Artificial Intelligence in Navigation

Topic Information

Dear Colleagues,

Navigation is the science and technology of accurately determining the position and velocity of an airborne, land, or marine vehicle relative to a known reference, wherein the planning and execution of the maneuvers necessary to move between desired locations are analyzed. Artificial intelligence (AI) is a wide-ranging branch of computer science concerning the design of machines and software capable of performing tasks associated with intelligent beings, which encompasses machine learning, expert systems, knowledge engineering, and language processing. Due to the accelerating development and widespread prevalence of AI, navigation can effectively be supported by newly established AI technologies. This Topic is open to the entire spectrum of basic and applied scientific research, novel practical applications, and innovations. It also promotes the dialogue between governmental and private actors or institutions. Contributions to navigation theory, systems design, interoperability, or social impact analyses are similarly welcome.

Topics include, but are not limited to, the following:

• Marine, land, and airborne navigation
• Positioning/localization, route planning, and guidance
• Vehicle control
• VR/AR technologies, computer vision, and image processing/analysis/understanding
• Voice/speech recognition, language processing, and machine translation
• Methodologies, frameworks, and models in artificial intelligence
• Machine learning, deep learning, neural networks, fuzzy systems, evolutionary computation, and agent-based systems
• Knowledge representation and reasoning
• Information systems, maps, databases, and open data sources
• Robotic systems and autonomous vehicles
• Commercial, military, intelligence, education, and research applications
• Security and privacy issues and ethical questions
• Best practices and use cases.

Prof. Dr. Arpad Barsi
Prof. Dr. Eliseo Clementini
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400
ISPRS International Journal of Geo-Information ijgi	3.4	6.2	2012	35.5 Days	CHF 1700
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Robotics robotics	3.7	5.9	2012	17.3 Days	CHF 1800
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600

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Published Papers (16 papers)

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19 pages, 899 KiB  

Open AccessArticle

5G Indoor Positioning Error Correction Based on 5G-PECNN

by Shan Yang, Qiyuan Zhang, Longxing Hu, Haina Ye, Xiaobo Wang, Ti Wang and Syuan Liu

Sensors 2024, 24(6), 1949; https://doi.org/10.3390/s24061949 - 19 Mar 2024

Viewed by 645

Abstract

With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is [...] Read more.

With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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29 pages, 33317 KiB  

Open AccessArticle

Method for the Identification and Classification of Zones with Vehicular Congestion

by Gary Reyes, Roberto Tolozano-Benites, Laura Lanzarini, César Estrebou, Aurelio F. Bariviera and Julio Barzola-Monteses

ISPRS Int. J. Geo-Inf. 2024, 13(3), 73; https://doi.org/10.3390/ijgi13030073 - 28 Feb 2024

Viewed by 1107

Abstract

Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on [...] Read more.

Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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17 pages, 7209 KiB  

Open AccessArticle

MixedSCNet: LiDAR-Based Place Recognition Using Multi-Channel Scan Context Neural Network

by Yan Si, Wenyi Han, Die Yu, Baizhong Bao, Jian Duan, Xiaobin Zhan and Tielin Shi

Electronics 2024, 13(2), 406; https://doi.org/10.3390/electronics13020406 - 18 Jan 2024

Viewed by 851

Abstract

In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while [...] Read more.

In the realm of LiDAR-based place recognition tasks, three predominant methodologies have emerged: manually crafted feature descriptor-based methods, deep learning-based methods, and hybrid methods that combine the former two. Manually crafted feature descriptors often falter in reverse visits and confined indoor environments, while deep learning-based methods exhibit limitations in terms of generalization to distinct data domains. Hybrid methods tend to fix these problems, albeit at the cost of an expensive computational burden. In response to this, this paper introduces MixedSCNet, a novel hybrid approach designed to harness the strengths of manually crafted feature descriptors and deep learning models while keeping a relatively low computing overhead. MixedSCNet starts with constructing a BEV descriptor called MixedSC, which takes height, intensity, and smoothness into consideration simultaneously, thus offering a more comprehensive representation of the point cloud. Subsequently, MixedSC is fed into a compact Convolutional Neural Network (CNN), which further extracts high-level features, ultimately yielding a discriminative global point cloud descriptor. This descriptor is then employed for place retrieval, effectively bridging the gap between manually crafted feature descriptors and deep learning models. To substantiate the efficacy of this amalgamation, we undertake an extensive array of experiments on the KITTI and NCLT datasets. Results show that MixedSCNet stands out as the sole method showcasing state-of-the-art performance across both datasets, outperforming the other five methods while maintaining a relatively short runtime. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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24 pages, 21652 KiB  

Open AccessArticle

A Multi-Source-Data-Assisted AUV for Path Cruising: An Energy-Efficient DDPG Approach

by Tianyu Xing, Xiaohao Wang, Kaiyang Ding, Kai Ni and Qian Zhou

Remote Sens. 2023, 15(23), 5607; https://doi.org/10.3390/rs15235607 - 02 Dec 2023

Viewed by 940

Abstract

As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates [...] Read more.

As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates remote sensing information to overcome traditional APF challenges and combines the IAPF with the traveling salesman problem for optimal path cruising. Through a combination of DRL and multi-source data optimization, the approach ensures minimal energy consumption across all target points. Inertial sensors further refine trajectory, ensuring smooth navigation and precise positioning. The comparative experiments confirm the method’s energy efficiency, trajectory refinement, and safety excellence. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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16 pages, 4228 KiB  

Open AccessArticle

Path Planning for Automatic Berthing Using Ship-Maneuvering Simulation-Based Deep Reinforcement Learning

by Anh Khoa Vo, Thi Loan Mai and Hyeon Kyu Yoon

Appl. Sci. 2023, 13(23), 12731; https://doi.org/10.3390/app132312731 - 27 Nov 2023

Cited by 2 | Viewed by 887

Abstract

Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potential risks involved. Many algorithms [...] Read more.

Despite receiving much attention from researchers in the field of naval architecture and marine engineering since the early stages of modern shipbuilding, the berthing phase is still one of the biggest challenges in ship maneuvering due to the potential risks involved. Many algorithms have been proposed to solve this problem. This paper proposes a new approach with a path-planning algorithm for automatic berthing tasks using deep reinforcement learning (RL) based on a maneuvering simulation. Unlike the conventional path-planning algorithm using the control theory or an advanced algorithm using deep learning, a state-of-the-art path-planning algorithm based on reinforcement learning automatically learns, explores, and optimizes the path for berthing performance through trial and error. The results of performing the twin delayed deep deterministic policy gradient (TD3) combined with the maneuvering simulation show that the approach can be used to propose a feasible and safe path for high-performing automatic berthing tasks. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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19 pages, 3947 KiB  

Open AccessTechnical Note

A Semantics-Guided Visual Simultaneous Localization and Mapping with U-Net for Complex Dynamic Indoor Environments

by Zhi Zeng, Hui Lin, Zhizhong Kang, Xiaokui Xie, Juntao Yang, Chuyu Li and Longze Zhu

Remote Sens. 2023, 15(23), 5479; https://doi.org/10.3390/rs15235479 - 23 Nov 2023

Viewed by 868

Abstract

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed [...] Read more.

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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27 pages, 6787 KiB  

Open AccessArticle

A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors

by Sumet Darapisut, Komate Amphawan, Nutthanon Leelathakul and Sunisa Rimcharoen

ISPRS Int. J. Geo-Inf. 2023, 12(10), 431; https://doi.org/10.3390/ijgi12100431 - 22 Oct 2023

Cited by 1 | Viewed by 1540

Abstract

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need [...] Read more.

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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17 pages, 10552 KiB  

Open AccessArticle

Scaling-Invariant Max-Filtering Enhancement Transformers for Efficient Visual Tracking

by Zhen Chen, Xingzhong Xiong, Fanqin Meng, Xianbing Xiao and Jun Liu

Electronics 2023, 12(18), 3905; https://doi.org/10.3390/electronics12183905 - 15 Sep 2023

Cited by 1 | Viewed by 878

Abstract

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to these robust trackers being unable to achieve satisfactory real-time performance in resource-constrained devices. In this work, we propose [...] Read more.

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to these robust trackers being unable to achieve satisfactory real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more accurately, this paper presents a scaling-invariant max-filtering operator. It uses local max-pooling to filter the suspected target portion in overlapping sliding windows for enhancement while suppressing the background. For a more compact target bounding-box, this paper presents an upsampling module based on Pixel-Shuffle to increase the fine-grained expression of target features. In addition, AnteaTrack can run in real time at 47 frames per second (FPS) on a CPU. We tested AnteaTrack on five datasets, and a large number of experiments showed that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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14 pages, 692 KiB  

Open AccessArticle

A Latent-Factor-Model-Based Approach for Traffic Data Imputation with Road Network Information

by Xing Su, Wenjie Sun, Chenting Song, Zhi Cai and Limin Guo

ISPRS Int. J. Geo-Inf. 2023, 12(9), 378; https://doi.org/10.3390/ijgi12090378 - 15 Sep 2023

Viewed by 1230

Abstract

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a [...] Read more.

With the rapid development of the economy, car ownership has grown rapidly, which causes many traffic problems. In recent years, intelligent transportation systems have been used to solve various traffic problems. To achieve effective and efficient traffic management, intelligent transportation systems need a large amount of complete traffic data. However, the current traffic data collection methods result in different forms of missing data. In the last twenty years, although many approaches have been proposed to impute missing data based on different mechanisms, these all have their limitations, which leads to low imputation accuracy, especially when the collected traffic data have a large amount of missing values. To this end, this paper proposes a latent-factor-model-based approach to impute the missing traffic data. In the proposed approach, the spatial information of the road network is first combined with the spatiotemporal matrix of the original traffic data. Then, the latent-factor-model-based algorithm is employed to impute the missing data in the combined matrix of the traffic data. Based on the real traffic data from METR-LA, we found that the imputation accuracy of the proposed approach was better than that of most of the current traffic-data-imputation approaches, especially when the original traffic data are limited. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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17 pages, 8254 KiB  

Open AccessArticle

MCBM-SLAM: An Improved Mask-Region-Convolutional Neural Network-Based Simultaneous Localization and Mapping System for Dynamic Environments

by Xiankun Wang and Xinguang Zhang

Electronics 2023, 12(17), 3596; https://doi.org/10.3390/electronics12173596 - 25 Aug 2023

Cited by 2 | Viewed by 943

Abstract

Current research on SLAM can be divided into two parts according to the research scenario: SLAM research in dynamic scenarios and SLAM research in static scenarios. Research is now relatively well established for static environments. However, in dynamic environments, the impact of moving [...] Read more.

Current research on SLAM can be divided into two parts according to the research scenario: SLAM research in dynamic scenarios and SLAM research in static scenarios. Research is now relatively well established for static environments. However, in dynamic environments, the impact of moving objects leads to inaccurate positioning accuracy and poor robustness of SLAM systems. To address the shortcomings of SLAM systems in dynamic environments, this paper develops a series of solutions to address these problems. First, an attention-based Mask R-CNN network is used to ensure the reliability of dynamic object extraction in dynamic environments. Dynamic feature points are then rejected based on the mask identified by the Mask R-CNN network, and a preliminary estimate of the camera pose is made. Secondly, in order to enhance the picture matching quality and efficiently reject the mismatched points, this paper proposes an image mismatching algorithm incorporating adaptive edge distance with grid motion statistics. Finally, static feature points on dynamic objects are re-added using motion constraints and chi-square tests, and the camera’s pose is re-estimated. The SLAM algorithm of this paper was run on the KITTI and TUM-RGBD datasets, respectively, and the results show that the SLAM algorithm of this paper outperforms the ORB-SLAM2 algorithm for sequences containing more dynamic objects in the KITTI dataset. On the TUM-RGBD dataset, the Dyna-SLAM algorithm increased localization accuracy by an average of 71.94% when compared to the ORB-SLAM2 method, while the SLAM algorithm in this study increased localization accuracy by an average of 78.18% when compared to the ORB-SLAM2 algorithm. When compared to the Dyna-SLAM technique, the SLAM algorithm in this work increased average positioning accuracy by 6.24%, proving that it is superior to Dyna-SLAM. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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16 pages, 1899 KiB  

Open AccessArticle

SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction

by Wei Li, Xi Zhan, Xin Liu, Lei Zhang, Yu Pan and Zhisong Pan

ISPRS Int. J. Geo-Inf. 2023, 12(8), 346; https://doi.org/10.3390/ijgi12080346 - 18 Aug 2023

Viewed by 1204

Abstract

Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks [...] Read more.

Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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19 pages, 4200 KiB  

Open AccessArticle

An Improved Adaptive Sparrow Search Algorithm for TDOA-Based Localization

by Jiaqi Dong, Zengzeng Lian, Jingcheng Xu and Zhe Yue

ISPRS Int. J. Geo-Inf. 2023, 12(8), 334; https://doi.org/10.3390/ijgi12080334 - 09 Aug 2023

Viewed by 1124

Abstract

The Ultra-Wideband (UWB) indoor positioning method is widely used in areas where no satellite signals are available. However, during the measurement process of UWB, the collected data contain random errors. To alleviate the effect of random errors on positioning accuracy, an improved adaptive [...] Read more.

The Ultra-Wideband (UWB) indoor positioning method is widely used in areas where no satellite signals are available. However, during the measurement process of UWB, the collected data contain random errors. To alleviate the effect of random errors on positioning accuracy, an improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this paper by introducing three strategies, namely, the two-step weighted least squares algorithm, adaptive adjustment of search boundary, and producer–scrounger quantity adaptive adjustment. The simulation and field test results indicate that the IASSA algorithm achieves significantly higher localization accuracy than previous methods. Meanwhile, the IASSA algorithm requires fewer iterations, which overcomes the problem of the long computation time of the swarm intelligence optimization algorithm. Therefore, the IASSA algorithm has advantages in indoor positioning accuracy and robustness performance. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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14 pages, 6698 KiB  

Open AccessArticle

Skill Fusion in Hybrid Robotic Framework for Visual Object Goal Navigation

by Aleksei Staroverov, Kirill Muravyev, Konstantin Yakovlev and Aleksandr I. Panov

Robotics 2023, 12(4), 104; https://doi.org/10.3390/robotics12040104 - 16 Jul 2023

Cited by 1 | Viewed by 2001

Abstract

In recent years, Embodied AI has become one of the main topics in robotics. For the agent to operate in human-centric environments, it needs the ability to explore previously unseen areas and to navigate to objects that humans want the agent to interact [...] Read more.

In recent years, Embodied AI has become one of the main topics in robotics. For the agent to operate in human-centric environments, it needs the ability to explore previously unseen areas and to navigate to objects that humans want the agent to interact with. This task, which can be formulated as ObjectGoal Navigation (ObjectNav), is the main focus of this work. To solve this challenging problem, we suggest a hybrid framework consisting of both not-learnable and learnable modules and a switcher between them—SkillFusion. The former are more accurate, while the latter are more robust to sensors’ noise. To mitigate the sim-to-real gap, which often arises with learnable methods, we suggest training them in such a way that they are less environment-dependent. As a result, our method showed top results in both the Habitat simulator and during the evaluations on a real robot. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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24 pages, 13149 KiB  

Open AccessArticle

An Improved Distributed Sampling PPO Algorithm Based on Beta Policy for Continuous Global Path Planning Scheme

by Qianhao Xiao, Li Jiang, Manman Wang and Xin Zhang

Sensors 2023, 23(13), 6101; https://doi.org/10.3390/s23136101 - 02 Jul 2023

Viewed by 1486

Abstract

Traditional path planning is mainly utilized for path planning in discrete action space, which results in incomplete ship navigation power propulsion strategies during the path search process. Moreover, reinforcement learning experiences low success rates due to its unbalanced sample collection and unreasonable design [...] Read more.

Traditional path planning is mainly utilized for path planning in discrete action space, which results in incomplete ship navigation power propulsion strategies during the path search process. Moreover, reinforcement learning experiences low success rates due to its unbalanced sample collection and unreasonable design of reward function. In this paper, an environment framework is designed, which is constructed using the Box2D physics engine and employs a reward function, with the distance between the agent and arrival point as the main, and the potential field superimposed by boundary control, obstacles, and arrival point as the supplement. We also employ the state-of-the-art PPO (Proximal Policy Optimization) algorithm as a baseline for global path planning to address the issue of incomplete ship navigation power propulsion strategy. Additionally, a Beta policy-based distributed sample collection PPO algorithm is proposed to overcome the problem of unbalanced sample collection in path planning by dividing sub-regions to achieve distributed sample collection. The experimental results show the following: (1) The distributed sample collection training policy exhibits stronger robustness in the PPO algorithm; (2) The introduced Beta policy for action sampling results in a higher path planning success rate and reward accumulation than the Gaussian policy at the same training time; (3) When planning a path of the same length, the proposed Beta policy-based distributed sample collection PPO algorithm generates a smoother path than traditional path planning algorithms, such as A*, IDA*, and Dijkstra. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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21 pages, 5035 KiB  

Open AccessArticle

A New Geomagnetic Vector Navigation Method Based on a Two-Stage Neural Network

by Zhuo Chen, Zhongyan Liu, Qi Zhang, Dixiang Chen, Mengchun Pan and Yujing Xu

Electronics 2023, 12(9), 1975; https://doi.org/10.3390/electronics12091975 - 24 Apr 2023

Cited by 3 | Viewed by 1107

Abstract

The traditional geomagnetic matching navigation method is based on the correlation criteria operations between measurement sequences and a geomagnetic map. However, when the gradient of the geomagnetic field is small, there are multiple similar data in the geomagnetic database to the measurement value, [...] Read more.

The traditional geomagnetic matching navigation method is based on the correlation criteria operations between measurement sequences and a geomagnetic map. However, when the gradient of the geomagnetic field is small, there are multiple similar data in the geomagnetic database to the measurement value, which means the correlation-based matching method fails. Based on the idea of pattern recognition, this paper constructs a two-stage neural network by cascading a probabilistic neural network and a non-fully connected neural network to, respectively, classify geomagnetic vectors and their feature information in two steps: “coarse screening” and “fine screening”. The effectiveness and accuracy of the geomagnetic vector navigation algorithm based on the two-stage neural network are verified through simulation and experiments. In simulation, it is verified that when the geomagnetic average gradient is 5 nT/km, the traditional geomagnetic matching method fails, while the positioning accuracy based on the proposed method is 40.17 m, and the matching success rate also reaches 98.13%. Further, in flight experiments, under an average gradient of 11 nT/km, the positioning error based on the proposed method is 39.01 m, and the matching success rate also reaches 99.42%. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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15 pages, 27174 KiB  

Open AccessArticle

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

by Jianan Bai, Danyang Qin, Ping Zheng and Lin Ma

ISPRS Int. J. Geo-Inf. 2023, 12(4), 169; https://doi.org/10.3390/ijgi12040169 - 14 Apr 2023

Viewed by 1258

Abstract

In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large [...] Read more.

In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance. Full article

(This article belongs to the Topic Artificial Intelligence in Navigation)

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