Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (536)

Search Parameters:
Keywords = moving obstacles

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
32 pages, 6588 KiB  
Article
Path Planning for Unmanned Aerial Vehicle: A-Star-Guided Potential Field Method
by Jaewan Choi and Younghoon Choi
Drones 2025, 9(8), 545; https://doi.org/10.3390/drones9080545 (registering DOI) - 1 Aug 2025
Abstract
The utilization of Unmanned Aerial Vehicles (UAVs) in missions such as reconnaissance and surveillance has grown rapidly, underscoring the need for efficient path planning algorithms that ensure both optimality and collision avoidance. The A-star algorithm is widely used for global path planning due [...] Read more.
The utilization of Unmanned Aerial Vehicles (UAVs) in missions such as reconnaissance and surveillance has grown rapidly, underscoring the need for efficient path planning algorithms that ensure both optimality and collision avoidance. The A-star algorithm is widely used for global path planning due to its ability to generate optimal routes; however, its high computational cost makes it unsuitable for real-time applications, particularly in unknown or dynamic environments. For local path planning, the Artificial Potential Field (APF) algorithm enables real-time navigation by attracting the UAV toward the target while repelling it from obstacles. Despite its efficiency, APF suffers from local minima and limited performance in dynamic settings. To address these challenges, this paper proposes the A-star-Guided Potential Field (AGPF) algorithm, which integrates the strengths of A-star and APF to achieve robust performance in both global and local path planning. The AGPF algorithm was validated through simulations conducted in the Robot Operating System (ROS) environment. Simulation results demonstrate that AGPF produces smoother and more optimal paths than A-star, while avoiding the local minima issues inherent in APF. Furthermore, AGPF effectively handles moving and previously unknown obstacles by generating real-time avoidance trajectories, demonstrating strong adaptability in dynamic and uncertain environments. Full article
Show Figures

Figure 1

20 pages, 9169 KiB  
Article
Dynamic Mission Planning Framework for Collaborative Underwater Operations Using Behavior Trees
by Seunghyuk Choi and Jongdae Jung
J. Mar. Sci. Eng. 2025, 13(8), 1458; https://doi.org/10.3390/jmse13081458 - 30 Jul 2025
Viewed by 33
Abstract
This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each [...] Read more.
This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each encapsulated in an independent sub-tree to enable modular error handling and seamless phase transitions. The AUV and mothership operate entirely underwater, with real-time docking to a moving platform. An extended Kalman filter (EKF) fuses data from inertial, pressure, and acoustic sensors for accurate navigation and state estimation. At the same time, obstacle avoidance leverages forward-looking sonar (FLS)-based potential field methods to react to unpredictable underwater hazards. The system is implemented on the robot operating system (ROS) and validated in the Stonefish physics engine simulator. Simulation results demonstrate reliable mission execution, successful dynamic docking under communication delays and sensor noise, and robust retrieval from injected faults, confirming the validity and stability of the proposed architecture. Full article
(This article belongs to the Special Issue Innovations in Underwater Robotic Software Systems)
Show Figures

Figure 1

24 pages, 5313 KiB  
Article
The Influence of Gravity Gradient on the Inertialess Stratified Flow and Vortex Structure over an Obstacle in a Narrow Channel
by Karanvir Singh Grewal, Roger E. Khayat and Kelly A. Ogden
Fluids 2025, 10(8), 195; https://doi.org/10.3390/fluids10080195 - 29 Jul 2025
Viewed by 154
Abstract
The current study examines the influence of a varying gravity field and its interaction with density stratification. This represents a novel area in baroclinic flow analysis. The classical vortex and internal wave structures in stratified flows are shown to be significantly modified when [...] Read more.
The current study examines the influence of a varying gravity field and its interaction with density stratification. This represents a novel area in baroclinic flow analysis. The classical vortex and internal wave structures in stratified flows are shown to be significantly modified when gravity varies with height. Vortices may shift, stretch, or weaken depending on the direction and strength of gravity variation, and internal waves develop asymmetries or damping that are not present under constant gravity. We examine the influence of gravity variation on the flow of both homogeneous and density-stratified fluids in a channel with topography consisting of a Gaussian obstacle lying at the bottom of the channel. The flow is without inertia, induced by the translation of the top plate. Both the density and gravity are assumed to vary linearly with height, with the minimum density at the moving top plate. The narrow-gap approach is used to generate the flow field in terms of the pressure gradient along the top plate, which, in turn, is obtained in terms of the bottom topography and the three parameters of the problem, namely, the Froude number and the density and gravity gradients. The resulting stream function is a fifth-order polynomial in the vertical coordinate. In the absence of stratification, the flow is smooth, affected rather slightly by the variable topography, with an essentially linear drop in the pressure induced by the contraction. For a weak stratified fluid, the streamlines become distorted in the form of standing gravity waves. For a stronger stratification, separation occurs, and a pair of vortices generally appears on the two sides of the obstacle, the size of which depends strongly on the flow parameters. The influence of gravity stratification is closely coupled to that of density. We examine conditions where the coupling impacts the pressure and the velocity fields, particularly the onset of gravity waves and vortex flow. Only a mild density gradient is needed for flow separation to occur. The influence of the amplitude and width of the obstacle is also investigated. Full article
(This article belongs to the Section Geophysical and Environmental Fluid Mechanics)
Show Figures

Figure 1

29 pages, 2926 KiB  
Review
Microbial Symbiosis in Lepidoptera: Analyzing the Gut Microbiota for Sustainable Pest Management
by Abdul Basit, Inzamam Ul Haq, Moazam Hyder, Muhammad Humza, Muhammad Younas, Muhammad Rehan Akhtar, Muhammad Adeel Ghafar, Tong-Xian Liu and Youming Hou
Biology 2025, 14(8), 937; https://doi.org/10.3390/biology14080937 - 25 Jul 2025
Viewed by 317
Abstract
Recent advances in microbiome studies have deepened our understanding of endosymbionts and gut-associated microbiota in host biology. Of those, lepidopteran systems in particular harbor a complex and diverse microbiome with various microbial taxa that are stable and transmitted between larval and adult stages, [...] Read more.
Recent advances in microbiome studies have deepened our understanding of endosymbionts and gut-associated microbiota in host biology. Of those, lepidopteran systems in particular harbor a complex and diverse microbiome with various microbial taxa that are stable and transmitted between larval and adult stages, and others that are transient and context-dependent. We highlight key microorganisms—including Bacillus, Lactobacillus, Escherichia coli, Pseudomonas, Rhizobium, Fusarium, Aspergillus, Saccharomyces, Bifidobacterium, and Wolbachia—that play critical roles in microbial ecology, biotechnology, and microbiome studies. The fitness implications of these microbial communities can be variable; some microbes improve host performance, while others neither positively nor negatively impact host fitness, or their impact is undetectable. This review examines the central position played by the gut microbiota in interactions of insects with plants, highlighting the functions of the microbiota in the manipulation of the behavior of herbivorous pests, modulating plant physiology, and regulating higher trophic levels in natural food webs. It also bridges microbiome ecology and applied pest management, emphasizing S. frugiperda as a model for symbiont-based intervention. As gut microbiota are central to the life history of herbivorous pests, we consider how these interactions can be exploited to drive the development of new, environmentally sound biocontrol strategies. Novel biotechnological strategies, including symbiont-based RNA interference (RNAi) and paratransgenesis, represent promising but still immature technologies with major obstacles to overcome in their practical application. However, microbiota-mediated pest control is an attractive strategy to move towards sustainable agriculture. Significantly, the gut microbiota of S. frugiperda is essential for S. frugiperda to adapt to a wide spectrum of host plants and different ecological niches. Studies have revealed that the microbiome of S. frugiperda has a close positive relationship with the fitness and susceptibility to entomopathogenic fungi; therefore, targeting the S. frugiperda microbiome may have good potential for innovative biocontrol strategies in the future. Full article
(This article belongs to the Special Issue Recent Advances in Wolbachia and Spiroplasma Symbiosis)
Show Figures

Graphical abstract

16 pages, 3775 KiB  
Article
Optimizing Energy Efficiency in Last-Mile Delivery: A Collaborative Approach with Public Transportation System and Drones
by Pierre Romet, Charbel Hage, El-Hassane Aglzim, Tonino Sophy and Franck Gechter
Drones 2025, 9(8), 513; https://doi.org/10.3390/drones9080513 - 22 Jul 2025
Viewed by 291
Abstract
Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission [...] Read more.
Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
Show Figures

Figure 1

23 pages, 15163 KiB  
Article
3D Dubins Curve-Based Path Planning for UUV in Unknown Environments Using an Improved RRT* Algorithm
by Feng Pan, Peng Cui, Bo Cui, Weisheng Yan and Shouxu Zhang
J. Mar. Sci. Eng. 2025, 13(7), 1354; https://doi.org/10.3390/jmse13071354 - 16 Jul 2025
Viewed by 229
Abstract
The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved [...] Read more.
The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
Show Figures

Figure 1

22 pages, 590 KiB  
Review
ROS-Based Navigation and Obstacle Avoidance: A Study of Architectures, Methods, and Trends
by Zhe Wei, Sen Wang, Kangyelin Chen and Fang Wang
Sensors 2025, 25(14), 4306; https://doi.org/10.3390/s25144306 - 10 Jul 2025
Viewed by 632
Abstract
With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation [...] Read more.
With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems. Full article
Show Figures

Figure 1

42 pages, 4946 KiB  
Article
Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection
by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li and Kangshun Li
J. Mar. Sci. Eng. 2025, 13(7), 1294; https://doi.org/10.3390/jmse13071294 - 30 Jun 2025
Viewed by 295
Abstract
Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with [...] Read more.
Autonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded Energy-Optimized Batch Informed Trees (MEO-BIT*) algorithm with Deep Q-Networks (DQN) to achieve robust AUV autonomy. The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. Critically, the DQN component facilitates dynamic obstacle detection and adaptive local navigation, enabling the AUV to adjust its trajectory intelligently in real time. This integrated approach leverages the strengths of both algorithms. The global path intelligence of MEO-BIT* is dynamically informed and refined by the DQN’s learned perception. This allows the DQN to make effective decisions to avoid moving obstacles. Experimental validation in a simulated Achao waterway (Chile) demonstrates the MEO-BIT* + DQN system’s superiority, achieving a 46% reduction in collision rates (directly reflecting improved detection and avoidance fusion), a 15.7% improvement in path smoothness, and a 78.9% faster execution time compared to conventional RRT* and BIT* methods. This work presents a robust solution that effectively fuses two key components: the computational efficiency of MEO-BIT* and the adaptive capabilities of DQN. This fusion significantly advances the integration of navigation with dynamic obstacle detection. Ultimately, it enhances AUV operational performance and autonomy in complex maritime scenarios. Full article
(This article belongs to the Special Issue Navigation and Detection Fusion for Autonomous Underwater Vehicles)
Show Figures

Figure 1

22 pages, 3885 KiB  
Article
Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping
by Ben Taylor, Mathew Allen, Preston Henson, Xu Gao, Haroon Malik and Pingping Zhu
Appl. Sci. 2025, 15(13), 7340; https://doi.org/10.3390/app15137340 - 30 Jun 2025
Viewed by 412
Abstract
Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and [...] Read more.
Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation. Full article
Show Figures

Figure 1

17 pages, 10129 KiB  
Article
Tennis Game Dynamic Prediction Model Based on Players’ Momentum
by Lechuan Wang, Puning Chen and Qurat Ul An Sabir
AppliedMath 2025, 5(3), 77; https://doi.org/10.3390/appliedmath5030077 - 26 Jun 2025
Viewed by 785
Abstract
Psychological momentum dynamics in tennis have triggered interest for a long time, but measuring their impact presents substantial obstacles. In this paper, we present an approach to quantify momentum that combines real-time winning probabilities, leverage, and an exponentially weighted moving average (EWMA). We [...] Read more.
Psychological momentum dynamics in tennis have triggered interest for a long time, but measuring their impact presents substantial obstacles. In this paper, we present an approach to quantify momentum that combines real-time winning probabilities, leverage, and an exponentially weighted moving average (EWMA). We test the method on a high-profile match between Carlos Alcaraz and Novak Djokovic, demonstrating how changes in leverage affect momentum. Furthermore, we use feature extraction methods from time series analysis to derive momentum-related characteristics, which are critical inputs for creating an eXtreme Gradient Boosting (XGBoost) binary classification model to predict game winners. The algorithm has an average accuracy of 84% and provides real-time predictions of each player’s chances of winning the match. Our findings indicate that momentum is a somewhat relevant element in forecasting match outcomes, highlighting its potential value in improving match prediction systems. Full article
Show Figures

Figure 1

27 pages, 7066 KiB  
Article
A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility
by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon and Kyuchul Lee
Drones 2025, 9(7), 460; https://doi.org/10.3390/drones9070460 - 26 Jun 2025
Viewed by 673
Abstract
As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and [...] Read more.
As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
Show Figures

Figure 1

22 pages, 5887 KiB  
Article
Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
by Jianlong Dai, Yinghao Chai and Peiyin Xiong
Appl. Sci. 2025, 15(13), 6953; https://doi.org/10.3390/app15136953 - 20 Jun 2025
Viewed by 318
Abstract
This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose [...] Read more.
This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article
Show Figures

Figure 1

14 pages, 3860 KiB  
Article
Large Eddy Simulations on the Diffusion Features of the Cold-Vented Natural Gas Containing Sulfur
by Xu Sun, Meijiao Song, Sen Dong, Dongying Wang, Yibao Guo, Jinpei Wang and Jingjing Yu
Processes 2025, 13(6), 1940; https://doi.org/10.3390/pr13061940 - 19 Jun 2025
Viewed by 323
Abstract
For cold venting processes frequently employed in oil and gas fields, precisely predicting the instantaneous diffusion process of the vented explosive and/or toxic gases is of great importance, which cannot be captured by the Reynolds-averaged Navier–Stokes (RANS) method. In this paper, the large [...] Read more.
For cold venting processes frequently employed in oil and gas fields, precisely predicting the instantaneous diffusion process of the vented explosive and/or toxic gases is of great importance, which cannot be captured by the Reynolds-averaged Navier–Stokes (RANS) method. In this paper, the large eddy simulation (LES) method is introduced for gas diffusion in an open space, and the diffusion characteristics of the sulfur-containing natural gas in the cold venting process is analyzed numerically. Firstly, a LES solution procedure of compressible gas diffusion is proposed based on the ANSYS Fluent 2022, and the numerical solution is verified using benchmark experiments. Subsequently, a computational model of the sulfur-containing natural gas diffusion process under the influence of a wind field is established, and the effects of wind speed, sulfur content, the venting rate and a downstream obstacle on the natural gas diffusion process are analyzed in detail. The results show that the proposed LES with the DSM sub-grid model is able to capture the transient diffusion process of heavy and light gases released in turbulent wind flow; the ratio between the venting rate and wind speed has a decisive influence on the gas diffusion process: a large venting rate increases the vertical diffusion distance and makes the gas cloud fluctuate more, while a large wind speed decreases the vertical width and stabilizes the gas cloud; for an obstacle located closely downstream, the venting pipe makes the vented gas gather on the windward side and move toward the ground, increasing the risk of ignition and poisoning near the ground. The LES solution procedure provides a more powerful tool for simulating the cold venting process of natural gas, and the results obtained could provide a theoretical basis for the safety evaluation and process optimization of sulfur-containing natural gas venting. Full article
(This article belongs to the Section Energy Systems)
Show Figures

Figure 1

35 pages, 4434 KiB  
Article
MDO of Robotic Landing Gear Systems: A Hybrid Belt-Driven Compliant Mechanism for VTOL Drones Application
by Masoud Kabganian and Seyed M. Hashemi
Drones 2025, 9(6), 434; https://doi.org/10.3390/drones9060434 - 14 Jun 2025
Viewed by 491
Abstract
This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground [...] Read more.
This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground slopes of 6–15°, beyond which rollover would happen. Moreover, articulated RLG concepts come with added complexity and weight penalties due to multiple drivetrain components. Previous research has highlighted that even a minor 3-degree slope change can increase the dynamic rollover risks by 40%. Therefore, the design optimization of robotic landing gear for enhanced VTOL capabilities requires a multidisciplinary framework that integrates static analysis, dynamic simulation, and control strategies for operations on complex terrain. This paper presents a novel, hybrid, compliant, belt-driven, three-legged RLG system, supported by a multidisciplinary design optimization (MDO) methodology, aimed at achieving enhanced VTOL capabilities on uneven surfaces and moving platforms like ship decks. The proposed system design utilizes compliant mechanisms featuring a series of three-flexure hinges (3SFH), to reduce the number of articulated drivetrain components and actuators. This results in a lower system weight, improved energy efficiency, and enhanced durability, compared to earlier fully actuated, articulated, four-legged, two-jointed designs. Additionally, the compliant belt-driven actuation mitigates issues such as backlash, wear, and high maintenance, while enabling smoother torque transfer and improved vibration damping relative to earlier three-legged cable-driven four-bar link RLG systems. The use of lightweight yet strong materials—aluminum and titanium—enables the legs to bend 19 and 26.57°, respectively, without failure. An animated simulation of full-contact landing tests, performed using a proportional-derivative (PD) controller and ship deck motion input, validate the performance of the design. Simulations are performed for a VTOL UAV, with two flexible legs made of aluminum, incorporating circular flexure hinges, and a passive third one positioned at the tail. The simulation results confirm stable landings with a 2 s settling time and only 2.29° of overshoot, well within the FAA-recommended maximum roll angle of 2.9°. Compared to the single-revolute (1R) model, the implementation of the optimal 3R Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. It is anticipated that the proposed hybrid design would also offer improved durability and ease of maintenance, thereby enhancing functionality and safety in comparison with existing robotic landing gear systems. Full article
Show Figures

Figure 1

29 pages, 1728 KiB  
Article
Who Can Afford to Decarbonize? Early Insights from a Socioeconomic Model for Energy Retrofit Decision-Making
by Daniela Tavano, Francesca Salvo, Marilena De Simone, Antonio Bilotta and Francesco Paolo Del Giudice
Real Estate 2025, 2(2), 6; https://doi.org/10.3390/realestate2020006 - 11 Jun 2025
Cited by 1 | Viewed by 373
Abstract
The real estate sector is steadily moving towards zero-emission buildings, driven by EU policies to achieve near-zero energy (NZEB) buildings by 2050. In Italy, more than 70% of residential buildings fall into the lower energy classes, and this mainly affects low-income households. As [...] Read more.
The real estate sector is steadily moving towards zero-emission buildings, driven by EU policies to achieve near-zero energy (NZEB) buildings by 2050. In Italy, more than 70% of residential buildings fall into the lower energy classes, and this mainly affects low-income households. As a result, the decarbonisation of the real estate sector presents both technical and socio-economic obstacles. Building on these premises, this study introduces the Retrofit Optimization Problem (ROP), a methodological framework adapted from the Multidimensional Knapsack Problem (MdKP). This method is used in this study to conduct a qualitative analysis of accessibility to retrofit between different socio-economic groups, integrating constraints to simulate restructuring capacity based on different incomes. The results show significant disparities: although many retrofit strategies can meet regulatory energy performance targets, only a small number are financially sustainable for low-income households. In addition, interventions with the greatest environmental impact remain inaccessible to vulnerable groups. These preliminary results highlight important equity issues in the energy transition, indicating the need for specific and income-sensitive policies to prevent decarbonisation efforts from exacerbating social inequalities or increasing the risk of assets being stranded in the housing market. Full article
Show Figures

Figure 1

Back to TopTop