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Artificial Intelligence in Autonomous Navigation and Intelligent Unmanned Systems
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Artificial Intelligence in Autonomous Navigation and Intelligent Unmanned Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2588

Editor

Dr. Shengkai Zhang

E-Mail Website
Guest Editor
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: autonomous navigation; unmanned systems; multi-sensor fusion; SLAM; 3D reconstruction

Special Issue Information

Dear Colleagues,

Artificial intelligence has become a foundational driver of progress in autonomous navigation and intelligent unmanned systems. Recent advances in machine learning, perception, planning, and decision-making have enabled unmanned platforms, ranging from UAVs and UGVs to USVs and AUVs, to operate with increasing autonomy in complex and dynamic environments. Despite these advancements, substantial challenges persist. AI models must process and interpret multi-modal sensor data with high reliability, reason under uncertainty, adapt to domain shifts, and function within stringent real-time and safety-critical constraints. Achieving robustness, accuracy, and dependable real-time performance across diverse operational conditions remains a significant challenge for unmanned systems.

This Special Issue aims to bring together contributions that advance the theories, algorithms, and applications that relate to the use of AI for autonomous navigation and intelligent unmanned systems. Topics of interest include, but are not limited to, the following:

  • AI-driven perception, sensor fusion, and scene understanding;
  • Learning-based navigation, mapping, SLAM, and trajectory planning;
  • Deep reinforcement learning and decision-making under uncertainty;
  • Multi-agent coordination, swarm intelligence, and collaborative autonomy;
  • Simulation-to-real transfer and large-scale testing frameworks;
  • Safe, explainable, and trustworthy AI for autonomous systems;
  • Edge intelligence, onboard computing, and energy-aware AI models;
  • Cross-domain applications in aerial, ground, surface, and underwater unmanned systems.

Dr. Shengkai Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • autonomous navigation
  • unmanned systems
  • perception and planning
  • multi-agent cooperation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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25 pages, 2839 KB  
Article
UAV RF Signal Azimuth Estimation Using a UCA-8 and a Dual-Branch Circular-Regression Network
by Jingyang Wang, Jie Ma, Jiaxi Zhang, Zehan Li and Min Huang
Information 2026, 17(7), 705; https://doi.org/10.3390/info17070705 - 21 Jul 2026
Viewed by 304
Abstract
To address the problems that existing UAV RF signal azimuth estimation methods rely on idealized simulation data and lack accuracy and robustness in complex environments, a high-precision azimuth estimation method based on an improved ResNet, namely the Dual-Branch Circular-Regression Network, is proposed. Firstly, [...] Read more.
To address the problems that existing UAV RF signal azimuth estimation methods rely on idealized simulation data and lack accuracy and robustness in complex environments, a high-precision azimuth estimation method based on an improved ResNet, namely the Dual-Branch Circular-Regression Network, is proposed. Firstly, UCA-8 array data is generated from measured single-channel RF signals, and non-ideal factors such as channel mismatch and mutual coupling among array elements are incorporated to simulate the real RF receiving environment. Secondly, ResNet is improved from three aspects: input normalization, dynamic dual-branch (DDB) learning features and periodic angle regression. The input normalization strategy based on Per-Sample Complex Root Mean Square (PSCRMS) is adopted to improve the adaptability of the model to signal scale changes. The DDB structure is adopted to adaptively fuse I/Q spatiotemporal features with a spatial covariance statistical prior to enhance the spatial feature expression ability in complex scenes. A periodic angle regression method based on Unit Circular Vector Representation (UCVR) and the Huber Loss (GAH Loss) of geodesic angle distance is adopted to realize periodic angle continuous modeling and suppress abnormal angle errors. Finally, comparative and ablation experiments are conducted on the constructed UCA-8 dataset. The experimental results show that compared with the baseline ResNet, the Dual-Branch Circular-Regression Network achieves 85.8%, 95.3%, and 81.5% reductions in MAE, RMSE and P95, respectively, and maintains higher estimation accuracy and good robustness under low signal-to-noise ratio, hardware mismatch and co-frequency dual-source interference. Full article
(This article belongs to the Special Issue Artificial Intelligence in Autonomous Navigation and Intelligent Unmanned Systems)
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15 pages, 1269 KB  
Article
Deploying Efficient LLM Agents on Maritime Autonomous Surface Ships: Fine-Tuning, RAG, and Function Calling in a Mid-Size Model
by Yiling Ren, Mozi Chen, Junjie Weng, Shengkai Zhang, Xuedou Xiao and Kezhong Liu
Information 2026, 17(3), 284; https://doi.org/10.3390/info17030284 - 12 Mar 2026
Cited by 2 | Viewed by 1655
Abstract
Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference [...] Read more.
Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article
(This article belongs to the Special Issue Artificial Intelligence in Autonomous Navigation and Intelligent Unmanned Systems)
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