Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation

Topic Information

Dear Colleagues,

At present, the new unmanned vehicles technology and embodied intelligence systems for intelligent transportation are in a period of change. In the foreseeable near future, unmanned systems represented by UGV (Unmanned Ground Vehicle) and UAV (Unmanned Aerial Vehicle) will build new ground and air transportation, logistics, and operation systems, which will have great application potential in various fields of industry and agriculture. Unmanned driving systems (on the open road and the closed road) and intelligent agricultural machinery and equipment are representative intelligent transportation applications. 'Interactive' perception, 'learnable' cognition and decision making, and 'self-growth' behavior control are three important features of embodied intelligence. Correspondingly, multi-sensor (Lidar, millimeter wave radar, and optical sensor) and multi-source information fusion technology, SLAM technology, and bionic vision technology are applied to the perception stage. Brain-imitating intelligence and end-to-end deep learning neural networks are applied to the cognition and decision-making stage. Disturbance self-rejection control, integration control, bionic formation control, and manned/unmanned hybrid cooperative control technology are applied to the behavior control stage.

The scope of solicitation includes, but is not limited to, the following:

• Automatic driving, intelligent driving, and unmanned driving; embodied intelligence;
• Perception, cognition, and behavior;
• SLAM (Simultaneous Localization and Mapping);
• Lidar, millimeter-wave radar, RGB and RGB-D machine vision perception, and multi-spectral optical perception; 'interactive' perception;
• 'Learnable' cognition and decision making;
• 'Self-growth' behavior control; biologically inspired visual perception;
• Multi-sensor and multi-source information fusion;
• Brain-imitating intelligence and end-to-end deep learning neural networks;
• Disturbance observer and active disturbance rejection control;
• Perception, decision-making and control integration technology;
• Biologically inspired formation control;
• Hybrid cooperative control of manned/unmanned systems.

Dr. Jian Chen
Dr. Min Xia
Dr. Hui Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.2	4.0	2014	22.9 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Drones drones	4.8	7.4	2017	20.8 Days	CHF 2600	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Eng eng	2.4	3.2	2020	18 Days	CHF 1400	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

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Published Papers (24 papers)

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22 pages, 2995 KB  

Open AccessArticle

Energy-Efficient Distributed AUV Swarm for Target Tracking via LSTM-Assisted Offline-to-Online Reinforcement Learning

by Renbo Li, Denghui Li, Xiangxin Zhang and Weiming Ni

Drones 2026, 10(3), 158; https://doi.org/10.3390/drones10030158 - 26 Feb 2026

Viewed by 235

Abstract

In recent years, autonomous underwater vehicles (AUVs) have been increasingly employed for target surveillance and tracking. However, the limited performance and information-processing capability of a single AUV make it difficult to achieve high-precision tracking in practice. To address these challenges, this paper proposes [...] Read more.

In recent years, autonomous underwater vehicles (AUVs) have been increasingly employed for target surveillance and tracking. However, the limited performance and information-processing capability of a single AUV make it difficult to achieve high-precision tracking in practice. To address these challenges, this paper proposes an online-to-offline multi-agent reinforcement learning (MARL) framework that employs offline training on historical data to obtain the expert policy. Then, the optimal policy is generated by online fine-tuning technology, which enhances the training efficiency of reinforcement learning in new scenarios. To expand the surveillance range of AUV swarms, a distributed cooperative strategy based on area information entropy (AIE) is introduced. To reduce energy consumption in complex marine environments containing obstacles and vortices, ocean current and energy consumption models are introduced, together with an energy-efficiency optimization strategy. Furthermore, a long short-term memory (LSTM) network is integrated into the offline-to-online MARL framework to predict time-varying environmental states, thereby improving tracking accuracy and energy efficiency. Experimental results show that the proposed scheme is superior to the baseline schemes in terms of energy consumption, task success rate, and distance between AUVs. In addition, various performance indicators of the extended AUV swarm are also superior to the baseline schemes, demonstrating that the proposed scheme has excellent performance and scalability. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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25 pages, 5101 KB  

Open AccessArticle

Embodied Visual Perception for Driver Fatigue Monitoring Systems: A Hierarchical Decoupling Framework for Robust Fatigue Detection and Scenario Understanding

by Siyu Chen, Juhua Huang, Yinyin Liu, Saier Ye and Yuqi Bai

Electronics 2026, 15(3), 689; https://doi.org/10.3390/electronics15030689 - 5 Feb 2026

Viewed by 267

Abstract

As intelligent vehicle technologies evolve, reliable driver monitoring systems have become increasingly critical for ensuring the safety of human drivers and operational reliability. This paper proposes a novel visual computing framework for Driver Fatigue Monitoring Systems (DFMSs) based on hierarchical decoupling and scenario [...] Read more.

As intelligent vehicle technologies evolve, reliable driver monitoring systems have become increasingly critical for ensuring the safety of human drivers and operational reliability. This paper proposes a novel visual computing framework for Driver Fatigue Monitoring Systems (DFMSs) based on hierarchical decoupling and scenario element analysis, specifically designed for intelligent transportation environments. By treating the monitoring system as an engineering-level embodied perception–decision system deployed within the vehicle, rather than a purely disembodied vision module, the framework decouples low-level algorithmic perception from application-layer decision logic, enabling a more granular evaluation of visual computing performance in real-world scenarios. We leverage Python 3.9-driven automated test case generation to simulate diverse environmental variables, improving testing efficiency by 50% over traditional manual methods. The system utilizes deep learning-based visual computing to achieve high-fidelity monitoring of eye closure (PERCLOS, EAR), yawning (MAR), and head pose dynamics, enabling real-time assessment of the driver’s state within the embodied system loop. Comparative benchmarking reveals that our framework significantly outperforms existing models in visual understanding accuracy, achieving perfect confidence scores (1.000) for eye closure and smoking behavior detection, while drastically reducing false positives in mobile phone usage detection (misidentification rate: 0.016 vs. 0.805). These results demonstrate that an embodied approach to visual perception enhances the robustness and reliability of driver monitoring systems deployed in real vehicles, providing a scalable pathway for the development of next-generation intelligent transportation safety standards. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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35 pages, 580 KB  

Open AccessReview

Conflict Detection, Resolution, and Collision Avoidance for Decentralized UAV Autonomy: Classical Methods and AI Integration

by Francesco d’Apolito, Phillipp Fanta-Jende, Verena Widhalm and Christoph Sulzbachner

Aerospace 2026, 13(2), 113; https://doi.org/10.3390/aerospace13020113 - 23 Jan 2026

Viewed by 410

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains. Many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence [...] Read more.

Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains. Many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence of uncooperative airspace users, and rising traffic density are increasing research interest toward decentralized concepts such as free flight, in which each actor is responsible for its own safe trajectory. This survey reviews CD&R and CA methods with a particular focus on decentralized automation. It analyzes qualitatively classical rule-based approaches and their limitations, then examines machine learning (ML)-based techniques that aim to improve adaptability in complex environments. Building on recent regulatory discussions, it further considers how requirements for trust, transparency, explainability, and interpretability evolve with the degree of human oversight and autonomy, addressing gaps left by prior surveys. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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20 pages, 5778 KB  

Open AccessArticle

DTD: Density Triangle Descriptor for 3D LiDAR Loop Closure Detection

by Kaiwei Tang, Qing Wang, Chao Yan, Yang Sun and Shengyi Liu

Sensors 2026, 26(1), 201; https://doi.org/10.3390/s26010201 - 27 Dec 2025

Viewed by 634

Abstract

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this [...] Read more.

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this paper introduces a Density Triangle Descriptor (DTD). The proposed method first extracts keypoints from density images generated from LiDAR point clouds, and then constructs a triangle-based global descriptor that is invariant to rotation and translation, enabling robust structural representation. Furthermore, to enhance local discriminative ability, the neighborhood around each keypoint is modeled as a Gaussian distribution, and a local descriptor is derived from the entropy of its probability distribution. During loop closure detection, candidate matches are first retrieved via hash indexing of triangle edge lengths, followed by entropy-based local verification, and are finally refined by singular value decomposition for accurate pose estimation. Extensive experiments on multiple public datasets demonstrate that compared to STD, the proposed DTD improves the average F1 max score and EP by 18.30% and 20.08%, respectively, while achieving a 50.57% improvement in computational efficiency. Moreover, DTD generalizes well to solid-state LiDAR with non-repetitive scanning patterns, validating its robustness and applicability in complex environments. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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23 pages, 6012 KB  

Open AccessArticle

A Pseudo-Point-Based Adaptive Fusion Network for Multi-Modal 3D Detection

by Chenghong Zhang, Wei Wang, Bo Yu and Hanting Wei

Electronics 2026, 15(1), 59; https://doi.org/10.3390/electronics15010059 - 23 Dec 2025

Viewed by 295

Abstract

A 3D multi-modal detection method using a monocular camera and LiDAR has drawn much attention due to its low cost and strong applicability, making it highly valuable for autonomous driving and unmanned aerial vehicles (UAVs). However, conventional fusion approaches relying on static arithmetic [...] Read more.

A 3D multi-modal detection method using a monocular camera and LiDAR has drawn much attention due to its low cost and strong applicability, making it highly valuable for autonomous driving and unmanned aerial vehicles (UAVs). However, conventional fusion approaches relying on static arithmetic operations often fail to adapt to dynamic, complex scenarios. Furthermore, existing ROI alignment techniques, such as local projection and cross-attention, are inadequate for mitigating the feature misalignment triggered by depth estimation noise in pseudo-point clouds. To address these issues, this paper proposes a pseudo-point-based 3D object detection method that achieves biased fusion of multi-modal data. First, a meta-weight fusion module dynamically generates fusion weights based on global context, adaptively balancing the contributions of point clouds and images. Second, a module combining bidirectional cross-attention and a gating filter mechanism is introduced to eliminate the ROI feature misalignment caused by depth completion noise. Finally, a class-agnostic box fusion strategy is introduced to aggregate highly overlapping detection boxes at the decision level, improving localization accuracy. Experiments on the KITTI dataset show that the proposed method achieves APs of 92.22%, 85.03%, and 82.25% on Easy, Moderate, and Hard difficulty levels, respectively, demonstrating leading performance. Ablation studies further validate the effectiveness and computational efficiency of each module. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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24 pages, 11407 KB  

Open AccessArticle

An Autonomous UAV Power Inspection Framework with Vision-Based Waypoint Generation

by Qi Wang, Zixuan Zhang and Wei Wang

Appl. Sci. 2026, 16(1), 76; https://doi.org/10.3390/app16010076 - 21 Dec 2025

Viewed by 421

Abstract

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, it plays an increasingly important role in electrical power inspection. Automated approaches that generate inspection waypoints based on tower features have emerged in recent years. However, these solutions commonly rely on tower coordinates, [...] Read more.

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, it plays an increasingly important role in electrical power inspection. Automated approaches that generate inspection waypoints based on tower features have emerged in recent years. However, these solutions commonly rely on tower coordinates, which can be difficult to obtain at times. To address this issue, this study presents an autonomous inspection waypoint generation method based on object detection. The main contributions are as follows: (1) After acquiring and constructing the distribution tower dataset, we propose a lightweight object detector based on You Only Look Once (YOLOv8). The model integrates the Generalized Efficient Layer Aggregation Network (GELAN) module in the backbone to reduce model parameters and incorporates Powerful Intersection over Union (PIoU) to enhance the accuracy of bounding box regression. (2) Based on detection results, a three-stage waypoint generator is designed: Stage 1 estimates the initial tower’s coordinates and altitude; Stage 2 refines these estimates; and Stage 3 determines the positions of subsequent towers. The generator ultimately provides the target’s position and heading information, enabling the UAV to perform inspection maneuvers. Compared to classic models, the proposed model runs at 56 Frames Per Second (FPS) and achieves an approximate 2.1% improvement in mAP50:95. In addition, the proposed waypoint estimator achieves tower position estimation errors within 0.8 m and azimuth angle errors within 0.01 rad. Multiple consecutive tower inspection flights in actual environments further validate the effectiveness of the proposed method. The proposed method’s effectiveness is validated through actual flight tests involving multiple consecutive distribution towers. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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21 pages, 10825 KB  

Open AccessArticle

Vehicle–Road–Cloud Collaborative Perception: Resource and Intelligence Optimization

by Liang Xin, Guangtao Zhou, Zhaoyang Yu, Hong Zhu, Xiaolong Feng, Quan Yuan and Jinglin Li

Appl. Sci. 2025, 15(23), 12613; https://doi.org/10.3390/app152312613 - 28 Nov 2025

Viewed by 792

Abstract

Vehicle–road–cloud collaborative perception improves perception performance via multi-agent information sharing and data fusion, but it faces coupled trade-offs among perception accuracy, computing resources, and communication bandwidth. Optimizing agents’ intelligence or underlaying resources alone fails to resolve this conflict, limiting collaboration efficiency. We propose [...] Read more.

Vehicle–road–cloud collaborative perception improves perception performance via multi-agent information sharing and data fusion, but it faces coupled trade-offs among perception accuracy, computing resources, and communication bandwidth. Optimizing agents’ intelligence or underlaying resources alone fails to resolve this conflict, limiting collaboration efficiency. We propose C⁴I-JO, a joint resource and intelligence optimization method for vehicle–road–cloud collaborative perception. We employ slimmable networks to achieve intelligent elasticity. Based on these, C⁴I-JO jointly optimizes four key dimensions to minimize resource consumption while meeting accuracy and latency constraints, including collaborative mechanisms to cut redundant communication, resource allocation to avoid supply–demand bottlenecks, intelligent elasticity to balance accuracy and resources, and computation offloading to reduce local burden. We propose a two-layer iterative decoupling algorithm that addresses the optimization problem. Specifically, the outer level leverages Second-Order Cone Programming (SOCP) and the interior-point method, while the inner level utilizes a Genetic Algorithm (GA). Simulations show that C⁴I-JO outperforms baselines in both resource efficiency and perception quality. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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29 pages, 5406 KB  

Open AccessArticle

An Efficient 3D Multi-Object Tracking Algorithm for Low-Cost UGV Using Multi-Level Data Association

by Xiaochun Yang, Anmin Huang, Jin Lou, Junhua Gou, Wenxing Fu and Jie Yan

Drones 2025, 9(11), 747; https://doi.org/10.3390/drones9110747 - 28 Oct 2025

Cited by 1 | Viewed by 1183

Abstract

3D object detection and tracking technology are increasingly being adopted in unmanned ground vehicles, as robust perception systems significantly improve the obstacle avoidance performance of a UGV. However, most existing algorithms depend heavily on computationally intensive point cloud neural networks, rendering them unsuitable [...] Read more.

3D object detection and tracking technology are increasingly being adopted in unmanned ground vehicles, as robust perception systems significantly improve the obstacle avoidance performance of a UGV. However, most existing algorithms depend heavily on computationally intensive point cloud neural networks, rendering them unsuitable for resource-constrained platforms. In this work, we propose an efficient 3D object detection and tracking method specially designed for deployment on low-cost vehicle platforms. For the detection phase, our method integrates an image-based 2D detector with data fusion techniques to coarsely extract object point clouds, followed by an unsupervised learning approach to isolate objects from noisy point cloud data. For the tracking process, we propose a multi-target tracking algorithm based on multi-level data association. This method introduces an additional data association step to handle targets that fail in 3D detection, thereby effectively reducing the impact of detection errors on tracking performance. Moreover, our method enhances association precision between detection outputs and existing trajectories through the integration of 2D and 3D information, thereby further mitigating the adverse effects of detection inaccuracies. By adopting unsupervised learning as an alternative to complex neural networks, our approach demonstrates strong compatibility with both low-resolution LiDAR and GPU-free computing platforms. Experiments on the KITTI benchmark demonstrate that our tracking framework achieves significant computational efficiency gains while maintaining detection accuracy. Furthermore, experimental evaluations on the real-world UGV platform demonstrated the deployment feasibility of our approach. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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24 pages, 5571 KB  

Open AccessArticle

Deep Learning for Predicting Surface Elevation Change in Tailings Storage Facilities from UAV-Derived DEMs

by Wang Lu, Roohollah Shirani Faradonbeh, Hui Xie and Phillip Stothard

Appl. Sci. 2025, 15(20), 10982; https://doi.org/10.3390/app152010982 - 13 Oct 2025

Viewed by 936

Abstract

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition [...] Read more.

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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37 pages, 3151 KB  

Open AccessReview

Systematic Review of Multi-Objective UAV Swarm Mission Planning Systems from Regulatory Perspective

by Luke Checker, Hui Xie, Siavash Khaksar and Iain Murray

Drones 2025, 9(7), 509; https://doi.org/10.3390/drones9070509 - 20 Jul 2025

Cited by 1 | Viewed by 6725

Abstract

Advancements in Unmanned Aerial Vehicle (UAV) technologies have increased exponentially in recent years, with UAV swarm being a key area of interest. UAV swarm overcomes the energy reserve, payload, and single-objective limitations of single UAVs, enabling broader mission scopes. Despite these advantages, UAV [...] Read more.

Advancements in Unmanned Aerial Vehicle (UAV) technologies have increased exponentially in recent years, with UAV swarm being a key area of interest. UAV swarm overcomes the energy reserve, payload, and single-objective limitations of single UAVs, enabling broader mission scopes. Despite these advantages, UAV swarm has yet to see widespread application within global industry. A leading factor hindering swarm application within industry is the divide that currently exists between the functional capacity of modern UAV swarm systems and the functionality required by legislation. This paper investigates this divide through an overview of global legislative practice, contextualized via a case study of Australia’s UAV regulatory environment. The overview highlighted legislative objectives that coincided with open challenges in the UAV swarm literature. These objectives were then formulated into analysis criteria that assessed whether systems presented sufficient functionality to address legislative concern. A systematic review methodology was used to apply analysis criteria to multi-objective UAV swarm mission planning systems. Analysis focused on multi-objective mission planning systems due to their role in defining the functional capacity of UAV swarms within complex real-world operational environments. This, alongside the popularity of these systems within the modern literature, makes them ideal candidates for defining new enabling technologies that could address the identified areas of weakness. The results of this review highlighted several legislative considerations that remain under-addressed by existing technologies. These findings guided the proposal of enabling technologies to bridge the divide between functional capacity and legislative concern. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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27 pages, 12374 KB  

Open AccessArticle

A Novel Neural Network-Based Adaptive Formation Control for Cooperative Transportation of an Underwater Payload Using a Fleet of UUVs

by Wen Pang, Daqi Zhu, Mingzhi Chen, Wentao Xu and Bin Wang

Drones 2025, 9(7), 465; https://doi.org/10.3390/drones9070465 - 30 Jun 2025

Viewed by 1374

Abstract

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a [...] Read more.

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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23 pages, 12949 KB  

Open AccessArticle

A Grid-Based Hierarchical Representation Method for Large-Scale Scenes Based on Three-Dimensional Gaussian Splatting

by Yuzheng Guan, Zhao Wang, Shusheng Zhang, Jiakuan Han, Wei Wang, Shengli Wang, Yihu Zhu, Yan Lv, Wei Zhou and Jiangfeng She

Remote Sens. 2025, 17(10), 1801; https://doi.org/10.3390/rs17101801 - 21 May 2025

Cited by 1 | Viewed by 2592

Abstract

Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this [...] Read more.

Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this paper proposes a novel grid-based scene-segmentation technique for the process of reconstruction. Sparse point clouds, acting as an indirect input for 3DGS, are first processed by Z-Score and a percentile-based filter to prepare the pure scene for segmentation. Then, through grid creation, grid partitioning, and grid merging, rational and widely applicable sub-grids and sub-scenes are formed for training. This is followed by integrating Hierarchy-GS’s LOD strategy. This method achieves better large-scale reconstruction effects within limited computational resources. Experiments on multiple datasets show that this method matches others in single-block reconstruction and excels in complete scene reconstruction, achieving superior results in PSNR, LPIPS, SSIM, and visualization quality. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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22 pages, 23754 KB  

Open AccessArticle

A Low-Latency Dynamic Object Detection Algorithm Fusing Depth and Events

by Duowen Chen, Liqi Zhou and Chi Guo

Drones 2025, 9(3), 211; https://doi.org/10.3390/drones9030211 - 15 Mar 2025

Cited by 1 | Viewed by 1602

Abstract

Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training [...] Read more.

Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training and inference on high-performance graphics cards. These cards are not only bulky and power-hungry but also challenging to deploy on compact robotic platforms. Fortunately, the emergence of event cameras, inspired by biological vision, provides a promising solution to these limitations. These cameras offer low latency, minimal motion blur, and non-redundant outputs, making them well suited for dynamic obstacle detection. Building on these advantages, a novel methodology was developed through the fusion of events with depth to address the challenge of dynamic object detection. Initially, an adaptive temporal sampling window was implemented to selectively acquire event data and supplementary information, contingent upon the presence of objects within the visual field. Subsequently, a warping transformation was applied to the event data, effectively eliminating artifacts induced by ego-motion while preserving signals originating from moving objects. Following this preprocessing stage, the transformed event data were converted into an event queue representation, upon which denoising operations were performed. Ultimately, object detection was achieved through the application of image moment analysis to the processed event queue representation. The experimental results show that, compared with the current state-of-the-art methods, the proposed method has improved the detection speed by approximately 20% and the accuracy by approximately 5%. To substantiate real-world applicability, the authors implemented a complete obstacle avoidance pipeline, integrating our detector with planning modules and successfully deploying it on a custom-built quadrotor platform. Field tests confirm reliable avoidance of an obstacle approaching at approximately 8 m/s, thereby validating practical deployment potential. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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19 pages, 3025 KB  

Open AccessArticle

Two-Step Robust Fault-Tolerant Controller Design Based on Nonlinear Extended State Observer (NESO) for Unmanned Aerial Vehicles (UAVs) with Actuator Faults and Disturbances

by Wei Wang, Yiming Chen, Zhang Ren and Huanhua Liu

Drones 2025, 9(3), 183; https://doi.org/10.3390/drones9030183 - 1 Mar 2025

Cited by 7 | Viewed by 1554

Abstract

This paper presents a two-step robust fault-tolerant controller of incorporating disturbances and actuator faults rejection for a UAV flight control system, which is challenging due to its complex and nonlinear dynamics. First, the main controller, which is designed separately, considers all the design [...] Read more.

This paper presents a two-step robust fault-tolerant controller of incorporating disturbances and actuator faults rejection for a UAV flight control system, which is challenging due to its complex and nonlinear dynamics. First, the main controller, which is designed separately, considers all the design parameters giving the desired closed loop system response. Second, a method to design a standalone disturbance/fault compensator is suggested, which is integrated into the original system to ensure stability. The degraded system stability and performance are compensated by the compensator, which comes into effect only after the disturbance/fault residual error increases to a certain level. The disturbance/fault compensator is designed based on a nonlinear extended state observer (NESO), which cannot only estimate the system’s states but also the unknown disturbances and fault. In the faultless system, only the main controller is active. When an actuator fault/disturbance occurs, the compensator is automatically activated. This controller scheme solves the traditional control conflict between high performance and robustness. It also guarantees the stability of the system in the presence of the disturbances/faults. A civil fixed-wing unmanned aerial vehicle (UAV) that is equipped with a thrust vector control (TVC) with actuator faults and disturbance is chosen for the simulations, and the results prove the efficacy of the new approach. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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22 pages, 13198 KB  

Open AccessArticle

UAV Localization in Urban Area Mobility Environment Based on Monocular VSLAM with Deep Learning

by Mutagisha Norbelt, Xiling Luo, Jinping Sun and Uwimana Claude

Drones 2025, 9(3), 171; https://doi.org/10.3390/drones9030171 - 26 Feb 2025

Cited by 9 | Viewed by 2909

Abstract

Unmanned Aerial Vehicles (UAVs) play a major role in different applications, including surveillance, mapping, and disaster relief, particularly in urban environments. This paper presents a comprehensive framework for UAV localization in outdoor environments using monocular ORB-SLAM3 integrated with optical flow and YOLOv5 for [...] Read more.

Unmanned Aerial Vehicles (UAVs) play a major role in different applications, including surveillance, mapping, and disaster relief, particularly in urban environments. This paper presents a comprehensive framework for UAV localization in outdoor environments using monocular ORB-SLAM3 integrated with optical flow and YOLOv5 for enhanced performance. The proposed system addresses the challenges of accurate localization in dynamic outdoor environments where traditional GPS methods may falter. By leveraging the capabilities of ORB-SLAM3, the UAV can effectively map its environment while simultaneously tracking its position using visual information from a single camera. The integration of optical flow techniques allows for accurate motion estimation between consecutive frames, which is critical for maintaining accurate localization amidst dynamic changes in the environment. YOLOv5 is a highly efficient model utilized for real-time object detection, enabling the system to identify and classify dynamic objects within the UAV’s field of view. This dual approach of using both optical flow and deep learning enhances the robustness of the localization process by filtering out dynamic features that could otherwise cause mapping errors. Experimental results show that the combination of monocular ORB-SLAM3, optical flow, and YOLOv5 significantly improves localization accuracy and reduces trajectory errors compared to traditional methods. In terms of absolute trajectory error and average tracking time, the suggested approach performs better than ORB-SLAM3 and DynaSLAM. For real-time SLAM applications in dynamic situations, our technique is especially well-suited due to its potential to achieve lower latency and greater accuracy. These improvements guarantee more dependable performance in a variety of scenarios in addition to increasing overall efficiency. The framework effectively distinguishes between static and dynamic elements, allowing for more reliable map construction and navigation. The results show that our proposed method (U-SLAM) produces a considerable decrease of up to 43.47% in APE and 26.47% RPE in S000, and its accuracy is higher for sequences with moving objects and more motion inside the image. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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20 pages, 907 KB  

Open AccessArticle

Task-Offloading Optimization Using a Genetic Algorithm in Hybrid Fog Computing for the Internet of Drones

by Mohamed Amine Attalah, Sofiane Zaidi, Naçima Mellal and Carlos T. Calafate

Sensors 2025, 25(5), 1383; https://doi.org/10.3390/s25051383 - 24 Feb 2025

Cited by 6 | Viewed by 2144

Abstract

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, [...] Read more.

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge). Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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17 pages, 25856 KB  

Open AccessArticle

An Independent UAV-Based Mobile Base Station

by Sung-Chan Choi and Sung-Yeon Kim

Sensors 2025, 25(5), 1349; https://doi.org/10.3390/s25051349 - 22 Feb 2025

Cited by 5 | Viewed by 3159

Abstract

In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes [...] Read more.

In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes PS-LTE functionalities, mission-critical push-to-talk, proximity-based services, and isolated E-UTRAN operation to ensure the reliable and secure communication for emergency services. We provide a simulation result to achieve the radio coverage of mobile base station. By using this radio coverage, we find an appropriate location of the end device for performing the outdoor experiments. We develop a prototype of a proposed mobile base station and test its operation in an outdoor environment. The experimental results provide a sufficient data rate to make an independent mobile base station to restore communication infrastructure in areas that experienced environmental disasters. This prototype and experimental results offer a significant step forward in creating agile and efficient communication solutions for emergency scenarios. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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26 pages, 3057 KB  

Open AccessReview

Multi-Dimensional Research and Progress in Parking Space Detection Techniques

by Xi Wang, Haotian Miao, Jiaxin Liang, Kai Li, Jianheng Tan, Rui Luo and Yueqiu Jiang

Electronics 2025, 14(4), 748; https://doi.org/10.3390/electronics14040748 - 14 Feb 2025

Cited by 6 | Viewed by 5131

Abstract

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or [...] Read more.

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or parking management systems in real time, which has a significant impact on improving urban parking efficiency, alleviating traffic congestion, optimizing driving experience, and promoting the development of intelligent transportation systems. This paper firstly describes the research significance of parking space detection technology and its research background, and then systematically reviews different types of parking spaces and detection technologies, covering a variety of technical means such as ultrasonic sensors, infrared sensors, magnetic sensors, other sensors, methods based on traditional computer vision, and methods based on deep learning. At the end of the paper, the article summarizes the current research progress in parking space detection technology, analyzes the existing challenges, and provides an outlook on future research directions. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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28 pages, 4431 KB  

Open AccessArticle

Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints

by Yongxing Cao, Bijun Li, Zejian Deng and Xiaomin Guo

Electronics 2024, 13(24), 5041; https://doi.org/10.3390/electronics13245041 - 22 Dec 2024

Cited by 4 | Viewed by 2776

Abstract

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease [...] Read more.

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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16 pages, 2350 KB  

Open AccessArticle

Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

by Shaoxiong Guo, Li Zhou, Shijie Liang, Kuo Cao and Zhiqun Song

Aerospace 2024, 11(12), 969; https://doi.org/10.3390/aerospace11120969 - 25 Nov 2024

Cited by 2 | Viewed by 1611

Abstract

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the [...] Read more.

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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21 pages, 11869 KB  

Open AccessArticle

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

by Linghang Meng, Hongyang Zhang, Yifei Zhao and Kin Huat Low

Drones 2024, 8(11), 624; https://doi.org/10.3390/drones8110624 - 30 Oct 2024

Cited by 3 | Viewed by 2564

Abstract

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing [...] Read more.

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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17 pages, 5286 KB  

Open AccessArticle

U-Space Contingency Management Based on Enhanced Mission Description

by Jose L. Munoz-Gamarra, Juan J. Ramos and Zhiqiang Liu

Aerospace 2024, 11(11), 876; https://doi.org/10.3390/aerospace11110876 - 24 Oct 2024

Cited by 1 | Viewed by 1494

Abstract

Loss of communication, low battery, or bad weather conditions (like high-speed wind) are currently managed by performing a return to launch (RTL) point maneuver. However, the execution of this procedure can pose a safety threat since it has not been considered within the [...] Read more.

Loss of communication, low battery, or bad weather conditions (like high-speed wind) are currently managed by performing a return to launch (RTL) point maneuver. However, the execution of this procedure can pose a safety threat since it has not been considered within the mission planning process. This work proposes an advanced management of contingency events based on the integration of a new U-space service that enhances mission description. The proposed new service, deeply linked to demand capacity balance and strategic deconfliction services, assigns alternative safe landing spots by analyzing the planned mission. Two potential solutions are characterized (distinguished primarily by the number of contingency vertiports assigned): contingency management based on the assignment of a single alternative vertiport to each mission (static) or the allocation of a set of different contingency vertiports that are valid during certain time intervals. It is proven that this enhanced mission planning could ensure that U-space volumes operate in an ultra-safe system conditions while facing these unforeseen events, highlighting its importance in high-risk scenarios like urban air mobility deployments. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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19 pages, 11959 KB  

Open AccessArticle

Learning Autonomous Navigation in Unmapped and Unknown Environments

by Naifeng He, Zhong Yang, Chunguang Bu, Xiaoliang Fan, Jiying Wu, Yaoyu Sui and Wenqiang Que

Sensors 2024, 24(18), 5925; https://doi.org/10.3390/s24185925 - 12 Sep 2024

Cited by 5 | Viewed by 3141

Abstract

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, [...] Read more.

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent’s exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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37 pages, 10534 KB  

Open AccessArticle

Optimization of Urban Target Area Accessibility for Multi-UAV Data Gathering Based on Deep Reinforcement Learning

by Zhengmiao Jin, Renxiang Chen, Ke Wu, Tengwei Yu and Linghua Fu

Drones 2024, 8(9), 462; https://doi.org/10.3390/drones8090462 - 5 Sep 2024

Cited by 1 | Viewed by 1801

Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

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