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Keywords = soft energy paths

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32 pages, 2102 KiB  
Article
D* Lite and Transformer-Enhanced SAC: A Hybrid Reinforcement Learning Framework for COLREGs-Compliant Autonomous Navigation in Dynamic Maritime Environments
by Tianqing Chen, Yamei Lan, Yichen Li, Jiesen Zhang and Yijie Yin
J. Mar. Sci. Eng. 2025, 13(8), 1498; https://doi.org/10.3390/jmse13081498 - 4 Aug 2025
Abstract
Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently [...] Read more.
Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article
(This article belongs to the Special Issue Motion Control and Path Planning of Marine Vehicles—3rd Edition)
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22 pages, 5966 KiB  
Article
Road-Adaptive Precise Path Tracking Based on Reinforcement Learning Method
by Bingheng Han and Jinhong Sun
Sensors 2025, 25(15), 4533; https://doi.org/10.3390/s25154533 - 22 Jul 2025
Viewed by 275
Abstract
This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature [...] Read more.
This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
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21 pages, 5973 KiB  
Article
Soft Conductive Textile Sensors: Characterization Methodology and Behavioral Analysis
by Giulia Gamberini, Selene Tognarelli and Arianna Menciassi
Sensors 2025, 25(14), 4448; https://doi.org/10.3390/s25144448 - 17 Jul 2025
Viewed by 380
Abstract
Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize [...] Read more.
Resistive stretching sensors are currently used in healthcare robotics due to their ability to vary electrical resistance when subjected to mechanical strain. However, commercial sensors often lack the softness required for integration into soft structures. This study presents a detailed methodology to characterize fabric-based resistive stretching sensors, focusing on both static and dynamic performance, for application in a smart vascular simulator for surgical training. Five sensors, called #1–#5, were developed using conductive fabrics integrated into soft silicone. Stability and fatigue tests were performed to evaluate their behavior. The surface structure and fiber distribution were analyzed using digital microscopy and scanning electron microscopy, while element analysis was performed via Energy-Dispersive X-ray Spectroscopy. Sensors #1 and #3 are the most stable with a low relative standard deviation and good sensitivity at low strains. Sensor #3 showed the lowest hysteresis, while sensor #1 had the widest operating range (0–30% strain). Although all sensors showed non-monotonic behavior across 0–100% strain, deeper investigation suggested that the sensor response depends on the configuration of conductive paths within and between fabric layers. Soft fabric-based resistive sensors represent a promising technical solution for physical simulators for surgical training. Full article
(This article belongs to the Special Issue Sensor Technology in Robotic Surgery)
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33 pages, 5490 KiB  
Article
Comparative Evaluation of Reinforcement Learning Algorithms for Multi-Agent Unmanned Aerial Vehicle Path Planning in 2D and 3D Environments
by Mirza Aqib Ali, Adnan Maqsood, Usama Athar and Hasan Raza Khanzada
Drones 2025, 9(6), 438; https://doi.org/10.3390/drones9060438 - 16 Jun 2025
Viewed by 1104
Abstract
Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path planning in 2D [...] Read more.
Path planning in multi-agent UAV swarms is a crucial issue that involves avoiding collisions in dynamic, obstacle-filled environments while consuming the least amount of time and energy possible. This work comprehensively evaluates reinforcement learning (RL) algorithms for multi-agent UAV path planning in 2D and 3D simulated environments. First, we develop a 2D simulation setup using Python in which UAVs (quadcopters), represented as points in space, navigate toward their respective targets while avoiding static obstacles and inter-agent collisions. In the second phase, we transition this comparison to a physics-based 3D simulation, incorporating realistic UAV (fixed wing) dynamics and checkpoint-based navigation. We compared five algorithms, namely, Proximal Policy Optimization (PPO), Soft Actor–Critic (SAC), Deep Deterministic Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO), and Multi–Agent DDPG (MADDPG), in various scenarios. Our findings reveal significant performance differences between the algorithms across multiple dimensions. DDPG consistently demonstrated superior reward optimization and collision avoidance performance, while PPO and MADDPG excelled in the execution time required to reach the goal. Furthermore, our findings reveal how algorithms perform while transitioning from a simplistic 2D setup to a realistic 3D physics-based environment, which is essential for performing sim-to-real transfer. This work provides valuable insights into the suitability of several reinforcement learning (RL) algorithms for developing autonomous systems and UAV swarm navigation. Full article
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18 pages, 6756 KiB  
Article
An Isolated Resonant Voltage Balancing Charger of Series-Connected Lithium-Ion Batteries Based on Multi-Port Transformer
by Xifeng Xie, Chunjian Cai, Jianglin Nie, Shijie Jiao and Zeliang Shu
Electronics 2025, 14(9), 1861; https://doi.org/10.3390/electronics14091861 - 2 May 2025
Viewed by 397
Abstract
The inconsistency of individual lithium-ion batteries causes the voltage imbalance of the batteries. An effective voltage-balancing circuit is essential to improve the inconsistency of series-connected batteries. This paper presents an isolated resonant voltage-balancing circuit for series-connected lithium-ion batteries based on a multi-port transformer. [...] Read more.
The inconsistency of individual lithium-ion batteries causes the voltage imbalance of the batteries. An effective voltage-balancing circuit is essential to improve the inconsistency of series-connected batteries. This paper presents an isolated resonant voltage-balancing circuit for series-connected lithium-ion batteries based on a multi-port transformer. This circuit utilizes the multi-port transformer to enable free energy flow among the batteries. Without any direct transmission path between the capacitor and the battery string, it achieves full isolation between them. The resonant circuit is adopted to realize the soft-switching operation. Compared with other active balancing circuits, the proposed circuit requires only half a winding and one transistor per individual battery. Consequently, the proposed circuit enhances power density and further improves efficiency and reliability. Additionally, a fixed-group-number control strategy is introduced to enhance the circuit’s equalization voltage capabilities. Finally, a prototype of the voltage-balancing circuit for 24 series-connected lithium-ion batteries is established to verify the effectiveness and feasibility of the proposed circuit. Full article
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26 pages, 4783 KiB  
Article
A Hybrid Decision-Making Framework for UAV-Assisted MEC Systems: Integrating a Dynamic Adaptive Genetic Optimization Algorithm and Soft Actor–Critic Algorithm with Hierarchical Action Decomposition and Uncertainty-Quantified Critic Ensemble
by Yu Yang, Yanjun Shi, Xing Cui, Jiajian Li and Xijun Zhao
Drones 2025, 9(3), 206; https://doi.org/10.3390/drones9030206 - 13 Mar 2025
Viewed by 1137
Abstract
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods [...] Read more.
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods suffer from limitations such as difficulty in balancing multiple objectives and training convergence when making mixed action space decisions for UAV path planning and task offloading. This article innovatively proposes a hybrid decision framework based on the improved Dynamic Adaptive Genetic Optimization Algorithm (DAGOA) and soft actor–critic with hierarchical action decomposition, an uncertainty-quantified critic ensemble, and adaptive entropy temperature, where DAGOA performs an effective search and optimization in discrete action space, while SAC can perform fine control and adjustment in continuous action space. By combining the above algorithms, the joint optimization of drone path planning and task offloading can be achieved, improving the overall performance of the system. The experimental results show that the framework offers significant advantages in improving system performance, reducing energy consumption, and enhancing task completion efficiency. When the system adopts a hybrid decision framework, the reward score increases by a maximum of 153.53% compared to pure deep reinforcement learning algorithms for decision-making. Moreover, it can achieve an average improvement of 61.09% on the basis of various reinforcement learning algorithms such as proposed SAC, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3). Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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16 pages, 4674 KiB  
Article
Wave Attenuation by Australian Temperate Mangroves
by Ruth Reef and Sabrina Sayers
J. Mar. Sci. Eng. 2025, 13(2), 382; https://doi.org/10.3390/jmse13020382 - 19 Feb 2025
Cited by 1 | Viewed by 861
Abstract
Wave attenuation by natural coastal features is recognised as a soft engineering approach to shoreline protection from storm surges and destructive waves. The effectiveness of wave energy dissipation is determined, in part, by vegetation structure, extent, and distribution. Mangroves line ca. 15% of [...] Read more.
Wave attenuation by natural coastal features is recognised as a soft engineering approach to shoreline protection from storm surges and destructive waves. The effectiveness of wave energy dissipation is determined, in part, by vegetation structure, extent, and distribution. Mangroves line ca. 15% of the world’s coastlines, primarily in tropical and subtropical regions but also extending into temperate climates, where mangroves are shorter and multi-stemmed. Using wave loggers deployed across mangrove and non-mangrove shorelines, we studied the wave attenuating capacity and the drag coefficient (CD) of temperate Avicennia marina mangrove forests of varying structure in Western Port, Australia. The structure of the vegetation obstructing the flow path was represented along each transect in a three-dimensional point cloud derived from overlapping uncrewed aerial vehicle (UAV) images and structure-from-motion (SfM) algorithms. The wave attenuation coefficient (b) calculated from a fitted exponential decay model at the vegetated sites was on average 0.011 m−1 relative to only 0.009 m−1 at the unvegetated site. We calculated a CD for this forest type that ranged between 2.7 and 4.9, which is within the range of other pencil-rooted species such as Sonneratia sp. but significantly lower than prop-rooted species such as Rhizophora spp. Wave attenuation efficiency significantly decreased with increasing water depth, highlighting the dominance of near-bed friction on attenuation in this forest type. The UAV-derived point cloud did not describe the vegetation (especially near-bed) in sufficient detail to accurately depict the obstacles. We found that a temperate mangrove greenbelt of just 100 m can decrease incoming wave heights by close to 70%, indicating that, similarly to tropical and subtropical forests, temperate mangroves significantly attenuate incoming wave energy under normal sea conditions. Full article
(This article belongs to the Section Coastal Engineering)
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32 pages, 6349 KiB  
Review
Liquid Metal–Polymer Hydrogel Composites for Sustainable Electronics: A Review
by Abdollah Hajalilou
Molecules 2025, 30(4), 905; https://doi.org/10.3390/molecules30040905 - 15 Feb 2025
Cited by 1 | Viewed by 2126
Abstract
Hydrogels, renowned for their hydrophilic and viscoelastic properties, have emerged as key materials for flexible electronics, including electronic skins, wearable devices, and soft sensors. However, the application of pure double network hydrogel-based composites is limited by their poor chemical stability, low mechanical stretchability, [...] Read more.
Hydrogels, renowned for their hydrophilic and viscoelastic properties, have emerged as key materials for flexible electronics, including electronic skins, wearable devices, and soft sensors. However, the application of pure double network hydrogel-based composites is limited by their poor chemical stability, low mechanical stretchability, and low sensitivity. Recent research has focused on overcoming these limitations by incorporating conductive fillers, such as liquid metals (LMs), into hydrogel matrices or creating continuous conductive paths through LMs within the polymer matrix. LMs, including eutectic gallium and indium (EGaIn) alloys, offer exceptional electromechanical, electrochemical, thermal conductivity, and self-repairing properties, making them ideal candidates for diverse soft electronic applications. The integration of LMs into hydrogels improves conductivity and mechanical performance while addressing the challenges posed by rigid fillers, such as mismatched compliance with the hydrogel matrix. This review explores the incorporation of LMs into hydrogel composites, the challenges faced in achieving optimal dispersion, and the unique functionalities introduced by these composites. We also discuss recent advances in the use of LM droplets for polymerization processes and their applications in various fields, including tissue engineering, wearable devices, biomedical applications, electromagnetic shielding, energy harvesting, and storage. Additionally, 3D-printable hydrogels are highlighted. Despite the promise of LM-based hydrogels, challenges such as macrophase separation, weak interfacial interactions between LMs and polymer networks, and the difficulty of printing LM inks onto hydrogel substrates limit their broader application. However, this review proposes solutions to these challenges. Full article
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25 pages, 5516 KiB  
Article
Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm
by Yiquan Wang, Yan Cui, Yu Yang, Zhaodong Li and Xing Cui
Drones 2024, 8(12), 706; https://doi.org/10.3390/drones8120706 - 26 Nov 2024
Viewed by 1673
Abstract
With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to [...] Read more.
With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to address the local optimization problem, improving the communication efficiency and task execution capability of UAV cluster control. This paper explores the path planning problem in multi-UAV-to-ground relay communication, with a special focus on the application of the proposed Mix-Greedy MAPPO algorithm. The state space, action space, communication model, training environment, and reward function are designed by comprehensively considering the actual tasks and entity characteristics such as safe distance, no-fly zones, survival in a threatened environment, and energy consumption. The results show that the Mix-Greedy MAPPO algorithm significantly improves communication probability, reduces energy consumption, avoids no-fly zones, and facilitates exploration compared to other algorithms in the multi-UAV ground communication relay path planning task. After training with the same number of steps, the Mix-Greedy MAPPO algorithm has an average reward score that is 45.9% higher than the MAPPO algorithm and several times higher than the multi-agent soft actor-critic (MASAC) and multi-agent deep deterministic policy gradient (MADDPG) algorithms. The experimental results verify the superiority and adaptability of the algorithm in complex environments. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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19 pages, 4923 KiB  
Article
Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning
by Marco Rinaldi, Stefano Primatesta, Martin Bugaj, Ján Rostáš and Giorgio Guglieri
Smart Cities 2024, 7(5), 2842-2860; https://doi.org/10.3390/smartcities7050110 - 6 Oct 2024
Cited by 4 | Viewed by 3342
Abstract
In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology [...] Read more.
In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Full article
(This article belongs to the Special Issue Smart Urban Air Mobility)
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31 pages, 1117 KiB  
Article
Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps
by Anna Kozlowska, Francesco Guarino, Rosaria Volpe, Adriano Bisello, Andrea Gabaldòn, Abolfazl Rezaei, Vicky Albert-Seifried, Beril Alpagut, Han Vandevyvere, Francesco Reda, Giovanni Tumminia, Saeed Ranjbar, Roberta Rincione, Salvatore Cellura, Ursula Eicker, Shokufeh Zamini, Sergio Diaz de Garayo Balsategui, Matthias Haase and Lorenza Di Pilla
Energies 2024, 17(17), 4425; https://doi.org/10.3390/en17174425 - 3 Sep 2024
Cited by 6 | Viewed by 4366
Abstract
The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach [...] Read more.
The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach for Positive Energy Districts. As emerged throughout the workshop held during the fourth edition of Smart and Sustainable Planning for Cities and Regions Conference (SSPCR 2022) in Bolzano (Italy), further critical points are also linked to the planning, modeling and assessment steps, besides sustainability aspects and stakeholders’ involvement. The “World Café” methodology adopted during the workshop allowed for simple—but also effective and flexible—group discussions focused on the detection of key PED characteristics, such as morphologic, socio-economic, demographic, technological, quality-of-life and feasibility factors. Four main work groups were defined in order to allow them to share, compare and discuss around five main PED-related topics: energy efficiency, energy flexibility, e-mobility, soft mobility, and low-carbon generation. Indeed, to properly deal with PED challenges and crucial aspects, it is necessary to combine and balance these technologies with enabler factors like financing instruments, social innovation and involvement, innovative governance and far-sighted policies. This paper proposes, in a structured form, the main outcomes of the co-creation approach developed during the workshop. The importance of implementing a holistic approach was highlighted: it requires a systematic and consistent integration of economic, environmental and social aspects directly connected to an interdisciplinary cross-sectorial collaboration between researchers, policymakers, industries, municipalities, and citizens. Furthermore, it was reaffirmed that, to make informed and reasoned decisions throughout an effective PED design and planning process, social, ecological, and cultural factors (besides merely technical aspects) play a crucial role. Thanks to the valuable insights and recommendations gathered from the workshop participants, a conscious awareness of key issues in PED design and implementation emerged, and the fundamental role of stakeholders in the PED development path was confirmed. Full article
(This article belongs to the Topic Smart Electric Energy in Buildings)
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19 pages, 5349 KiB  
Article
UAV Path Planning Based on Random Obstacle Training and Linear Soft Update of DRL in Dense Urban Environment
by Yanfei Zhu, Yingjie Tan, Yongfa Chen, Liudan Chen and Kwang Y. Lee
Energies 2024, 17(11), 2762; https://doi.org/10.3390/en17112762 - 5 Jun 2024
Cited by 6 | Viewed by 1802
Abstract
The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while [...] Read more.
The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm’s robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm’s stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes. Full article
(This article belongs to the Section B: Energy and Environment)
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16 pages, 1524 KiB  
Article
Soft Actor-Critic and Risk Assessment-Based Reinforcement Learning Method for Ship Path Planning
by Jue Wang, Bin Ji and Qian Fu
Sustainability 2024, 16(8), 3239; https://doi.org/10.3390/su16083239 - 12 Apr 2024
Cited by 1 | Viewed by 1785
Abstract
Ship path planning is one of the most important themes in waterway transportation, which is deemed as the cleanest mode of transportation due to its environmentally friendly and energy-efficient nature. A path-planning method that combines the soft actor-critic (SAC) and navigation risk assessment [...] Read more.
Ship path planning is one of the most important themes in waterway transportation, which is deemed as the cleanest mode of transportation due to its environmentally friendly and energy-efficient nature. A path-planning method that combines the soft actor-critic (SAC) and navigation risk assessment is proposed to address ship path planning in complex water environments. Specifically, a continuous environment model is established based on the Markov decision process (MDP), which considers the characteristics of the ship path-planning problem. To enhance the algorithm’s performance, an information detection strategy for restricted navigation areas is employed to improve state space, converting absolute bearing into relative bearing. Additionally, a risk penalty based on the navigation risk assessment model is introduced to ensure path safety while imposing potential energy rewards regarding navigation distance and turning angle. Finally, experimental results obtained from a navigation simulation environment verify the robustness of the proposed method. The results also demonstrate that the proposed algorithm achieves a smaller path length and sum of turning angles with safety and fuel economy improvement compared with traditional methods such as RRT (rapidly exploring random tree) and DQN (deep Q-network). Full article
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33 pages, 15630 KiB  
Review
Polymer Hydrogels and Frontal Polymerization: A Winning Coupling
by Alberto Mariani and Giulio Malucelli
Polymers 2023, 15(21), 4242; https://doi.org/10.3390/polym15214242 - 27 Oct 2023
Cited by 7 | Viewed by 2673
Abstract
Polymer hydrogels are 3D networks consisting of hydrophilic crosslinked macromolecular chains, allowing them to swell and retain water. Since their invention in the 1960s, they have become an outstanding pillar in the design, development, and application of engineered polymer systems suitable for biomedical [...] Read more.
Polymer hydrogels are 3D networks consisting of hydrophilic crosslinked macromolecular chains, allowing them to swell and retain water. Since their invention in the 1960s, they have become an outstanding pillar in the design, development, and application of engineered polymer systems suitable for biomedical and pharmaceutical applications (such as drug or cell delivery, the regeneration of hard and soft tissues, wound healing, and bleeding prevention, among others). Despite several well-established synthetic routes for developing polymer hydrogels based on batch polymerization techniques, about fifteen years ago, researchers started to look for alternative methods involving simpler reaction paths, shorter reaction times, and lower energy consumption. In this context, frontal polymerization (FP) has undoubtedly become an alternative and efficient reaction model that allows for the conversion of monomers into polymers via a localized and propagating reaction—by means of exploiting the formation and propagation of a “hot” polymerization front—able to self-sustain and propagate throughout the monomeric mixture. Therefore, the present work aims to summarize the main research outcomes achieved during the last few years concerning the design, preparation, and application of FP-derived polymeric hydrogels, demonstrating the feasibility of this technique for the obtainment of functional 3D networks and providing the reader with some perspectives for the forthcoming years. Full article
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64 pages, 13019 KiB  
Review
Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review
by Haoming Liu, Muhammad Yasir Ali Khan and Xiaoling Yuan
Energies 2023, 16(15), 5665; https://doi.org/10.3390/en16155665 - 27 Jul 2023
Cited by 20 | Viewed by 3461
Abstract
To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: [...] Read more.
To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives. Full article
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