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Search Results (1,034)

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Keywords = multi path planning

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28 pages, 3712 KB  
Article
Conflict-Free 3D Path Planning for Multi-UAV Based on Jump Point Search and Incremental Update
by Yuan Lu, De Yan, Zhiqiang Wan and Chuanyan Feng
Drones 2025, 9(10), 688; https://doi.org/10.3390/drones9100688 (registering DOI) - 4 Oct 2025
Abstract
To address the challenges of frequent path conflicts and prolonged computation times in path planning for large-scale multi-UAV operations within urban low-altitude airspace, this study proposes a conflict-free path planning method integrating 3D Jump Point Search (JPS) and an incremental update mechanism. A [...] Read more.
To address the challenges of frequent path conflicts and prolonged computation times in path planning for large-scale multi-UAV operations within urban low-altitude airspace, this study proposes a conflict-free path planning method integrating 3D Jump Point Search (JPS) and an incremental update mechanism. A hierarchical algorithmic architecture is employed: the lower level utilizes the 3D-JPS algorithm for efficient single-UAV path planning, while the upper level implements a conflict detection and resolution mechanism based on a dual-objective cost function and incremental updates for multi-UAV coordination. Large-scale UAV path planning simulations were conducted using a 3D grid model representing urban low-altitude airspace, with performance comparisons made against traditional methods. The results demonstrate that the proposed algorithm significantly reduces the number of path search nodes and exhibits superior computational efficiency for large-scale UAV path planning. Specifically, under high-density scenarios of 120 UAVs per square kilometer, the proposed DOCBS + IJPS method can reduce the conflict-free path planning time by 35.56% compared to the traditional CBS + A* conflict search and resolution algorithm. Full article
(This article belongs to the Section Artificial Intelligence in Drones (AID))
36 pages, 2558 KB  
Article
Research on Warship System Resilience Based on Intelligent Recovery with Improved Ant Colony Optimization
by Zhen Li, Luhong Wang, Lingzhong Meng and Guang Yang
Algorithms 2025, 18(10), 626; https://doi.org/10.3390/a18100626 - 3 Oct 2025
Abstract
Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) [...] Read more.
Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article
(This article belongs to the Special Issue Evolutionary and Swarm Computing for Emerging Applications)
26 pages, 5216 KB  
Article
A Data-Driven Method for Ship Route Planning Under Dynamic Environments
by Zhaofeng Song, Jinfen Zhang, Chengpeng Wan and C. Guedes Soares
J. Mar. Sci. Eng. 2025, 13(10), 1901; https://doi.org/10.3390/jmse13101901 - 3 Oct 2025
Abstract
The paper proposes an improved A* Algorithm based on historical AIS data for the multi-objective optimisation of ship weather routes, explicitly focusing on optimising voyage distance, economic costs, and emission costs within Sulphur Emission Control Areas. The method utilises trajectory interpolation, Ordering Points [...] Read more.
The paper proposes an improved A* Algorithm based on historical AIS data for the multi-objective optimisation of ship weather routes, explicitly focusing on optimising voyage distance, economic costs, and emission costs within Sulphur Emission Control Areas. The method utilises trajectory interpolation, Ordering Points to Identify the Clustering Structure, and the Douglas–Peucker algorithm to preprocess AIS data, thereby enhancing the flexibility and accuracy of multi-objective path planning. The method incorporates different cost weights and the time dimension to optimise different routes dynamically. The technique also optimises the route in real time by treating ship power as a decision variable, adjusting the power according to different task requirements. The proposed method is compared with other commonly used path planning algorithms within a specific maritime area. The results show that it offers better adaptability in terms of multi-objective costs and timeliness. Full article
(This article belongs to the Section Ocean Engineering)
23 pages, 7554 KB  
Article
A*-TEB: An Improved A* Algorithm Based on the TEB Strategy for Multi-Robot Motion Planning
by Xu Li, Tuanjie Li, Yan Zhang, Yulin Zhang, Ziang Li, Lixiang Ban and Kecheng Sun
Sensors 2025, 25(19), 6117; https://doi.org/10.3390/s25196117 - 3 Oct 2025
Abstract
Multi-robot motion planning (MRMP) requires each robot to possess strong local planning capabilities while maintaining global consistency. However, existing research often fails to address both global and local planning simultaneously, resulting in conflicts in real-time path execution. The A* algorithm is widely used [...] Read more.
Multi-robot motion planning (MRMP) requires each robot to possess strong local planning capabilities while maintaining global consistency. However, existing research often fails to address both global and local planning simultaneously, resulting in conflicts in real-time path execution. The A* algorithm is widely used for global path planning due to its adaptability and search efficiency, while the Timed Elastic Band (TEB) algorithm excels in local trajectory optimization and real-time dynamic obstacle avoidance. This paper presents a novel motion planning framework integrating an improved A* algorithm with an enhanced TEB strategy to address both levels of planning collaboratively. The proposed improvements include the introduction of steering costs and dynamic weights into the A* algorithm to enhance path smoothness and efficiency, and a hierarchical obstacle treatment in TEB for improved local avoidance. Simulation and real-world experiments conducted with ROS confirmed the feasibility and effectiveness of the method. Compared to the traditional A* algorithm, the proposed framework reduces the average path length by 5.2%, shortens completion time by 11.5%, and decreases inflection points by 66.7%, demonstrating superior performance for multi-robot systems in dynamic environments. Full article
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43 pages, 4987 KB  
Review
A Review of Robotic Aircraft Skin Inspection: From Data Acquisition to Defect Analysis
by Minnan Piao, Xuan Wang, Weiling Wang, Yonghui Xie and Biao Lu
Mathematics 2025, 13(19), 3161; https://doi.org/10.3390/math13193161 - 2 Oct 2025
Abstract
In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and [...] Read more.
In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) for external skin inspection, which present clear technical contributions, while excluding internal inspections and non-technical reports. Literature was retrieved from IEEE conferences, journals, and other academic databases, and key findings were summarized via the categorical analysis of motion planning, perception modules, and defect detection algorithms. Key limitations identified include the fragmentation of core technical modules, unresolved bottlenecks in dynamic environments, challenges in weak-texture and all-weather perception, and a lack of mature integrated systems with practical validation. The study concludes by advocating for future research in multi-robot heterogeneous collaborative systems, intelligent dynamic task scheduling, large model-based airworthiness assessment, and the expansion of inspection scenarios, all aimed at achieving fully autonomous and reliable operations. Full article
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22 pages, 12774 KB  
Article
Multi-Agent Coverage Path Planning Using Graph-Adapted K-Means in Road Network Digital Twin
by Haeseong Lee and Myungho Lee
Electronics 2025, 14(19), 3921; https://doi.org/10.3390/electronics14193921 - 1 Oct 2025
Abstract
In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are [...] Read more.
In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article
46 pages, 6388 KB  
Article
A Multi-Strategy Improved Zebra Optimization Algorithm for AGV Path Planning
by Cunji Zhang, Chuangeng Chen, Jiaqi Lu, Xuan Jing and Wei Liu
Biomimetics 2025, 10(10), 660; https://doi.org/10.3390/biomimetics10100660 - 1 Oct 2025
Abstract
The Zebra Optimization Algorithm (ZOA) is a metaheuristic algorithm inspired by the collective behavior of zebras in the wild. Like many other swarm intelligence algorithms, the ZOA faces several limitations, including slow convergence, susceptibility to local optima, and an imbalance between exploration and [...] Read more.
The Zebra Optimization Algorithm (ZOA) is a metaheuristic algorithm inspired by the collective behavior of zebras in the wild. Like many other swarm intelligence algorithms, the ZOA faces several limitations, including slow convergence, susceptibility to local optima, and an imbalance between exploration and exploitation. To address these challenges, this paper proposes an improved version of the ZOA, termed the Multi-strategy Improved Zebra Optimization Algorithm (MIZOA). First, a multi-population search strategy is introduced to replace the traditional single population structure, dividing the population into multiple subpopulations to enhance diversity and improve global convergence. Second, the mutation operation of genetic algorithm (GA) is integrated with the Metropolis criterion to boost exploration capability in the early stages while maintaining strong exploitation performance in the later stages. Third, a novel selective aggregation strategy is proposed, incorporating the hunting behavior of the Coati Optimization Algorithm (COA) and Lévy flight to further enhance global exploration and convergence accuracy during the defense phase. Experimental evaluations are conducted on 23 benchmark functions, comparing the MIZOA with eight existing swarm intelligence algorithms. The performance is assessed using non-parametric statistical tests, including the Wilcoxon rank-sum test and the Friedman test. The results demonstrate that the MIZOA achieves superior global convergence accuracy and optimization performance, confirming its robustness and effectiveness. The MIZOA was evaluated on real-world engineering problems against seven algorithms to validate its practical performance. Furthermore, when applied to path planning tasks for Automated Guided Vehicles (AGVs), the MIZOA consistently identifies paths closer to the global optimum in both simple and complex environments, thereby further validating the effectiveness of the proposed improvements. Full article
(This article belongs to the Section Biological Optimisation and Management)
47 pages, 24562 KB  
Article
An Improved Whale Migration Optimization Algorithm for Cooperative UAV 3D Path Planning
by Zhanwei Liu, Shichao Li and Hong Xu
Biomimetics 2025, 10(10), 655; https://doi.org/10.3390/biomimetics10100655 - 1 Oct 2025
Abstract
This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, [...] Read more.
This study proposes an Improved Whale Migration Algorithm (IWMA) to overcome the shortcomings of the original Whale Migration Algorithm, which suffers from premature convergence and insufficient local exploitation in high-dimensional multimodal optimization. IWMA introduces three enhancements: circle chaotic initialization to improve population diversity, a three-layer cooperative search framework to achieve a stronger balance between exploration and exploitation, and a dynamic adaptive mechanism with t-distribution re-exploration to reinforce both global escaping and local refinement. On the CEC2017 benchmark suite, IWMA demonstrates clear superiority over seven representative algorithms, delivering the best results on 27 out of 29 functions by best, 25 by mean, and 23 by standard deviation in 30 dimensions, and on 25, 18, and 18 functions, respectively, in 50 dimensions. Compared with other migration-based optimizers, its average rank improves by more than 30 percent, while runtime analysis shows only a small additional overhead of 7 to 12 percent. These outcomes, supported by convergence curves, boxplots, radar charts, and Wilcoxon tests, confirm the effectiveness of the proposed improvements. In six multi-UAV path planning scenarios, IWMA reduces the average cost by 14.5 percent compared with WMA and achieves up to 32.1 percent reduction in the most complex case. Overall, its average cost decreases by 27.4 percent across seven competitors, with a 23.6 percent improvement in the best solutions. These results demonstrate that the proposed modifications are effective, enabling IWMA to transfer its performance gains from benchmark tests to practical multi-UAV cooperative mission planning, where it consistently produces safer and smoother trajectories under complex constraints. Full article
(This article belongs to the Section Biological Optimisation and Management)
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27 pages, 2315 KB  
Article
Study on Travel Characteristics and Satisfaction in Low-Density Areas Based on MNL and SEM Models—A Case of Lanzhou
by Minan Yang, Liyun Wang, Xin Li and Yongsheng Qian
Sustainability 2025, 17(19), 8802; https://doi.org/10.3390/su17198802 - 30 Sep 2025
Abstract
This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It [...] Read more.
This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development. Full article
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35 pages, 5864 KB  
Article
Risk-Constrained Multi-Objective Deep Reinforcement Learning for AGV Path Planning in Rail Transit
by Zihan Yang and Huiyu Xiang
Appl. Syst. Innov. 2025, 8(5), 145; https://doi.org/10.3390/asi8050145 - 30 Sep 2025
Abstract
Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A [...] Read more.
Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A conflict-aware global planner, extended from the A* algorithm, generates feasible routes, while a multi-sensor perception stack integrates LiDAR and camera data to distinguish moving AGVs, static obstacles, and task targets. Based on this perception, a Deep Q-Network (DQN) policy with a tailored reward function enables real-time dynamic obstacle avoidance in complex traffic. Simulation results demonstrate that, compared with the Artificial Potential Field (APF) baseline, the proposed GG-DRL approach reduces collisions by ~70%, lowers planning time by 25–30%, shortens paths by 10–15%, and improves smoothness by 20–25%. On the Maze Benchmark Map, GG-DRL surpasses classical planners (e.g., RRT) and deep RL baselines (e.g., DDPG) in path quality, computation, and avoidance behavior, achieving an average path length of 81.12, computation time of 11.94 s, 5.2 avoidance maneuvers, and smoothness of 0.86. Robustness is maintained as a dynamic obstacles scale up to 30. These findings confirm that combining multi-sensor fusion with deep reinforcement learning enhances AGV safety, efficiency, and reliability, with broad potential for intelligent railway logistics. Full article
(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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43 pages, 5662 KB  
Article
Coordinating V2V Energy Sharing for Electric Fleets via Multi-Granularity Modeling and Dynamic Spatiotemporal Matching
by Zhaonian Ye, Qike Han, Kai Han, Yongzhen Wang, Changlu Zhao, Haoran Yang and Jun Du
Sustainability 2025, 17(19), 8783; https://doi.org/10.3390/su17198783 - 30 Sep 2025
Abstract
The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This [...] Read more.
The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article
(This article belongs to the Section Sustainable Transportation)
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17 pages, 26449 KB  
Article
Federated Learning for Distributed Multi-Robotic Arm Trajectory Optimization
by Fazal Khan and Zhuo Meng
Robotics 2025, 14(10), 137; https://doi.org/10.3390/robotics14100137 - 29 Sep 2025
Abstract
The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and [...] Read more.
The optimization of trajectories for multiple robotic arms in a shared workspace is critical for industrial automation but presents significant challenges, including data sharing, communication overhead, and adaptability in dynamic environments. Traditional centralized control methods require sharing raw sensor data, raising concerns and creating computational bottlenecks. This paper proposes a novel Federated Learning (FL) framework for distributed multi-robotic arm trajectory optimization. Our method enables collaborative learning where robots train a shared model locally and only exchange gradient updates, preserving data privacy. The framework integrates an adaptive Rapidly exploring Random Tree (RRT) algorithm enhanced with a dynamic pruning strategy to reduce computational overhead and ensure collision-free paths. Real-time synchronization is achieved via EtherCAT, ensuring precise coordination. Experimental results demonstrate that our approach achieves a 17% reduction in average path length, a 22% decrease in collision rate, and a 31% improvement in planning speed compared to a centralized RRT baseline, while reducing inter-robot communication overhead by 45%. This work provides a scalable and efficient solution for collaborative manipulation in applications ranging from assembly lines to warehouse automation. Full article
(This article belongs to the Section Sensors and Control in Robotics)
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21 pages, 5486 KB  
Article
Research on Mobile Energy Storage Configuration and Path Planning Strategy Under Dual Source-Load Uncertainty in Typhoon Disasters
by Bingchao Zhang, Chunyang Gong, Songli Fan, Jian Wang, Tianyuan Yu and Zhixin Wang
Energies 2025, 18(19), 5169; https://doi.org/10.3390/en18195169 - 28 Sep 2025
Abstract
In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of [...] Read more.
In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article
(This article belongs to the Special Issue Control Technologies for Wind and Photovoltaic Power Generation)
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22 pages, 17573 KB  
Article
Robust UAV Path Planning Using RSS in GPS-Denied and Dense Environments Based on Deep Reinforcement Learning
by Kyounghun Kim, Joonho Seon, Jinwook Kim, Jeongho Kim, Youngghyu Sun, Seongwoo Lee, Soohyun Kim, Byungsun Hwang, Mingyu Lee and Jinyoung Kim
Electronics 2025, 14(19), 3844; https://doi.org/10.3390/electronics14193844 - 28 Sep 2025
Abstract
A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical [...] Read more.
A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods. Full article
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19 pages, 4834 KB  
Article
Continuous Picking Path Planning Based on Lightweight Marigold Corollas Recognition in the Field
by Baojian Ma, Zhenghao Wu, Yun Ge, Bangbang Chen, Jijing Lin, He Zhang and Hao Xia
Biomimetics 2025, 10(10), 648; https://doi.org/10.3390/biomimetics10100648 - 26 Sep 2025
Abstract
This study addresses the core challenges of precise marigold corollas recognition and efficient continuous path planning under complex natural conditions (strong illumination, occlusion, adhesion) by proposing an integrated lightweight visual recognition and real-time path planning framework. We introduce MPD-YOLO, an optimized model based [...] Read more.
This study addresses the core challenges of precise marigold corollas recognition and efficient continuous path planning under complex natural conditions (strong illumination, occlusion, adhesion) by proposing an integrated lightweight visual recognition and real-time path planning framework. We introduce MPD-YOLO, an optimized model based on YOLOv11n, incorporating (1) a Multi-scale Information Enhancement Module (MSEE) to boost feature extraction; (2) structured pruning for significant model compression (final size: 2.1 MB, 39.6% of original); and (3) knowledge distillation to recover accuracy loss post-pruning. The resulting model achieves high precision (P: 89.8%, mAP@0.5: 95.1%) with reduced computational load (3.2 GFLOPs) while demonstrating enhanced robustness in challenging scenarios—recall significantly increased by 6.8% versus YOLOv11n. Leveraging these recognition outputs, an adaptive ant colony algorithm featuring dynamic parameter adjustment and an improved pheromone strategy reduces average path planning time to 2.2 s—a 68.6% speedup over benchmark methods. This integrated approach significantly enhances perception accuracy and operational efficiency for automated marigold harvesting in unstructured environments, providing robust technical support for continuous automated operations. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation: 3rd Edition)
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