The proposed Very Large Area Space Telescope (VLAST), with its expected unprecedented sensitivity in the MeV-GeV range, can also address the longstanding "MeV Gap" in gamma-ray observations. We explore the capability of VLAST to detect sub-GeV leptophilic dark matter (DM) annihilation, focusing on scalar and vector mediators and emphasizing the resonance region where the mediator mass is approximately twice the DM mass. While $s$-wave annihilation is tightly constrained by relic density and cosmic microwave background observations, $p$-wave and mixed $(s+p)$-wave scenarios remain viable, particularly near resonance. Additionally, direct detection experiments, especially those probing DM-electron scattering, significantly constrain nonresonance parameter space but are less effective in the resonance regime. VLAST can uniquely probe this surviving region, outperforming existing and planned instruments, and establishing itself as a crucial tool for indirect detection of thermal relic DM.
In order to broaden the adoption of highly-demanded quantum functionalities such as QKD, there is a need for having quantum signals coexist with classical traffic over the same physical medium, typically optical fibers in already-deployed networks. Beyond the experimental point-to-point demonstrations of the past few years, efforts are now underway to integrate QKD at the network level: developing interfaces with the software-defined-network ecosystem; but also network planning tools that satisfy physical-layer contraints jointly on the classical and quantum signals. We have found that in certain situations, na\"ive network planning prioritizing quantum traffic drastically degrades classical capacity, whereas a quantum-aware wavelength assignment heuristic allows coexistence with minimal impact on both capacities. More such techniques will be required to enable widespread deployment of QKD and other future quantum functionalities.
Efficient view planning is a fundamental challenge in computer vision and robotic perception, critical for tasks ranging from search and rescue operations to autonomous navigation. While classical approaches, including sampling-based and deterministic methods, have shown promise in planning camera viewpoints for scene exploration, they often struggle with computational scalability and solution optimality in complex settings. This study introduces HQC-NBV, a hybrid quantum-classical framework for view planning that leverages quantum properties to efficiently explore the parameter space while maintaining robustness and scalability. We propose a specific Hamiltonian formulation with multi-component cost terms and a parameter-centric variational ansatz with bidirectional alternating entanglement patterns that capture the hierarchical dependencies between viewpoint parameters. Comprehensive experiments demonstrate that quantum-specific components provide measurable performance advantages. Compared to the classical methods, our approach achieves up to 49.2% higher exploration efficiency across diverse environments. Our analysis of entanglement architecture and coherence-preserving terms provides insights into the mechanisms of quantum advantage in robotic exploration tasks. This work represents a significant advancement in integrating quantum computing into robotic perception systems, offering a paradigm-shifting solution for various robot vision tasks.
This work presents an online velocity planner for autonomous racing that adapts to changing dynamic constraints, such as grip variations from tire temperature changes and rubber accumulation. The method combines a forward-backward solver for online velocity optimization with a novel spatial sampling strategy for local trajectory planning, utilizing a three-dimensional track representation. The computed velocity profile serves as a reference for the local planner, ensuring adaptability to environmental and vehicle dynamics. We demonstrate the approach's robust performance and computational efficiency in racing scenarios and discuss its limitations, including sensitivity to deviations from the predefined racing line and high jerk characteristics of the velocity profile.
High-resolution energy consumption and emissions datasets are essential for localized policy-making, resource optimization, and climate action planning. They enable municipalities to monitor mitigation strategies and foster engagement among governments, businesses, and communities. However, smaller municipalities often face data limitations that hinder tailored climate strategies. This study generates detailed final energy consumption and emissions data at the local administrative level for Germany and Spain. Using national datasets, we apply spatial disaggregation techniques with open data sources. A key innovation is the application of XGBoost for imputing missing data, combined with a stepwise spatial disaggregation process incorporating district- and province-level statistics. Prioritizing reproducibility, our open-data approach provides a scalable framework for municipalities to develop actionable climate plans. To ensure transparency, we assess the reliability of imputed values and assign confidence ratings to the disaggregated data.
The majority of electric vehicles (EVs) are charged domestically overnight, where the precise timing of power allocation is not important to the user, thus representing a source of flexibility that can be leveraged by charging control algorithms. In this paper, we relax the common assumption, that EVs require full charge every morning, enabling additional flexibility to defer charging of surplus energy to subsequent nights, which can enhance the performance of controlled charging. In particular, we consider a simple domestic smart plug, scheduling power delivery with the objective to minimize CO$_2$ emissions over prediction horizons of multiple sessions -- up to seven days ahead -- utilising model predictive control (MPC). Based on carbon intensity data from the UK National Grid, we demonstrate significant potential for emission reductions with multi-session planning of 40 to 46\% compared to uncontrolled charging and 19 to 26\% compared to single-session planning. Furthermore, we assess, how the driving and charging behaviour of EV users affects the available flexibility and consequentially the potential for emission reductions. Finally, using grid carbon intensity data from 14 different UK regions, we report significant variations in absolute emission reductions based on the local energy mix.
Coverage Path Planning (CPP) is vital in precision agriculture to improve efficiency and resource utilization. In irregular and dispersed plantations, traditional grid-based CPP often causes redundant coverage over non-vegetated areas, leading to waste and pollution. To overcome these limitations, we propose CPP-DIP, a multi-objective CPP framework designed for Micro Air Vehicles (MAVs). The framework transforms the CPP task into a Traveling Salesman Problem (TSP) and optimizes flight paths by minimizing travel distance, turning angles, and intersection counts. Unlike conventional approaches, our method does not rely on GPS-based environmental modeling. Instead, it uses aerial imagery and a Histogram of Oriented Gradients (HOG)-based approach to detect trees and extract image coordinates. A density-aware waypoint strategy is applied: Kernel Density Estimation (KDE) is used to reduce redundant waypoints in dense regions, while a greedy algorithm ensures complete coverage in sparse areas. To verify the generality of the framework, we solve the resulting TSP using three different methods: Greedy Heuristic Insertion (GHI), Ant Colony Optimization (ACO), and Monte Carlo Reinforcement Learning (MCRL). Then an object-based optimization is applied to further refine the resulting path. Additionally, CPP-DIP integrates ForaNav, our insect-inspired navigation method, for accurate tree localization and tracking. The experimental results show that MCRL offers a balanced solution, reducing the travel distance by 16.9 % compared to ACO while maintaining a similar performance to GHI. It also improves path smoothness by reducing turning angles by 28.3 % and 59.9 % relative to ACO and GHI, respectively, and effectively eliminates intersections. These results confirm the robustness and effectiveness of CPP-DIP in different TSP solvers.
We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ("edge cases") that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.
This paper proposes a novel Large Vision-Language Model (LVLM) and Model Predictive Control (MPC) integration framework that delivers both task scalability and safety for Autonomous Driving (AD). LVLMs excel at high-level task planning across diverse driving scenarios. However, since these foundation models are not specifically designed for driving and their reasoning is not consistent with the feasibility of low-level motion planning, concerns remain regarding safety and smooth task switching. This paper integrates LVLMs with MPC Builder, which automatically generates MPCs on demand, based on symbolic task commands generated by the LVLM, while ensuring optimality and safety. The generated MPCs can strongly assist the execution or rejection of LVLM-driven task switching by providing feedback on the feasibility of the given tasks and generating task-switching-aware MPCs. Our approach provides a safe, flexible, and adaptable control framework, bridging the gap between cutting-edge foundation models and reliable vehicle operation. We demonstrate the effectiveness of our approach through a simulation experiment, showing that our system can safely and effectively handle highway driving while maintaining the flexibility and adaptability of LVLMs.
Robotic dexterous in-hand manipulation, where multiple fingers dynamically make and break contact, represents a step toward human-like dexterity in real-world robotic applications. Unlike learning-based approaches that rely on large-scale training or extensive data collection for each specific task, model-based methods offer an efficient alternative. Their online computing nature allows for ready application to new tasks without extensive retraining. However, due to the complexity of physical contacts, existing model-based methods encounter challenges in efficient online planning and handling modeling errors, which limit their practical applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel integrated framework that mitigates these limitations. The integration involves two key aspects: 1) integrated real-time planning and tracking achieved by a hierarchical structure; and 2) joint optimization of motions and contacts achieved by integrated motion-contact modeling. Specifically, at the high level, finger motion and contact force references are jointly generated using contact-implicit model predictive control. The high-level module facilitates real-time planning and disturbance recovery. At the low level, these integrated references are concurrently tracked using a hand force-motion model and actual tactile feedback. The low-level module compensates for modeling errors and enhances the robustness of manipulation. Extensive experiments demonstrate that our approach outperforms existing model-based methods in terms of accuracy, robustness, and real-time performance. Our method successfully completes five challenging tasks in real-world environments, even under appreciable external disturbances.
The miniaturisation of sensors and processors, the advancements in connected edge intelligence, and the exponential interest in Artificial Intelligence are boosting the affirmation of autonomous nano-size drones in the Internet of Robotic Things ecosystem. However, achieving safe autonomous navigation and high-level tasks such as exploration and surveillance with these tiny platforms is extremely challenging due to their limited resources. This work focuses on enabling the safe and autonomous flight of a pocket-size, 30-gram platform called Crazyflie 2.1 in a partially known environment. We propose a novel AI-aided, vision-based reactive planning method for obstacle avoidance under the ambit of Integrated Sensing, Computing and Communication paradigm. We deal with the constraints of the nano-drone by splitting the navigation task into two parts: a deep learning-based object detector runs on the edge (external hardware) while the planning algorithm is executed onboard. The results show the ability to command the drone at $\sim8$ frames-per-second and a model performance reaching a COCO mean-average-precision of $60.8$. Field experiments demonstrate the feasibility of the solution with the drone flying at a top speed of $1$ m/s while steering away from an obstacle placed in an unknown position and reaching the target destination. The outcome highlights the compatibility of the communication delay and the model performance with the requirements of the real-time navigation task. We provide a feasible alternative to a fully onboard implementation that can be extended to autonomous exploration with nano-drones.
Coronary angiography (CAG) is the gold-standard imaging modality for evaluating coronary artery disease, but its interpretation and subsequent treatment planning rely heavily on expert cardiologists. To enable AI-based decision support, we introduce a two-stage, physician-curated pipeline and a bilingual (Japanese/English) CAG image-report dataset. First, we sample 14,686 frames from 539 exams and annotate them for key-frame detection and left/right laterality; a ConvNeXt-Base CNN trained on this data achieves 0.96 F1 on laterality classification, even on low-contrast frames. Second, we apply the CNN to 243 independent exams, extract 1,114 key frames, and pair each with its pre-procedure report and expert-validated diagnostic and treatment summary, yielding a parallel corpus. We then fine-tune three open-source VLMs (PaliGemma2, Gemma3, and ConceptCLIP-enhanced Gemma3) via LoRA and evaluate them using VLScore and cardiologist review. Although PaliGemma2 w/LoRA attains the highest VLScore, Gemma3 w/LoRA achieves the top clinician rating (mean 7.20/10); we designate this best-performing model as CAG-VLM. These results demonstrate that specialized, fine-tuned VLMs can effectively assist cardiologists in generating clinical reports and treatment recommendations from CAG images.
We study load balancing for a queueing system where parallel stations are distant from customers. In the presence of traveling delays, the join-the-shortest-queue (JSQ) policy induces queue length oscillations and prolongs the mean waiting time. A variant of the JSQ policy, dubbed the randomized join-the-shortest-queue (RJSQ) policy, is devised to mitigate the oscillation phenomenon. By the RJSQ policy, customers are sent to each station with a probability approximately proportional to its service capacity; only a small fraction of customers are purposely routed to the shortest queue. The additional probability of routing a customer to the shortest queue, referred to as the balancing fraction, dictates the policy's performance. When the balancing fraction is within a certain range, load imbalance between the stations is negligible in heavy traffic, so that complete resource pooling is achieved. We specify the optimal order of magnitude for the balancing fraction, by which heuristic formulas are proposed to fine-tune the RJSQ policy. A joint problem of capacity planning and load balancing is considered for geographically separated stations. With well planned service capacities, the RJSQ policy sends all but a small fraction of customers to the nearest stations, rendering the system asymptotically equivalent to an aggregated single-server system with all customers having minimum traveling delays. If each customer's service requirement does not depend on the station, the RJSQ policy is asymptotically optimal for reducing workload.
Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning. This study proposes a novel Building-Guided Pseudo-Label Learning Framework to address the challenges of mapping building damage from pre-disaster optical and post-disaster SAR images. First, we train a series of building extraction models using pre-disaster optical images and building labels. To enhance building segmentation, we employ multi-model fusion and test-time augmentation strategies to generate pseudo-probabilities, followed by a low-uncertainty pseudo-label training method for further refinement. Next, a change detection model is trained on bi-temporal cross-modal images and damaged building labels. To improve damage classification accuracy, we introduce a building-guided low-uncertainty pseudo-label refinement strategy, which leverages building priors from the previous step to guide pseudo-label generation for damaged buildings, reducing uncertainty and enhancing reliability. Experimental results on the 2025 IEEE GRSS Data Fusion Contest dataset demonstrate the effectiveness of our approach, which achieved the highest mIoU score (54.28%) and secured first place in the competition.
This paper proposes vehicle motion planning methods with obstacle avoidance in tight spaces by incorporating polygonal approximations of both the vehicle and obstacles into a model predictive control (MPC) framework. Representing these shapes is crucial for navigation in tight spaces to ensure accurate collision detection. However, incorporating polygonal approximations leads to disjunctive OR constraints in the MPC formulation, which require a mixed integer programming and cause significant computational cost. To overcome this, we propose two different collision-avoidance constraints that reformulate the disjunctive OR constraints as tractable conjunctive AND constraints: (1) a Support Vector Machine (SVM)-based formulation that recasts collision avoidance as a SVM optimization problem, and (2) a Minimum Signed Distance to Edges (MSDE) formulation that leverages minimum signed-distance metrics. We validate both methods through extensive simulations, including tight-space parking scenarios and varied-shape obstacle courses, as well as hardware experiments on an RC-car platform. Our results demonstrate that the SVM-based approach achieves superior navigation accuracy in constrained environments; the MSDE approach, by contrast, runs in real time with only a modest reduction in collision-avoidance performance.
In concrete troweling for building construction, robots can significantly reduce workload and improve automation level. However, as a primary task of coverage path planning (CPP) for troweling, delimitating area of interest (AOI) in complex scenes is still challenging, especially for swing-arm robots with more complex working modes. Thus, this research proposes an algorithm to delimitate AOI for swing-arm troweling robot (SatAOI algorithm). By analyzing characteristics of the robot and obstacle maps, mathematical models and collision principles are established. On this basis, SatAOI algorithm achieves AOI delimitation by global search and collision detection. Experiments on different obstacle maps indicate that AOI can be effectively delimitated in scenes under different complexity, and the algorithm can fully consider the connectivity of obstacle maps. This research serves as a foundation for CPP algorithm and full process simulation of swing-arm troweling robots.
Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models, systematically organized across five thematic pillars that structure the landscape of this rapidly evolving field. We begin by establishing the conceptual foundations of VLA systems, tracing their evolution from cross-modal learning architectures to generalist agents that tightly integrate vision-language models (VLMs), action planners, and hierarchical controllers. Our methodology adopts a rigorous literature review framework, covering over 80 VLA models published in the past three years. Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations. We explore diverse application domains such as humanoid robotics, autonomous vehicles, medical and industrial robotics, precision agriculture, and augmented reality navigation. The review further addresses major challenges across real-time control, multimodal action representation, system scalability, generalization to unseen tasks, and ethical deployment risks. Drawing from the state-of-the-art, we propose targeted solutions including agentic AI adaptation, cross-embodiment generalization, and unified neuro-symbolic planning. In our forward-looking discussion, we outline a future roadmap where VLA models, VLMs, and agentic AI converge to power socially aligned, adaptive, and general-purpose embodied agents. This work serves as a foundational reference for advancing intelligent, real-world robotics and artificial general intelligence. >Vision-language-action, Agentic AI, AI Agents, Vision-language Models
We report on the status of charged lepton flavor violating (CLFV) experiments with muons. We focus on the three "golden channels": $\mu^{+} \rightarrow e^{+} \gamma$, $\mu^{+} \rightarrow e^{+} e^{-} e^{+}$ and $\mu^{-} N \rightarrow e^{-} N$. The collection of upcoming experiments aim for sensitivity improvements up to $10^{4}$ with respect to previous searches. The MEG II experiment, searching for $\mu^{+} \rightarrow e^{+} \gamma$, is currently in its 4th year of physics data-taking with a published result from its first year of data. The Mu3e experiment is an upcoming experiment searching for $\mu^{+} \rightarrow e^{+} e^{-} e^{+}$ with plans of physics data-taking as soon as 2025. The Mu2e and COMET experiments are upcoming searches for $\mu^{-} N \rightarrow e^{-} N$ with the goal of physics data-taking starting in 2027 and 2026 respectively. This proceeding summarizes the signal signature, expected background, resolutions, and timelines for the mentioned searches.
This paper presents a compliant manipulation system capable of placing items onto densely packed shelves. The wide diversity of items and strict business requirements for high producing rates and low defect generation have prohibited warehouse robotics from performing this task. Our innovations in hardware, perception, decision-making, motion planning, and control have enabled this system to perform over 500,000 stows in a large e-commerce fulfillment center. The system achieves human levels of packing density and speed while prioritizing work on overhead shelves to enhance the safety of humans working alongside the robots.
Model-based planning and execution systems offer a principled approach to building flexible autonomous robots that can perform diverse tasks by automatically combining a host of basic skills. This idea is almost as old as modern robotics. Yet, while diverse general-purpose reasoning architectures have been proposed since, general-purpose systems that are integrated with modern robotic platforms have emerged only recently, starting with the influential ROSPlan system. Since then, a growing number of model-based systems for robot task-level control have emerged. In this paper, we consider the diverse design choices and issues existing systems attempt to address, the different solutions proposed so far, and suggest avenues for future development.
The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation.
A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed. The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases. The method operates in low visibility conditions or in dense traffic where the anomaly is occluded by a preceding vehicle. Anomalies are detected predictively, i.e., before a vehicle encounters them, which allows to pre-configure low-level vehicle systems (such as chassis) or to plan an avoidance maneuver in case of autonomous driving. A challenge is that the signal coming from camera-based tracking of a preceding vehicle may be weak and disturbed by camera ego motion due to vibrations affecting the ego vehicle. Therefore, we propose an efficient method to compensate camera pitch rotation by an iterative robust estimator. Our experiments on both controlled setup and normal traffic conditions show that road anomalies can be detected reliably at a distance even in challenging cases where the ego vehicle traverses imperfect road surfaces. The method is effective and performs in real time on standard consumer hardware.
Machine Ethics decisions should consider the implications of uncertainty over decisions. Decisions should be made over sequences of actions to reach preferable outcomes long term. The evaluation of outcomes, however, may invoke one or more moral theories, which might have conflicting judgements. Each theory will require differing representations of the ethical situation. For example, Utilitarianism measures numerical values, Deontology analyses duties, and Virtue Ethics emphasises moral character. While balancing potentially conflicting moral considerations, decisions may need to be made, for example, to achieve morally neutral goals with minimal costs. In this paper, we formalise the problem as a Multi-Moral Markov Decision Process and a Multi-Moral Stochastic Shortest Path Problem. We develop a heuristic algorithm based on Multi-Objective AO*, utilising Sven-Ove Hansson's Hypothetical Retrospection procedure for ethical reasoning under uncertainty. Our approach is validated by a case study from Machine Ethics literature: the problem of whether to steal insulin for someone who needs it.
This work proposes new estimators for discrete optimal transport plans that enjoy Gaussian limits centered at the true solution. This behavior stands in stark contrast with the performance of existing estimators, including those based on entropic regularization, which are asymptotically biased and only satisfy a CLT centered at a regularized version of the population-level plan. We develop a new regularization approach based on a different class of penalty functions, which can be viewed as the duals of those previously considered in the literature. The key feature of these penalty schemes it that they give rise to preliminary estimates that are asymptotically linear in the penalization strength. Our final estimator is obtained by constructing an appropriate linear combination of two penalized solutions corresponding to two different tuning parameters so that the bias introduced by the penalization cancels out. Unlike classical debiasing procedures, therefore, our proposal entirely avoids the delicate problem of estimating and then subtracting the estimated bias term. Our proofs, which apply beyond the case of optimal transport, are based on a novel asymptotic analysis of penalization schemes for linear programs. As a corollary of our results, we obtain the consistency of the naive bootstrap for fully data-driven inference on the true optimal solution. Simulation results and two data analyses support strongly the benefits of our approach relative to existing techniques.
Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from a product's 3D model. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps enabling scalable, adaptive, and precise process planning across varied product types.
In 2023, CEPC accelerator Technical Design Report (TDR) has been formally released, and CEPC project entered Engineering Design Report (EDR) phase before construction. In the paper the accelerator EDR and CEPC detector ref-Design report progresses have been reported, where booster magnet and vacuum chamber NEG coating automatic fabrication lines, 650 MHz full size cryomodule, C-band 80 MW klystron. C-band test band and CCT SC quadrupole studies, etc. have been reported. The CEPC EDR goal, timeline and EDR site geological feasibility study status have been given. The industrial and international collaboration preparation have been addressed. The EDR goal is to complete a CEPC proposal application to Chinese government in 2025 for the construction during the 15th five-year plan (2026-2030) around 2027 and complete the construction by 2035.
The increasing frequency and severity of natural disasters underscore the critical importance of effective disaster emergency response planning to minimize human and economic losses. This survey provides a comprehensive review of recent advancements (2019--2024) in five essential areas of disaster emergency response planning: evacuation, facility location, casualty transport, search and rescue, and relief distribution. Research in these areas is systematically categorized based on methodologies, including optimization models, machine learning, and simulation, with a focus on their individual strengths and synergies. A notable contribution of this work is its examination of the interplay between machine learning, simulation, and optimization frameworks, highlighting how these approaches can address the dynamic, uncertain, and complex nature of disaster scenarios. By identifying key research trends and challenges, this study offers valuable insights to improve the effectiveness and resilience of emergency response strategies in future disaster planning efforts.
Cloud security concerns have been greatly realized in recent years due to the increase of complicated threats in the computing world. Many traditional solutions do not work well in real-time to detect or prevent more complex threats. Artificial intelligence is today regarded as a revolution in determining a protection plan for cloud data architecture through machine learning, statistical visualization of computing infrastructure, and detection of security breaches followed by counteraction. These AI-enabled systems make work easier as more network activities are scrutinized, and any anomalous behavior that might be a precursor to a more serious breach is prevented. This paper examines ways AI can enhance cloud security by applying predictive analytics, behavior-based security threat detection, and AI-stirring encryption. It also outlines the problems of the previous security models and how AI overcomes them. For a similar reason, issues like data privacy, biases in the AI model, and regulatory compliance are also covered. So, AI improves the protection of cloud computing contexts; however, more efforts are needed in the subsequent phases to extend the technology's reliability, modularity, and ethical aspects. This means that AI can be blended with other new computing technologies, including blockchain, to improve security frameworks further. The paper discusses the current trends in securing cloud data architecture using AI and presents further research and application directions.
Quadcopters are versatile aerial robots gaining popularity in numerous critical applications. However, their operational effectiveness is constrained by limited battery life and restricted flight range. To address these challenges, autonomous drone landing on stationary or mobile charging and battery-swapping stations has become an essential capability. In this study, we present NMPC-Lander, a novel control architecture that integrates Nonlinear Model Predictive Control (NMPC) with Control Barrier Functions (CBF) to achieve precise and safe autonomous landing on both static and dynamic platforms. Our approach employs NMPC for accurate trajectory tracking and landing, while simultaneously incorporating CBF to ensure collision avoidance with static obstacles. Experimental evaluations on the real hardware demonstrate high precision in landing scenarios, with an average final position error of 9.0 cm and 11 cm for stationary and mobile platforms, respectively. Notably, NMPC-Lander outperforms the B-spline combined with the A* planning method by nearly threefold in terms of position tracking, underscoring its superior robustness and practical effectiveness.
Robot motion can have many goals. Depending on the task, we might optimize for pose error, speed, collision, or similarity to a human demonstration. Motivated by this, we present PyRoki: a modular, extensible, and cross-platform toolkit for solving kinematic optimization problems. PyRoki couples an interface for specifying kinematic variables and costs with an efficient nonlinear least squares optimizer. Unlike existing tools, it is also cross-platform: optimization runs natively on CPU, GPU, and TPU. In this paper, we present (i) the design and implementation of PyRoki, (ii) motion retargeting and planning case studies that highlight the advantages of PyRoki's modularity, and (iii) optimization benchmarking, where PyRoki can be 1.4-1.7x faster and converges to lower errors than cuRobo, an existing GPU-accelerated inverse kinematics library.