Chengqiao Zhao, Zhicheng Deng, Zilong Zhang
et al.
Autonomous exploration in unknown environments remains a challenging problem for UAVs. This paper proposes a hierarchical exploration planning framework that explicitly leverages real-time acquired prior knowledge to improve exploration efficiency. To efficiently represent the structural information embedded in the prior knowledge, two map structures, namely the quasi-prior map and the hybrid-topo map, are designed, enabling more reasonable space partition and facilitating exploration planning. Subsequently, based on the hybrid-topo map, the hierarchical exploration planner computes a global exploration guidance that provides an efficient traversal order over all unexplored regions. The local coverage problem in unknown regions is formulated as a coverage traveling salesman problem (CTSP), where visibility information derived from the hybrid-topo map is exploited to optimize local viewpoint sequences with high coverage efficiency. Finally, a long-horizon trajectory planning strategy is proposed to maintain high flight speed while ensuring safety and dynamic feasibility. Simulations demonstrate that the proposed framework significantly outperforms state-of-the-art exploration methods in terms of exploration efficiency, while ablation studies further validate the effectiveness of each module. Real-world experiments are conducted to confirm the practical capability of the proposed approach.
Vehicle computing represents a fundamental shift in how autonomous vehicles are designed and deployed, transforming them from isolated transportation systems into mobile computing platforms that support both safety-critical, real-time driving and data-centric services. In this setting, vehicles simultaneously support real-time driving pipelines and a growing set of data-driven applications, placing increased responsibility on the vehicle operating system to coordinate computation, data movement, storage, and access. These demands highlight recurring system considerations related to predictable execution, data and execution protection, efficient handling of high-rate sensor data, and long-term system evolvability, commonly summarized as Safety, Security, Efficiency, and Extensibility (SSEE). Existing vehicle operating systems and runtimes address these concerns in isolation, resulting in fragmented software stacks that limit coordination between autonomy workloads and vehicle data services. This paper presents DAVOS, the Dependable Autonomous Vehicle Operating System, a unified vehicle operating system architecture designed for the vehicle computing context. DAVOS provides a cohesive operating system foundation that supports both real-time autonomy and extensible vehicle computing within a single system framework.
The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions.
Dimitris Perikleous, Katerina Margariti, Pantelis Velanas
et al.
This review explores the evolution and current state of aerial drones’ use in geophysical mining applications. Aerial drones have transformed many fields by offering high-resolution and cost-effective data acquisition. In geophysics, drones equipped with advanced sensors such as magnetometers, ground-penetrating radar, electromagnetic induction, and gamma-ray spectrometry have enabled more precise and rapid subsurface investigations, reducing operational costs and improving safety in mining exploration and monitoring. Over the last decade, advances in drone navigation, sensor integration, and data processing have improved the accuracy and applicability of geophysical surveys in mining. This review provides a historical overview and examines the latest developments in aerial drones, sensing technologies, data acquisition strategies, and processing methodologies. It analyses 59 studies spanning 66 drone-based geophysical applications and 63 geophysical method entries, published between 2005 and 2025. Multirotor drones are the most common, used in 72.73% of cases, followed by fixed-wing drones (12.12%), unmanned helicopters (9.09%), hybrid VTOL designs (3.03%), airships (1.52%), and one unspecified platform (1.52%). In terms of geophysical methods, magnetometry was the most frequently used technique, applied in thirty-nine studies, followed by gamma-ray spectrometry (eighteen studies), electromagnetic surveys (five studies), and ground-penetrating radar (one study). The findings show how drone-based geophysical techniques enhance resource exploration, safety, and sustainability in mining.
This paper presents an attitude stabilization algorithm for a Low Earth Orbit (LEO) Earth-pointing spacecraft using magnetorquers as the only torque actuators and employing Model-Free Adaptive Control (MFAC) as the control algorithm. MFAC is a data-driven control algorithm that relies solely on input–output data from the plant. This paper validates the effectiveness of the proposed approach through numerical simulations in a specific case study. The simulations show that the proposed algorithm drives the spacecraft’s attitude to three-axis stabilization in the orbital frame from arbitrary initial tumbling conditions. The numerical study also shows that the proposed control algorithm outperforms a model-based Proportional–Derivative (PD) control in terms of pointing accuracy at the expense of higher energy consumption.
Unmanned aerial vehicles (UAVs) may encounter actuator faults in diverse flight scenarios, requiring robust fault detection models that can adapt to varying data distributions. To address this challenge, this paper proposes an approach that integrates Domain-Adversarial Neural Networks (DANNs) with a Mixture of Experts (MoE) framework. By employing domain-adversarial learning, the method extracts domain-invariant features, mitigating distribution discrepancies between source and target domains. The MoE architecture dynamically selects specialized expert models based on task-specific data characteristics, improving adaptability to multimodal environments. This integration enhances fault detection accuracy and robustness while maintaining efficiency under constrained computational resources. To validate the proposed model, we conducted flight experiments, demonstrating its superior performance in actuator fault detection compared to conventional deep learning methods. The results highlight the potential of MoE-enhanced domain adaptation for real-time UAV fault detection in dynamic and uncertain environments.
Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras and deep-learning-based object detection algorithms are increasingly used in search operations. This study investigates the optimal flight parameters, specifically flight speed and ground sampling distance (GSD), to maximize a search efficiency metric called effective coverage. A custom dataset of 4468 aerial images with 35,410 annotated cardboard targets was collected and used to evaluate the influence of flight conditions on detection accuracy. The effects of flight speed and GSD were analyzed using regression modeling, revealing a trade-off between the area coverage and detection confidence of trained YOLOv8 and YOLOv11 models. Area coverage was modeled based on flight speed and camera specifications, enabling an estimation of the effective coverage. The results provide insights into how the detection performance varies across different operating conditions and demonstrate that a balance point exists where the combination of the detection reliability and coverage efficiency is optimized. Our table of the optimal flight regimes and metrics for the most commonly used cameras in UAV operations offers practical guidelines for efficient and reliable mission planning.
Christian Danner Ramos de Carvalho, João Viana da Fonseca Neto
This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorithms for these rockets are presented. Initially, the methodology is evaluated for a robust controller based on a reference model with ANN-RBF for altitude control. The main objective of the control is to suppress the effect of non-linear uncertainties inherent in the process. The method involves mathematical and computational modelling, together with the design of adaptive controllers for stability and performance analysis. The controllers considered include model reference adaptive control (MRAC) techniques and a model reference neuro-adaptive control (MRNAC) approach. The analysis, carried out using computer simulations, evaluates the behavior of each controller in relation to system stability and performance. The final objective is to select the most suitable controller for the suborbital rocket, taking into account the system constraints, robust performance requirements, robust stability, and optimal adaptability. This research promotes the development of adaptive controllers for suborbital rockets, with possible applications in scientific research and commercial launches.
Technology, Motor vehicles. Aeronautics. Astronautics
Quasi-direct-drive (QDD) actuation is transforming legged and manipulator robots by eliminating high-ratio gearboxes, yet it demands motors that deliver very high torque at low speed within a thin, disc-shaped joint envelope. Axial-flux permanent-magnet (AFPM) machines meet these geometric and torque requirements, but scaling them below a 20mm outer diameter is hampered by poor copper fill in conventional wound stators, inflating resistance and throttling continuous torque. This paper introduces a micro-scale AFPM motor that overcomes these limitations through printed-circuit-board (PCB) windings fabricated with advanced IC-substrate high-density interconnect (HDI) technology. The resulting 48-layer stator-formed by stacking four 12-layer HDI modules-achieves a record 45\% copper fill in a package only 5mm thick and 19mm in diameter. We perform comprehensive electromagnetic and thermal analyses to inform the motor design, then fabricate a prototype whose performance characteristics are experimentally verified.
The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry. However, as these vehicles become more connected, cybersecurity threats have emerged as a serious concern. Protecting the security and integrity of autonomous systems is essential to prevent malicious activities that can harm passengers, other road users, and the overall transportation network. This paper focuses on addressing the cybersecurity issues in autonomous vehicles by examining the challenges and risks involved, which are important for building a secure future. Since autonomous vehicles depend on the communication between sensors, artificial intelligence, external infrastructure, and other systems, they are exposed to different types of cyber threats. A cybersecurity breach in an autonomous vehicle can cause serious problems, including a loss of public trust and safety. Therefore, it is very important to develop and apply strong cybersecurity measures to support the growth and acceptance of self-driving cars. This paper discusses major cybersecurity challenges like vulnerabilities in software and hardware, risks from wireless communication, and threats through external interfaces. It also reviews existing solutions such as secure software development, intrusion detection systems, cryptographic protocols, and anomaly detection methods. Additionally, the paper highlights the role of regulatory bodies, industry collaborations, and cybersecurity standards in creating a secure environment for autonomous vehicles. Setting clear rules and best practices is necessary for consistent protection across manufacturers and regions. By analyzing the current cybersecurity landscape and suggesting practical countermeasures, this paper aims to contribute to the safe development and public trust of autonomous vehicle technology.
Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.
This review paper facilitates the examination of the comprehensive thought patterns within electric vehicles (EVs) technologies and elucidates the primary significance derived from re-cent research. Furthermore, it systematically identifies and explores key themes related to EVs through the incorporation of the keyword "electric vehicle" in the bibliometric analysis. The selection of the Scopus database for this research is grounded in its superior importance com-pared to other databases, emphasizing its utilization in the bibliometric analysis. The VOSviewer software served as the analytical tool employed to visually represent crucial data, including information about countries, authors, journals, and keywords. The analysis, conduct-ed on November 19, 2022, encompassed a thorough examination of 1074 documents spanning from 1985 to 2023. While the analysis of the number of publications over the years revealed in 2020 were 190 publications, marking the highest point for research and work on electric vehi-cle studies. The most of the articles were Conference paper among all 1074 documents with 61.7 % while review papers were identified as lowest document type with only 1.3 % of all of selected documents. Bagheri, M is the top writer with 25 documents on the Scopus database re-garding to the key words, while others have publications around 11 and 16 number of papers. Russian Federation is the top contributor to the research of EVs with 61 % of all documents while Egypt is contributed with 1 % among all selected areas on the Scopus database. Notably, the IOP Conference Series Materials Science and Engineering was hold as one of the primary sources, accounting 76 documents to the electric vehicle studies. The outcomes of this investi-gation reveal noteworthy advancements in the volume of publications and the growing interest in electric vehicles, particularly within the academic and manufacturing sectors.
Jarrad Rinaldo, Levin Kuhlmann, Jason Friedman
et al.
Neural Network movement controllers promise a variety of advantages over conventional control methods, however, they are not widely adopted due to their inability to produce reliably precise movements. This research explores a bilateral neural network architecture as a control system for motor tasks. We aimed to achieve hemispheric specialisation similar to what is observed in humans across different tasks; the dominant system (usually the right hand, left hemisphere) excels at tasks involving coordination and efficiency of movement, and the non-dominant system performs better at tasks requiring positional stability. Specialisation was achieved by training the hemispheres with different loss functions tailored to the expected behaviour of the respective hemispheres. We compared bilateral models with and without specialised hemispheres, with and without inter-hemispheric connectivity (representing the biological Corpus Callosum), and unilateral models with and without specialisation. The models were trained and tested on two tasks common in the human motor control literature: the random reach task, suited to the dominant system, a model with better coordination, and the hold position task, suited to the non-dominant system, a model with more stable movement. Each system outperformed the non-preferred system in its preferred task. For both tasks, a bilateral model outperformed the non-preferred hand and was as good or better than the preferred hand. The results suggest that the hemispheres could collaborate on tasks or work independently to their strengths. This study provides ideas for how a biologically inspired bilateral architecture could be exploited for industrial motor control.
This paper presents an efficient deep learning solution for decoding motor movements from neural recordings in non-human primates. An Autoencoder Gated Recurrent Unit (AEGRU) model was adopted as the model architecture for this task. The autoencoder is only used during the training stage to achieve better generalization. Together with the preprocessing techniques, our model achieved 0.71 $R^2$ score, surpassing the baseline models in Neurobench and is ranked first for $R^2$ in the IEEE BioCAS 2024 Grand Challenge on Neural Decoding. Model pruning is also applied leading to a reduction of 41.4% of the multiply-accumulate (MAC) operations with little change in the $R^2$ score compared to the unpruned model.
Inspired by recent experiments on fluctuations of the flagellar beating in sperms and C. reinhardtii, we investigate the precision of phase fluctuations in a system of nearest-neighbour-coupled molecular motors. We model the system as a Kuramoto chain of oscillators with coupling constant $k$ and noisy driving. The precision $p$ is a Fano-factor-like observable which obeys the Thermodynamic Uncertainty Relation (TUR), that is an upper bound related to dissipation. We first consider independent motor noises with diffusivity $D$: in this case the precision goes as $k/D$, coherently with the behavior of spatial order. The minimum observed precision is that of the uncoupled oscillator $p_{unc}$, the maximum observed one is $Np_{unc}$, saturating the TUR bound. Then we consider driving noises which are spatially correlated, as it may happen in the presence of some direct coupling between adjacent motors. Such a spatial correlation in the noise does not reduce evidently the degree of spatial correlation in the chain, but sensibly reduces the maximum attainable precision $p$, coherently with experimental observations. The limiting behaviors of the precision, in the two opposite cases of negligible interaction and strong interaction, are well reproduced by the precision of the single chain site $p_{unc}$ and the precision of the center of mass of the chain $N_{eff} p_{unc}$ with $N_{eff}<N$: both do not depend on the degree of interaction in the chain, but $N_{eff}$ decreases with the correlation length of the motor noises.
In the process of robust controller design for aircraft engines, it is difficult to divide the flight envelope re-gion systematically. In this paper, the flight envelope division methods for aircraft engines based on thrust fuel con-sumption rate characteristics and dynamic pressure fuel consumption rate characteristics is proposed. According to the thrust, fuel consumption and dynamic pressure characteristics of a turbofan engine during steady-state operation within the full envelope, combined with the objective law of atmospheric conditions, the flight envelope is divided into 65 regions by two division methods, and the parameters of the surrounding small deviation regions and bound-ary points are replaced by the parameters of the corresponding nominal points of each region. By comparing the pa-rameters of nominal points and boundary points within the full envelope of the engine, the results show that both methods are effective for the full flight envelope division, which provides a theoretical basis for the subsequent air-craft engine controller design.
This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.