, damage-free disengagement under shear torques ranging from 2100 Nm to 2200 Nm. This threshold ensures that accidental triggering will absolutely not occur during routine operations (1400 Nm) and establishes a safe underwater robotic (ROV) operating speed of ≤4 r/min. This study provides a robust theoretical framework and empirical data for the future design of yield-resistant subsea connectors and safe emergency recovery. Full article

The emergence of organic–inorganic hybrid composites integrating magnetic nanoparticles (MNPs) with polymers has been an important advancement in modern biological research. Among these systems, magnetic sodium alginate (SA)-based hydrogels (MSABHs), produced by embedding MNPs within an SA framework, exhibit remarkable potential for biomedical applications owing to their high biocompatibility, rapid magnetic response, controllable spatiotemporal behavior, and remote, non-invasive operation. Under the influence of an alternating magnetic field (AMF), MSABHs can exhibit various responses, including deformation, motion, and thermal generation, which are highly valuable for diagnostic and therapeutic medical applications. This review first outlines the key studies on SA and MNPs, along with the various synthesis routes used to fabricate MSABHs. Subsequently, the discussion primarily focuses on their versatile biomedical applications, including tissue engineering, targeted drug delivery, thermotherapy, imaging, and micro-robotics, followed by an evaluation of current challenges and prospects for future improvement. Through this comprehensive examination and synthesis, the review aims to further reveal the full potential of MSABHs and broaden their applications in the biological domain. Full article

Although the loading and targeted release of drugs are core biomedical applications of micro-nano robots, they are restricted by the complexity of robot fabrication and drug loading/release regulation. This work adopts magnetic microspheres (MMs) as microrobot bodies, owing to their low driving resistance and ease of preparation, in order to explore the loading and release of anticancer drugs via physical adsorption and chitosan surface functionalization. Two modification routes, chitosan solution (CS) and chitosan colloid (CC), were compared in terms of their efficacy in fabricating magnetic chitosan microspheres (MCMs). The dispersion procedure of chitosan gel (CG)-encapsulated MMs was optimized to obtain microspheres with uniform size and good encapsulation. Doxorubicin (DOX) was used as a model drug, and the optimized microstructure exhibited high loading efficiency and excellent controlled release. This study offers a low-cost strategy to advance micro-nano robots toward targeted drug delivery applications. Full article

Autonomous omnidirectional vehicles that combine redundant holonomic kinematics, ROS 2/micro-ROS implementation, and simulation-to-real validation remain limited in the literature. This paper presents an eight-wheel Mecanum autonomous mobile robot for campus navigation in environments shared with pedestrians. The work formulates forward and inverse kinematics for the redundant eight-wheel topology and implements a distributed architecture in which ROS 2 handles high-level navigation and micro-ROS connects ESP32-based wheel interfaces. The platform integrates LiDAR, stereo vision, inertial, encoder, and ultrasonic sensing within a closed-loop navigation stack. Validation was conducted through Gazebo simulation and physical experiments using an out-and-back navigation protocol. In the physical platform, 91 of 100 missions were completed without safety interruptions, with pose-accuracy success rates of 96% for outbound legs and 81% for return legs under $e_p<1.5\mathrm{m}$ and $|e_{\theta }|<{15}^{\circ }$. Median errors at the intermediate waypoint were $0.64\mathrm{m}$, $0.191\mathrm{m}$, and ${17}^{\circ }$, while final-pose medians after return were $1.016\mathrm{m}$, $0.573\mathrm{m}$, and ${28.5}^{\circ }$. These results provide a quantitative baseline for campus-scale redundant Mecanum navigation and identify heading recovery as the main limitation. Full article

This study examines the micro-level mechanisms through which increasing market concentration and the entrenchment of competitive advantage arise. We develop a theoretical framework based on a constant elasticity of substitution (CES) production function and a CES demand structure, integrating—within a unified analytical chain—firms’ inter-firm cost competition, cost-plus pricing, demand allocation, and market-share decision-making. Methodologically, we first derive the relationship between firm prices and productivity from cost minimization and optimal pricing rules and then obtain explicit expressions for market shares and sales under CES demand. We subsequently introduce endogenous learning effects, in which firms’ market shares feed into future productivity growth via a learning curve mechanism, thereby characterizing the conversion of static advantage into dynamic competitive advantage. We further incorporate technology-factor substitutions, such as robotics, into firms’ production and cost structures to analyze how cost savings and efficiency gains accelerate the evolution of concentration and to show how learning disadvantages induced by insufficient market shares deter potential entry. The results indicate that firms’ market shares are primarily determined by their relative productivity, while profits respond asymmetrically to productivity differentials. Learning and technological substitution support the accumulation of advantages for incumbent firms and endogenously generate barriers to entry. Overall, this paper explains how the entrenchment of competitive advantage is reinforced through dynamic feedback, driving industries toward higher levels of concentration. Full article

Background: The growing demand for versatile laboratory automation is exemplified in the context of liquid biopsy, where multi-analyte approaches are increasingly recognised for their potential to enhance diagnostic sensitivity in oncology. However, current practice often necessitates the use of dedicated instruments and workflows for the extraction of each analyte, posing financial and logistical barriers for automated multi-analyte liquid biopsy. Methods: Here, we present Robotic Centrifugal Microfluidics (RoCM), an all-in-one platform that combines the versatility of centrifugal microfluidics and operational flexibility of robotic liquid handling. This combination enables the automation of complex micro- and macrofluidic protocols, realised through the use of (1). exchangeable microfluidic cartridges and (2). programmable robotic operations such as in-rotation liquid supply, magnetic bead manipulation, or microfluidic valving. In-rotation robotic liquid manipulation maintains fluid control under centrifugal forces and reduces the cartridge footprint associated with pre-loaded liquid reservoirs. Platform applicability was demonstrated using two exemplary liquid biopsy workflows: the extraction of cell-free DNA (cfDNA) from blood plasma using RoCM-cfDNA slices and the extraction of extracellular vesicles (EVs) from blood plasma using RoCM-EV slices. Results: In a pilot study with patient samples from different cancer entities, the RoCM-cfDNA slices yielded comparable variant allele frequencies to a commercial bead-based instrument, while the RoCM-EV slices achieved a recovery of a greater diversity of EV subpopulations than semi-automated size-exclusion chromatography. Conclusions: By simply exchanging cartridges, RoCM enables the extraction of diverse analytes within a single automated system. Its application can be extended to further analytes, such as circulating tumour cells (CTCs), or to applications beyond liquid biopsies, where versatile micro- and macrofluidic protocols benefit from implementation in a single automation instrument. Full article

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire response, their deployment inside buildings remains limited due to constraints in system mass, cost, and operational complexity. This paper presents the design and preliminary validation of an attritable micro-UAV as a proof-of-concept platform for indoor search support and post-fire inspection and assessment. The platform emphasizes portability, durability, and multi-sensor integration, enabling deployment by minimally trained personnel. System requirements were derived in collaboration with the Southfield Fire Department. The finalized design achieved a total mass of 247.34 g at a cost of $2969. Experimental evaluation demonstrated reliable sensing and communication performance at the subsystem level and confirmed structural robustness through drop tests from heights up to 3 m. Endurance testing yielded a maximum flight time of 28 min, slightly below the targeted 30 min requirement. While full task-level validation in operational firefighting scenarios has not been conducted, the proposed platform establishes a foundation for future development, including system-level validation, post-fire structural assessment, and enhanced visualization interfaces for improved situational awareness in emergency response operations. Full article

Micro/nanorobotics is emerging as a promising biomedical technology because of its precision, minimal invasiveness, multifunctionality, and potential to mitigate systemic adverse effects. At these ultraminiaturized scales, unique physical constraints necessitate design principles and actuation strategies distinct from those of conventional robotic systems, making material choice, structural design, propulsion mechanisms, and fabrication methods central to overall performance. In this review, we examine recent trends in micro/nanorobot development, with particular emphasis on the advantages of employing hydrogels and the current technical limitations associated with their use. Magnetic, chemical, acoustic, optical, and biohybrid propulsion strategies are comparatively analyzed, together with the material requirements and biological compatibility associated with each approach. Representative applications in drug delivery, tissue regeneration, and cancer therapy are further discussed to highlight the broad medical potential of these systems. Finally, remaining challenges related to material limitations, actuation efficiency, biocompatibility, and manufacturing scalability are identified, and future directions toward clinical translation and practical deployment are outlined. Overall, this review provides an integrated perspective on how hydrogel properties, actuation physics, fabrication strategies, and translational considerations collectively shape the development of more adaptive, biocompatible, and clinically relevant microrobotic systems. Full article

Recent advancements in Unmanned Aerial Vehicle (UAV) physics simulators, flight control firmware, and network virtualization have been substantial. However, operating these systems independently fails to capture the complex dynamics of real-world multi-UAV networks, thereby compromising simulation reliability. To address this, we propose the Multi-UAV Network-in-the-Loop Simulation (MUNILS) platform, which seamlessly integrates the Gazebo physics engine, the PX4 flight controller, and the ns-3 network simulator via Robot Operating System 2 (ROS2) middleware. Specifically, MUNILS leverages Micro eXtremely Resource Constrained Environments–Data Distribution Service (XRCE-DDS) for high-speed data bridging and employs Linux network namespaces to enforce traffic isolation and routing exclusively through ns-3. Crucially, we introduce a precise cross-layer time synchronization mechanism spanning the physical, control, and network domains to resolve inherent clock discrepancies among these heterogeneous simulators. Experimental evaluations confirm that MUNILS achieves strict traffic isolation, scalable closed-loop flight control, and highly accurate time synchronization across all integrated modules (Gazebo, ns-3, ROS2, and PX4) without cumulative clock drift, thereby providing a highly reliable verification environment for large-scale swarm operations on a single machine. Full article

Modern scanning microscopes and robotic scanning systems increasingly use visual recognition and machine learning technologies to extract complex data from acquired images. This study examined sensor data fusion in optical imaging to detect and control the deviation of the position of the tool during various micro-manipulations for biologic and microscale engineering. The sensor data fusion study was performed using a scanning micro-robotic system with an integrated optical microscope and a vision sensor providing an image of the object’s bottom. The bottom vision sensor is a typical complementary metal–oxide–semiconductor sensor that is used to observe micrometer-sized semi-transparent objects. The challenge for sensor fusion in such a study is not only data fusion, but also the trajectory deviation inherent in directing the manipulator in the X and Y directions according to the selected trajectory. The data fusion method was applied to estimate deviations from the given trajectory of the scanning microscope. The unique novelty of this work is that an additional vision sensor is used to increase the accuracy of positioning determination of a scanning micro-robotic system, placed under the semi-transparent object, using the fusion of the obtained data, thus additionally controlling the objective deviations. By testing several known data fusion methods, a unique solution was achieved. The proposed sensor fusion method achieved a positioning accuracy of less than 0.5 μm at speeds up to 5 mm/s. Experimental results demonstrate that the system maintains high stability. This quantitative performance proves the system’s suitability for high-precision biological micro-manipulation, where mechanical drift was previously a limiting factor. Full article

The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. To address this issue, this paper proposes Area-to-Point Matching Visual–Inertial SLAM (A2PM-VINS), a visual–inertial SLAM method based on Area-to-Point matching. The method introduces Area-to-Point hierarchical matching and a kinematic temporal inheritance mechanism to improve matching reliability and track continuity, and further designs an Anchor–Explorer feature selection strategy to retain features with higher geometric value for back-end optimization. In addition, a Sub-Window Consistency (SWC) weighting strategy is incorporated into the back end to suppress geometrically deceptive observations with poor temporal continuity and geometric consistency. Experiments on the European Robotics Challenge Micro Aerial Vehicle (EuRoC MAV) dataset show that A2PM-VINS achieves superior or competitive localization accuracy on multiple challenging sequences. The absolute trajectory errors on MH_04 and MH_05 are 0.0983 m and 0.1191 m, respectively, and stable tracking is maintained on V2_02, where VINS-Fusion fails. These results show that the proposed method effectively improves the robustness of visual–inertial state estimation in complex degraded environments. Full article

Heterogeneous unmanned ground vehicle-unmanned aerial vehicle (UGV-UAV) collaborative systems offer clear advantages for field exploration. However, when tethered unmanned aerial vehicles (TUAVs) are introduced to extend mission capability, a major compatibility gap emerges for small and highly maneuverable UGVs: existing industrial tethered ground stations are generally too heavy and bulky to be carried by such platforms. In addition, on unstructured ground, residual station tilt can significantly complicate UAV launch and recovery. To address these issues, this paper develops an ultralight vehicle-mounted tethered ground station for micro unmanned aerial vehicles (micro-UAVs) that can be integrated directly with small UGVs. Through co-design of a 2-degree-of-freedom (2-DOF) self-leveling launch platform and a passive tether-assisted recovery scheme without visual fiducials, in which a customized UAV flight-control loop is coordinated with the state transitions of the ground tether-management system, the proposed system achieves practical tether-assisted recovery. Experiments show that the complete platform weighs only 4.1 kg and that the self-leveling mechanism compensates for ground inclinations over a total range of 24 degrees. Repeated passive-landing tests further demonstrate the feasibility of the proposed recovery scheme and its tolerance to moderate bay tilt and terminal off-axis activation. System-level flight validation confirms practical tether-assisted recovery without visual fiducials. In addition, we conduct a simplified exploratory simulation of tether-based ground-anchor localization under the proposed system architecture. Overall, these results establish a lightweight and low-cost hardware design and a practically viable recovery strategy for multimodal micro air-ground robotic systems. Full article

Dynamic knowledge representation in curved manifolds conventionally relies on integer-order Markovian sequence encoders, intrinsically yielding exponential memory decay. This paradigm fails to model the anomalous diffusion and heavy-tailed historical dependencies inherent in complex evolutionary networks and dense physical environments. This manuscript proposes the Fractional–Temporal Lorentz Graph Convolutional Network (FTL-GCN), formalizing temporal evolution as a continuous fractional geometric flow explicitly defined on the tangent bundle of the Lorentz manifold. Analytical derivations demonstrate that the discrete Grünwald–Letnikov memory kernel establishes a non-exponential, power-law lower bound for historical state retention, preventing topological manifold collapse over extended temporal horizons. Empirical evaluations demonstrate that FTL-GCN achieves competitive forecasting accuracy against the latest 2025–2026 state-of-the-art discrete models within specific temporal windows, while uniquely mitigating predictive degradation by up to 52% in long-horizon dependency stress tests and maintaining sub-millisecond latency for physical control. The architecture is subsequently deployed within an in silico biophysical simulation for autonomous micro–nano robotic navigation in the Tumor Microenvironment (TME). By establishing a physical-mathematical structural analogy—mapping the empirical fractional viscoelasticity of the extracellular matrix to the cognitive network’s fractional derivative order—FTL-GCN sustains continuous-space navigation policies in dense anomalous environments where standard integer-order models experience mechanical slip. Full article

This review comprehensively examines magnetic nanomaterials, ferrofluids, and their integration into ferrogel systems, systematically exploring their structural characteristics, dynamic behaviors, preparation techniques, and applications across medical and engineering fields. Structural characterization reveals that particle size and dispersibility directly influence functional efficiency in fluid and gel matrices, such as SAR (specific absorption rate) values in hyperthermia applications. For ferrofluids and magnetic gels, macroscopic behaviors and microscopic mechanisms are governed by key parameters like the magnetic Bond number. Preparation encompasses green synthesis, chemical reagent synthesis, and the cross-linking of these nanoparticles into hydrogel networks. Applications span diverse areas: in medicine, these include targeted hyperthermia, pH-responsive magnetic gel drug delivery, and MRI (magnetic resonance imaging); in engineering, applications range from efficient extraction and triboelectric power generation to magnetically regulated heat transfer and soft gel robotics. The paper also discusses current challenges, including material stability and unclear micro–macro correlations in complex fluid–gel systems, outlining future research directions for multifunctional magnetic materials. Full article