Resource
2026 EN
Tiancheng Liu · Ling Bai · David Zou
+1 more
Roof gutters are highly susceptible to the accumulation of debris, such as fallen leaves, which can block drain outlets and pose a significant threat to the structural integrity of a building. Traditional high-altitude cleaning methods primarily rely on manual operations with ropes, ladders, and extended tools. These methods are not only inherently dangerous, but also inefficient, making them inadequate for building maintenance. The use of drones offers a promising solution for high-altitude cleaning. However, drones still face significant technical challenges in ensuring precise arrival at target points after detecting debris while simultaneously navigating multi-point obstacle avoidance during flight. In particular, autonomous roof gutter cleaning requires robust multi-stage trajectory planning in a cluttered environment where state estimation and waypoint accuracy can be degraded by the noises and inaccuracies from sensing and Global Navigation Satellite System (GNSS). This paper presents an Adaptive Newton Ego Planner (ANEP) framework for multi-waypoint trajectory optimization under constrained environments. The proposed ANEP integrates a novel Adaptive Newton-Raphson based optimizer (ANRBO) to enhance exploration and prevent premature convergence during online trajectory refinement. The optimizer leverages Newton-Raphson Search Rules (NRSR) and a Trap Avoidance Operator (TAO), while an online dynamic adaptation mechanism enables efficient multi-point exploration and real-time trajectory optimization. In this paper, we focus on trajectory planning and optimization. Perception, e.g., debris detection, and cleaning actuation were not evaluated in this simulation study. Experimental results demonstrate that the proposed trajectory planning method improves multi-stage flight missions by generating smoother trajectories, lower trajectory errors, and a higher mission completion rate. These results suggest that ANEP can serve as an effective trajectory planning component for UAV-based roof gutter maintenance scenarios.
Resource
2026 EN
Faezeh Kardan · Aditya Shekhar · Pavol Bauer
Failures associated with thermo-mechanical fatigue are one of the dominant reasons for faults in power electronic converter-based electrical systems. This review explores such thermal stress-induced reliability challenges in power converters, focusing on key package-related failure mechanisms such as bond-wire fatigue, solder degradation, and chip metallization wear-out. The study emphasizes the importance of mission-profile-based reliability assessment, highlighting the effects of operational and environmental conditions on the long-term performance of power modules. Key findings reveal how repetitive thermal cycling and environmental variations lead to critical failures, underscoring the need for effective thermal management and design-for-reliability strategies. The primary goal of this paper is the quantitative, comparative reliability analysis across multiple high-power applications, moving beyond qualitative summaries. This review aims to support future research on predictive reliability modeling, mission-profile-based lifetime estimation, and robust design strategies for wide-bandgap-based high-power converters. Ultimately, the insights provided are intended to guide the development of more robust power electronic systems for emerging energy and mobility infrastructures.
Resource
2026 EN
Mattia Sinigaglia · Angelo Garofalo · Elia Cereda
+10 more
Miniaturized autonomous robotics, such as sub-10 cm nano-sized Unmanned Aerial Vehicles (UAVs), is a novel and pervasive technology gaining attention in both civil and industrial application domains. To enable full autonomy, tiny robots must execute multiple real-time workloads, ranging from precise Artificial Intelligence (AI)-based perception algorithms to high-throughput control pipelines, while leveraging their minimal onboard computational resources. At the same time, well-trained AI-based algorithms suffer from the domain shift problem, which leads to poor performance when deployed in real-world domains. This work presents AlSaqr , a heterogeneous and secure RISC-V System-on-Chip (SoC) that integrates a dual-core Linux-capable 64-bit host processor, an 8-core 32-bit Programmable Parallel Accelerator (PPA) embedding an FP8/FP16 tensor core, and a secure subsystem. Fabricated in GlobalFoundries 22nm technology, the proposed Pixhawk FMUv6-compliant SoC operates from 210MHz at 0.52V to 900MHz at 0.8V within a power envelope from 90mW to 635mW occupying 9mm 2 silicon footprint, matching the tight size, weight, and power constraints of nano-UAVs. Post-silicon results show that the PPA achieves a peak FP16 throughput of 83GFLOp/s at 900MHz and a peak energy efficiency of 1.2 TFLOp/s/W at 210MHz. To demonstrate the SoC’s capabilities, we showcase a deep learning model for human pose estimation that produces low-level setpoints for the nano-UAV’s control loops. AlSaqr achieves a 40 frame/s inference, while concurrently fine-tuning the same model with on-device learning (i.e., backpropagation), enabling real-time on-the-fly adaptation without interruption of the primary mission.
Resource
2026 EN
Inam Ullah · Hesham El-Sayed · Alexis Dowhuszko
+2 more
The rise of data-intensive applications in Fifth-Generation (5G) mobile networks demands that next-generation mobile systems deliver seamless, high-bandwidth, and immersive services with improved quality of service. To address these challenges, the use of Integrated Access and Backhaul (IAB) nodes operating over millimeter-wave frequency bands onboard Unmanned Aerial Vehicle (UAV) presents a promising solution. The UAV-mounted IAB network has the potential to enhance line-of-sight conditions to the donor Base Station (BS) via the backhaul link, enabling temporary high data rates in mission-critical and emergency response communication scenarios that require a rapid deployment of new network elements for boosting cellular coverage. This paper proposed a framework that integrates terrestrial IAB nodes, non-terrestrial UAV-mounted IAB nodes, and terrestrial donor BS, operating in a dense urban Manhattan-like environment. The research work primarily focuses on how variations in UAV-mounted IAB altitudes, donor BS down-tilt angle, and IAB antenna configuration influence the downlink end-to-end (E2E) spectral efficiency performance of mobile users. Simulation results demonstrate that significant performance gains can be achieved when non-terrestrial IAB nodes are deployed at suitable altitudes when equipped with appropriate antenna configurations. These improvements are further improved when the donor BS employs properly adjusted down-tilt angles, enabling the hybrid terrestrial-aerial IAB mobile network to operate more efficiently and deliver enhanced E2E performances.
Resource
2026 EN
HICHEM CHERIET · KHELLAT KIHEL BADRA · CHOURAQUI SAMIRA
Navigating unmanned aerial vehicles (UAVs) efficiently and safely in complex 3D environments, especially those with unknown maps, remains a significant challenge for general autonomous applications. This paper introduces TIG*, an improved version of the Tangent Intersection Guidance algorithm designed to address this challenge by generating shorter and smoother paths with minimal computational time in environments with irregular shapes. TIG* operates by identifying obstacles and selecting optimal avoidance tangents using an optimized heuristic cost function. The evaluations in static environments show that TIG* outperforms static 3D-TG, PRM*, RRT*, and Informed RRT* across key metrics, including path length, smoothness, algorithm success, and computational time. In many cases, TIG* is capable of generating paths in less than 0.05 seconds, making it highly suitable for efficient pre-mission planning in diverse mission scenarios. Furthermore, for online scenarios, the proposed O-TIG* algorithm offers real-time adaptability, smoothly modifying paths in response to unforeseen obstacles. Comparative studies show that O-TIG* requires fewer path modifications, faster re-planning, and superior smoothness compared to dynamic 3D-TG, RH-RRT* and APF, even in dense clutter, which is crucial for UAV survivability and mission continuity in dense settings. TIG* represents a powerful and practical solution by integrating the geometric solution with optimized path calculation for next-generation UAV navigation, particularly for demanding and critical missions.
Resource
2026 EN
Misha Urooj Khan · Ahmad Suleman · Yazeed Alkhrijah
+1 more
Autonomous underwater vehicles (AUVs) operating in harsh, communication-limited underwater environments require robust fault diagnosis systems to ensure mission success and safety. Existing model-based approaches suffer from hydrodynamic modeling inaccuracies, while data-driven methods typically employ monolithic architectures that lack interpretability and real-time deployment awareness. This paper proposes M ulti- E xpert L ightweight F usion Model with Ethical & E xplainable F ault D iagnosis (MELF-XFD), a novel model-free framework that addresses these limitations through physically-grounded multi-domain signal decomposition. MELF-XFD performs explainable fault diagnosis by fusing temporal, spectral, and statistical representations of multivariate AUV time-series data using lightweight expert networks with adaptive weighting for real-time onboard deployment. Experimental results on a public AUV benchmark with five fault classes show that MELF-XFD outperforms existing methods, achieving a 95.7% macro-F1 score, 90.0% severe fault recall, and 39 ms inference latency. It attains the highest composite diagnostic criterion around 93.6%, balancing accuracy, safety-critical sensitivity, and computational efficiency. A low expected calibration error of 0.0880 ensures reliable confidence estimates for safety-critical deployment. Ablation studies confirm the critical role of temporal and frequency experts, while adaptive fusion enables interpretable fault attribution, establishing MELF-XFD as a practical and deployment-ready solution.
Resource
2026 EN
Huihui Yu · Yang Chen · Yanhua Yang
+1 more
Urban IoT devices face increasing challenges due to urbanization and complex traffic conditions. Traditional monitoring methods are inadequate for tracking urban traffic and ensuring public safety. Collaborative monitoring models involving multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are emerging. However, sudden emergencies such as traffic accidents and geological disasters can disrupt monitoring tasks across urban traffic networks, leading to traffic paralysis and personal injury. This study develops a mathematical model for multi-UAV-UGV path planning, specifically tailored for dual mission applications, namely continuous monitoring and emergency response. A foresight-based task allocation and path optimization algorithm is proposed, which accounts for future situation prediction. This algorithm takes into account various constraints, such as the road network restriction and information timeliness, with the aim of optimizing system efficiency and response speed. Various factors are comprehensively considered to select response vehicles, and the potential loss caused by emergency situations of the entire road network is reduced in the case of rapid emergency treatment. Simulation results demonstrate that this method is capable of providing an efficient and reliable path for dual mission operation. Additionally, the algorithm exhibits good stability and strong adaptability to random emergency events.
Resource
2026 EN
Gregorio Marchesini · Siyuan Liu · Lars Lindemann
+1 more
We propose a variant of the Rapidly Exploring Random Tree Star (RRT star }$ ) algorithm for planning trajectories of linear systems under input constraints subject to spatio-temporal specifications expressed in a fragment of Signal Temporal Logic (STL). Compared to existing sampling-based approaches that rely on mixed-integer or non-smooth optimization, which suffer from poor scalability, we adopt a control-theoretic framework based on set forward invariance. First, STL specifications with polyhedral predicates are encoded as a time-varying set computed via linear programming, such that all trajectories evolving within the set satisfy the specification. Forward invariance of the set for linear systems under input constraints is guaranteed using non-smooth analysis. Second, a modified RRT star }$ algorithm is proposed to efficiently sample dynamically feasible and minimum cost trajectories within this time-varying set, in order to satisfy the given specifications. The effectiveness of the approach is demonstrated on an autonomous inspection mission of the International Space Station and a timed room-servicing task.
Resource
2026 EN
Nanxiang Wang · Zhongyuan Chen · Wanchun Chen
Guidance poses a significant issue in aerospace vehicle development. Simultaneous impacts and higher lethality necessitate constraints on impact time and angles, posing a substantial obstacle to effective guidance law design. Similarly, the flexibility and maneuverability of aerospace vehicles, which require a designable acceleration profile, are equally essential issues in guidance law design. Therefore, to overcome the limited selectable constraint ranges and the challenges of shaping acceleration profiles in existing guidance laws, without sacrificing their penetration and cooperation capabilities, a three-stage catenary-based guidance law is proposed that considers constrained impact and robustness to external disturbances. To begin with, the desired launch angle and initial velocity direction of the catenary are enforced during the first stage, which uses a circular arc. Subsequently, the impact time constraint is met in the second catenary-based stage, and the final stage employs an additional circular arc to enforce the desired impact angle. Using the proposed analytical geometric rules, trajectory parameters are determined explicitly. Meanwhile, a robust guidance law is developed, with all constraints met. The proposed guidance law requires no numerical calculations, nonlinear model linearization, or reliance on time-to-go error estimation, making it convenient to implement. Furthermore, the proposed guidance law extends the range of available time constraints from the minimum to infinity and the range of angle constraints from 0 degrees to 360 degrees, thereby addressing the issue of a wide range of spatiotemporal multi-constraints. The adaptability issue arising from an extensive range of impact time constraints and arbitrary angle constraints is addressed by a shapeable acceleration profile, enabling the maximum acceleration value or peak time to be adjusted to meet mission requirements. Various disturbed-engagement scenarios are simulated, and numerous analyses are conducted to evaluate the effectiveness and robustness of the proposed sliding mode control guidance law.
Resource
2026 EN
Nour El-Din Safwat · Ahmed Elmeligy · Al Anoud Almurshidi
+1 more
The emergence of Advanced Air Mobility (AAM) has prompted significant interest in developing sustainable and highly automated/autonomous flight platforms featuring Vertical Take-Off and Landing (VTOL) capabilities. However, challenges related to limited range and emissions associated with battery-powered electric motors necessitate innovative approaches. This paper proposes a hybrid-electric propulsion management system for VTOL drones based on a mechanically decoupled push–pull hybrid configuration that integrates a puller electric motor and a pusher internal combustion engine. The proposed system coordinates multiple energy sources, including conventional fuel, batteries, solar cells, and hydrogen fuel cells, within a mission-adaptive power allocation strategy designed to optimize energy utilization across different operational objectives. This strategy explicitly links propulsion decisions to mission-level objectives such as emissions reduction, endurance maximization, and flight time minimization. The paper details the hardware functional architecture and software algorithms essential for implementing the power and propulsion management system, ensuring seamless operation and efficient energy utilization. A simulation case study perfomed on a representative VTOL platfrom allows the comparative evaluation of different hybridization levels, demonstrating the practicality of the proposed solution for medium-range and time-sensitive AAM missions.